DZone Spotlight

Cloud deployments often fail because environment configurations are hardcoded into the build process. Here is a pattern to decouple your Build Artifacts from your Deployment Logic using GitHub Actions and a flexible JSON Configuration map. In the world of Kubernetes, we are used to the separation of concerns: Docker builds the image, and Helm/Kustomize handles the environment configuration. However, when working with serverless (Azure Functions) or PaaS (App Service), developers often fall into the trap of monolithic pipelines. They build a package that only works in DEV, and then rebuild it for PROD. This leads to "Artifact Drift," where the binary running in Production is not arguably the same binary that passed testing in Staging. A recent implementation by Fujitsu’s Global Gateway Division tackles this problem head-on. By moving away from manual Azure CLI deployments to a strict GitHub Actions workflow, they reduced release time by 75% (from 2 hours to 30 minutes) and eliminated manual configuration errors. Here is how to implement their "Environment Configuration Map" pattern to achieve safe, automated deployments across Azure environments. The Problem: The "Config Matrix" Hell In an Agile environment, especially during Proof-of-Concept (PoC) phases, assets are replaced frequently. You might have: Azure Functions (API logic)Cosmos DB (Data persistence)Virtual machines (Legacy processing) The challenge is that DEV, STAGING, and PROD have completely different Resource Group names, Subscription IDs, and tier settings (e.g., Use a cheaper SKU for Dev). If you hardcode these into your pipeline YAML, your pipeline becomes brittle. If you try to manage them manually, you risk human error. The Solution: The "Build-Once, Deploy-Many" Architecture The core philosophy of this pattern is simple: Build the binary once, version it, and then inject configuration only at the moment of deployment. The workflow consists of three distinct phases: Build phase: Compile code and generate a .zip artifact.Release phase: Store the artifact in GitHub Releases (immutable versioning).Deploy phase: A workflow reads a Config JSON, pulls the artifact, and pushes it to Azure. A sample workflow to understand release flow using GitHub Actions (manual versus automatic execution): 1. The Environment Configuration Map (config.json) Instead of scattering variables across GitHub Secrets or Azure App Settings, we centralize the environment definition in a JSON file committed to the repository. This acts as our "Source of Truth." File: .github/config/config.json JSON { "dev": { "subscriptionId": "sub-id-dev-001", "resourceGroup": "rg-app-dev", "resources": [ { "type": "function", "name": "func-api-dev", "slot": "staging" } ] }, "prod": { "subscriptionId": "sub-id-prod-999", "resourceGroup": "rg-app-prod", "resources": [ { "type": "function", "name": "func-api-prod", "slot": "production" }, { "type": "cosmosdb", "name": "cosmos-core-prod", "partitionKey": "/userId" } ] } } Key insight: Notice that dev might deploy to a staging slot, while prod deploys to production. This logic is abstracted away from the build script. 2. The Deployment "Operator" (Shell + Azure CLI) Instead of relying solely on rigid GitHub Actions plugins, this pattern uses a Shell script to parse the JSON and execute the logic. This makes the deployment portable — you can run it locally or in CI. File: scripts/deploy.sh Shell #!/bin/bash ENV_FLAVOR=$1 # e.g., "prod" TAG_VERSION=$2 # e.g., "v1.0.2" # 1. Read Config using jq CONFIG=$(cat .github/config/config.json | jq -r --arg env "$ENV_FLAVOR" '.[$env]') RG_NAME=$(echo $CONFIG | jq -r '.resourceGroup') SUB_ID=$(echo $CONFIG | jq -r '.subscriptionId') # 2. Login to Azure az account set --subscription $SUB_ID # 3. Download the Immutable Artifact from GitHub Releases wget https://github.com/my-org/my-repo/releases/download/$TAG_VERSION/build-artifact.zip # 4. Iterate through defined resources and deploy echo $CONFIG | jq -c '.resources[]' | while read resource; do TYPE=$(echo $resource | jq -r '.type') NAME=$(echo $resource | jq -r '.name') if [ "$TYPE" == "function" ]; then echo "Deploying $TAG_VERSION to Azure Function: $NAME..." az functionapp deployment source config-zip \ --resource-group $RG_NAME \ --name $NAME \ --src build-artifact.zip fi done 3. The GitHub Actions Workflow Finally, we tie it together with a Workflow that requires manual approval for Production environments. File: .github/workflows/deploy.yaml YAML name: Deploy to Azure on: release: types: [published] workflow_dispatch: inputs: environment: description: 'Target Environment' required: true default: 'dev' type: choice options: - dev - prod jobs: deploy: runs-on: ubuntu-latest environment: ${{ github.event.inputs.environment } # Uses GitHub Environments for approvals steps: - name: Checkout Code uses: actions/checkout@v3 - name: Azure Login uses: azure/login@v1 with: creds: ${{ secrets.AZURE_CREDENTIALS } - name: Run Deployment Operator run: | chmod +x scripts/deploy.sh ./scripts/deploy.sh ${{ github.event.inputs.environment } ${{ github.event.release.tag_name } The Architecture Visualized This approach separates the lifecycle of the code from the lifecycle of the environment. Why This Pattern Works (The Results) In the Fujitsu case study, adopting this pattern solved three critical issues: Instant rollbacks: Because artifacts are stored in GitHub Releases, "rolling back" is just re-running the deployment job with the previous Version Tag (e.g., v1.0.1 instead of v1.0.2). No rebuilding necessary.Resource isolation: The config.json allows granular control. You can define specific permissions or exclusion rules (e.g., ignore: ["iam"]) for Dev environments to prevent accidental permission overwrites.Eliminating "VM Lock": Previously, deployments required exclusive access to VMs, blocking other developers. By moving to Azure Functions and asynchronous Actions, the pipeline became non-blocking. Conclusion Tools like Terraform and Bicep are excellent for Infrastructure as Code (creating the resources). However, for Deployment as Code (moving the bits to the resources), a lightweight, configuration-driven approach using GitHub Actions and JSON maps provides the flexibility needed for high-velocity teams. By decoupling the "What" (the build artifact) from the "Where" (the environment config), you turn a fragile manual release process into a robust, repeatable engine.

If you’ve ever shipped a feature and thought, “Did we actually make things better?”, you’re not alone. A/B testing is supposed to be our scientific answer to that question, but running good experiments takes more than sprinkling some feature flags and plotting a graph. In practice, many teams learn experimentation the hard way. They launch tests with unclear hypotheses, biased assignments, or underpowered sample sizes, only to discover weeks later that their results are inconclusive or misleading. This means going back to the drawing board, restarting experiments, and losing valuable time, a hit to both product velocity and team morale. Even worse, decisions made on noisy or misinterpreted data can lead teams to ship the wrong features, double down on bad ideas, or miss opportunities that would have moved the needle. The result is a slower feedback loop, wasted engineering cycles, and products that evolve by gut feel rather than evidence. At scale, these problems compound. When you have millions of users, dozens of simultaneous tests, and machine learning models depending on clean signals, sloppy experimentation can quietly derail your roadmap. This is why A/B testing must be treated as an engineering discipline, one with rigor, guardrails, and repeatable processes that let teams move fast without breaking trust in their data. This post lays out a set of battle-tested best practices for running experiments that not only produce reliable results but also help teams ship faster, learn more, and build better products. 1. Align on Goals and Hypotheses Define the Purpose Identify the user or business problem you want to solve (e.g., improving onboarding conversion). Set a single, measurable primary metric (click-through rate, conversion rate, etc).If needed, add secondary metrics (engagement, error rate, revenue impact) to catch side effects or ecosystem impact Formulate a Hypothesis Express it in a testable format: “We believe that redesigning the onboarding screen will increase click-through rate compared to the current experience.” This keeps the team aligned on why the experiment exists. 2. Collaborate Early Across Teams A/B tests succeed when product managers, engineers, data scientists, and designers work together: PM/Design defines user impact and success metrics.Engineering ensures feature flags, rollout control, and logging are reliable.Data/Analytics validate statistical power, experiment length, and segmentation.QA/Support plan for potential user confusion or errors. 3. Design the Experiment Carefully Randomization and Segmentation Use random assignment to avoid bias in the experiment. Ensure mutually exclusive cohorts if running multiple tests simultaneously.Consider stratified sampling if different user segments behave differently.Exposure logging to detect biases in the experiment setup is shown in the code snippet below Sample Size and Duration Calculate minimum sample size (power analysis) before launch to avoid underpowered tests. Run the experiment long enough to capture normal user behavior (usually 1–2 business cycles). Guardrails and Safety Checks Define guardrail metrics (e.g., crash rate, latency, unsubscribe rate) to prevent harm. Have a kill switch or staged rollout (e.g., 1%, 10%, 50%, 100%) to react quickly if issues arise. 4. Implement With Robust Engineering Practices Use feature flags/toggles for easy control.Log all relevant events with timestamps, experiment ID, and user/session identifiers.Ensure data quality: no missing or duplicated events.Run canary tests internally before full rollout to catch issues early. Here is a sampler experiment handler code that takes care of key aspects of user assignment to experiment branches, exposure logging, and conversion logging. Exposure logging records when a user sees an impression of a variant, while conversion logging records when the user completes a desired action (e.g., a click) after exposure. Python class ABTestExperiment: """Sample A/B Test experiment handler with selected logging""" def __init__(self, config: ExperimentConfig): self.config = config self.logger = logging.getLogger(f'ab_test.{config.experiment_id}') # Log experiment initialization self.logger.info(f"Initialized experiment: {config.name}") self.logger.info(f"Traffic allocation: {config.traffic_allocation}") def assign_user_to_branch(self, user_id: str) -> ExperimentBranch: """Assign user to experiment branch using consistent hashing""" if not self.config.is_active: self.logger.warning(f"Experiment {self.config.experiment_id} is inactive") return ExperimentBranch.CONTROL # Create deterministic hash for consistent assignment hash_input = f"{self.config.experiment_id}:{user_id}" hash_value = hashlib.md5(hash_input.encode()).hexdigest() hash_number = int(hash_value[:8], 16) / (16**8) # Convert to 0-1 range # Assign based on traffic allocation cumulative_allocation = 0.0 assigned_branch = ExperimentBranch.CONTROL for branch, allocation in self.config.traffic_allocation.items(): cumulative_allocation += allocation if hash_number <= cumulative_allocation: assigned_branch = branch break # Log assignment self.log_assignment(user_id, assigned_branch, hash_number) return assigned_branch def log_assignment(self, user_id: str, branch: ExperimentBranch, hash_value: float): """Log user assignment to experiment branch""" assignment_data = { 'event_type': 'user_assignment', 'experiment_id': self.config.experiment_id, 'experiment_name': self.config.name, 'user_id': user_id, 'assigned_branch': branch.value, 'hash_value': hash_value, 'timestamp': datetime.utcnow().isoformat(), } self.logger.info(f"User assignment: {json.dumps(assignment_data)}") def log_exposure(self, user_id: str, branch: ExperimentBranch, context: Dict[str, Any] = None): """Log when user is exposed to experiment. This log is very critical to detect bias in experiments and make sure same user is not exposed to multiple variants""" exposure_data = { 'event_type': 'experiment_exposure', 'experiment_id': self.config.experiment_id, 'experiment_name': self.config.name, 'user_id': user_id, 'branch': branch.value, 'timestamp': datetime.utcnow().isoformat(), 'context': context or {} } self.logger.info(f"Experiment exposure: {json.dumps(exposure_data)}") def log_conversion(self, user_id: str, branch: ExperimentBranch, conversion_type: str, value: float = None, metadata: Dict[str, Any] = None): """Log conversion event for analysis. This log tracks the key metrics used to determine experiment result. Eg : click through rate, click through sale, etc""" conversion_data = { 'event_type': 'conversion', 'experiment_id': self.config.experiment_id, 'experiment_name': self.config.name, 'user_id': user_id, 'branch': branch.value, 'conversion_type': conversion_type, 'value': value, 'timestamp': datetime.utcnow().isoformat(), 'metadata': metadata or {} } self.logger.info(f"Conversion: {json.dumps(conversion_data)}") 5. Monitor in Real Time Track key metrics as soon as data comes in.Watch for anomalies or negative effects that exceed thresholds.Pause or roll back if user experience or system health is at risk. 6. Analyze With Statistical Rigor Use appropriate statistical tests (t-test, chi-squared, Bayesian inference).Correct for multiple comparisons if testing multiple variants.Look beyond p-values, consider practical significance (effect size, ROI).Segment results (e.g., by platform, geography, user cohort) to understand nuances. 7. Communicate and Document Results Share experiment results in a consistent format (objective, design, metrics, results, interpretation).Include charts and confidence intervals for clarity.Document learnings in a centralized experiment repository so future teams avoid duplicating work. 8. Iterate and Build a Culture of Experimentation Use findings to inform product decisions (ship, iterate, or pivot).Encourage teams to ask “why”, not just whether a metric moved.Continuously improve your experimentation platform and processes. In the era of data-driven product development and machine learning — powered features, experimentation isn’t just a tool; it’s the feedback loop that powers innovation. Teams that master it move faster, learn more, and build better products than those that rely on guesswork. So the next time you spin up an experiment, ask yourself: Are we treating this as a side project, or as the core engine that drives our product forward?

As software development practices have advanced over time, so too have the methodologies for managing code and changes. The autonomous pipeline, as it relates to continuous integration or continuous delivery (CI/CD) technology, embodies the next step in sophistication, where the pipeline can function almost entirely independently with no or very little human interaction. In an autonomous pipeline, the entire code integration and delivery process is managed automatically, producing fewer opportunities for human mistakes and allowing for faster release cycles. As organizations continue to seek more reliable and efficient software delivery practices, the desire for autonomous capabilities has become a trend to further reduce the need for human involvement in the CI/CD workflow process. This represents a fundamental shift in CI/CD practices that allows for self-governed decision-making and execution to be performed entirely independently of human input. Understanding Autonomous Pipelines Autonomous pipelines centralize a layered orchestration of automated decisioning and execution processes implemented within CI/CD workflows. Without user interaction at any stage, autonomous pipelines trigger and control all processes of source code integration and assembly from committed changes, through automated tests and validation, artifacts indexing and distribution, down to deployment into production target environments. Based on knowledge encoded in policies, rules, intelligent triggers, and feedback loops, the system perceives changes in the source code under integration, identifies relevant test suites, and dynamically calculates deployment strategies for each target environment. Furthermore, using configuration scripts and built-in logic, autonomous pipelines dynamically adjust their behavior based on test results, compliance verifications, or the state of their infrastructure, ensuring outstanding reproducibility and reliability. In this manner, autonomous pipelines perform essential groundwork in software delivery, minimizing lead time from idea to production, and supporting enterprises in their endeavor to deliver and meet the growing demands of software continuous delivery at scale. Moreover, the autonomous pipelines foster multiple benefits that facilitate the efficient functioning of CI/CD activities. The automated orchestration reduces the time consumed by repetitive processes and improves overall efficiency during the software delivery process. Besides, by applying automated validations and reinforcing several rules through the pipeline, the number of deployment errors associated with manual errors also decreased significantly. The entire build, test, and deployment process takes less time and allows teams to quickly respond to changes in project requirements, thus leading to quicker releases. As a result, the organizations that apply the autonomous pipelines become capable of ensuring stable software quality, faster time-to-market, and redirecting human efforts from mundane operational tasks to more sophisticated engineering problems. Conversely, if full autonomy of pipelines is sought, there are a variety of obstacles and constraints to be faced to ensure homogeneous results. The main hurdle constitutes the technicalities of programming pipelines that work with multiple codebases, diverse deployment environments, and complex dependency management situations. Error recovery and dynamic decision-making procedures demand sophisticated logic and extended safety measures, which contribute to increased challenges in the setup and maintenance of the system. Besides, the absence of human control could lead to risks caused by accidental deployments, security breaches, and weak compliance enforcement procedures if rules or anomaly detection methods neglect edge cases. All these difficulties will demand continuous improvement of autonomous systems to weigh the outcome of an automated process against the unavoidable requirement for control, reliability, and safety in the CI/CD pipeline. Future Possibilities and Implications The future of CI/CD pipelines should be significantly impacted by the constant evolution of technology in key areas, such as artificial intelligence, machine learning, and intelligent process automation. Relying on breakthroughs in predictive analytics and autonomous anomaly detection, CI/CD pipelines could prevent releases with defects and prove more reliable by predicting failures before they happen. Additionally, future pipelines could employ self-healing capabilities to automatically recover from unexpected errors, learn from previous situations, and improve their operational behavior continuously. With the widening adoption of cloud-native designs and microservice architectures, future pipelines might evolve to operate in an increasingly dynamic and distributed environment with low levels of manual configuration. Overall, these trends point to the possible evolution of CI/CD systems into autonomous actors of technical change, rather than only executors of the prescribed process, and herald the coming of a new era of fully autonomous software delivery. Implementing the Future: A Conceptual Framework and Code Building an autonomous pipeline requires integrating a layer of intelligence on top of existing CI/CD tools. This can be achieved by using AI-powered platforms or by building custom scripts that leverage machine learning models. The core idea is to create a feedback loop where data from the pipeline's execution informs future decisions. Here's a conceptual example using Python and a hypothetical "autonomous agent" that learns from pipeline data. This agent would monitor build success rates and test performance to make informed decisions. A simplified Python class representing an autonomous agent: Python import pandas as pd from sklearn.ensemble import RandomForestClassifier class AutonomousPipelineAgent: def __init__(self, historical_data_path): self.data = pd.read_csv(historical_data_path) self.model = RandomForestClassifier() self.train_model() def train_model(self): # Feature engineering: create a 'risk_score' based on various factors # For simplicity, we'll use a few columns from our historical data features = self.data[['build_time', 'num_tests', 'security_scan_results']] labels = self.data['is_successful'] self.model.fit(features, labels) def predict_success(self, current_build_metrics): # Predict if the current pipeline run will be successful return self.model.predict(current_build_metrics) def optimize_stage(self, stage_name, stage_metrics): # This is where the magic happens. The agent can suggest or # automatically apply optimizations based on its predictions. # For example, if it predicts a failure, it might re-order tests. prediction = self.predict_success(stage_metrics) if prediction == 0: # 0 indicates a predicted failure print(f"Prediction: Stage '{stage_name}' is at high risk of failure. Initiating self-healing...") self.initiate_self_healing(stage_name) else: print(f"Prediction: Stage '{stage_name}' is likely to succeed. Proceeding normally.") def initiate_self_healing(self, stage_name): # Example self-healing action: # In a real-world scenario, this would trigger a specific action # like re-running a failed test with different parameters, or # automatically reverting a recent code change. if stage_name == 'build': print("Running static code analysis and linting to find potential issues.") elif stage_name == 'test': print("Re-prioritizing and re-running the most critical tests.") elif stage_name == 'deploy': print("Initiating automated rollback to the last stable version.") In a real-world implementation, this Python agent would be a microservice in the pipeline, communicating with tools like Jenkins, GitLab CI/CD, or GitHub Actions. It would receive data in real time, run its predictions, and use APIs to control the pipeline's flow. Conclusion To conclude, we argue that self-driving pipelines represent a paradigm shift in the CI/CD space with respect to software delivery processes that are now capable of being fully automated, adaptive, and resilient. The level of technical sophistication that these systems have reached empowers companies to further increase release velocity and maintain quality assurance while reducing the risks associated with human involvement. Although there are challenges to be addressed in the transition to autonomous pipelines (i.e., volatility, configuration complexity, security), the evolution of the enabling technologies offers promising paths to circumvent them. Ultimately, the growth of intelligent automation and self-healing systems will transform the nature of the relationship between engineering teams and deployment infrastructure, making it more agile. As self-driving pipelines become more widespread, their impact will continue to transform software industry practices toward a more reliable, efficient, and fully automated CI/CD environment.

Why does choosing the right JavaScript Data Grid still matter in 2026? Data grids remain a cornerstone of web applications: dashboards, admin panels, CRMs, analytics, and enterprise systems all rely on them. The choice of the right grid still defines performance, customization flexibility, accessibility, and cost. To find which grid fits your needs, I reviewed eight top JavaScript data grids and compared them by performance, customization, accessibility, cost, integration, and devX. To make the comparison more practical and memorable, I’ve added a short “Did you know?” fact to each grid. These highlight unique traits, quirks, or lesser-known details that help readers quickly distinguish one grid from another. 1. Webix Grid Webix Grid is arguably one of the most underrated JavaScript components for enterprise-scale applications. Originally part of the broader Webix UI library, but is now available as a standalone datagrid. It’s widely used for dashboards, admin panels, and complex data-driven applications. Did you know? Webix Grid has been around since 2013 and still ranks among the fastest grids on the market — rendering 100,000 rows in just ~17 ms. It even comes with a visual Skin Builder tool for instant theme customization. Best for: enterprise dashboards, complex data-driven appsKey features: frozen rows, Excel-like filters, grouping & colspans, Skin Builder for themingPerformance: the benchmark leader (100,000 rows init render time – 17 ms)Customization: flexible APIs, custom editors/templates, Skin BuilderAccessibility: WAI-ARIA, keyboard navigation, high-contrast skinCost: Free (GPL) / Pro from $749DevX: solid documentation, moderate learning curve, official integrations (React/Angular/Vue) 2. Tabulator Tabulator is a free, open-source JavaScript data grid component commonly used for dashboards, admin panels, and general-purpose data tables. Its highly modular design allows developers to easily add custom formatters, editors, and themes. Did you know? Tabulator is completely free under the MIT license with no hidden Enterprise edition. Its community has built a wide ecosystem of plugins, and it even supports Excel-style copy-paste out of the box. Best for: startups, open-source projects, cost-sensitive solutionsKey features: modular design, custom formatters/editors, MIT licensePerformance: solid with medium datasets; large column sets slow renderingCustomization: very high, through API and pluginsAccessibility: ARIA roles, but some WCAG gapsCost: completely free (MIT)DevX: easy to start, community wrappers for React/Angular/Vue 3. AG Grid AG Grid is a high-performance JavaScript datagrid, widely adopted in financial dashboards, analytics platforms, and large-scale enterprise applications. Known for its extensive capabilities that can genuinely blow your mind. From advanced row models to complex filtering, pivoting, and live updates, AG Grid is one of the most powerful tools in its category. Did you know? AG Grid is trusted by financial giants like Bloomberg and Goldman Sachs. But beware: if your Enterprise license expires, the grid will display watermarks inside your app — a rare but strict enforcement tactic. Best for: financial dashboards, high-load analytics platformsKey features: pivoting, server-side row model, integrated charts, Excel-like operationsPerformance: high performanceCustomization: custom cells, editors, renderers, Theme BuilderAccessibility: full WCAG complianceCost: Free (Community) / Enterprise from $999 (+ deployment fees)DevX: extremely flexible but steep learning curve; strong community 4. DHTMLX Grid DHTMLX Grid is a lightweight, efficient JavaScript grid ideal for building responsive web applications and admin interfaces. It is commonly used in dashboards, CRM systems, and other data-heavy web apps. Did you know? DHTMLX Grid is one of the few that natively support TreeGrid structures and spreadsheet-like editing. It’s especially popular in CRM systems and lightweight admin tools. Best for: lightweight admin apps, CRM, responsive web appsKey features: split mode (frozen columns), Excel/PDF export, tree gridPerformance: efficient with virtual scrollingCustomization: configurable templates, editors, stylingAccessibility: basic (keyboard only)Cost: Free (GPL) / Pro from $749DevX: modular architecture, lightweight by design 5. Bryntum Grid Bryntum Grid is a specialized JavaScript grid most often used in project management and scheduling applications, including Gantt charts, resource planners, and complex enterprise scheduling tools. Its primary audience is teams building applications where data visualization tightly integrates with task and resource planning. Did you know? Bryntum Grid shares its core with the company’s Gantt chart, making it uniquely powerful for scheduling and project management use cases. It even ships with built-in resource planners that few competitors can match. Best for: project management, Gantt, resource planningKey features: complex grouping, Excel-like drag-fill, Excel/PDF export, Gantt integrationPerformance: Performs a little bit lower than peersCustomization: 5 themes, API-based extensionsAccessibility: ARIA, keyboard supportCost: Commercial from $850DevX: niche-focused with powerful scheduling API 6. Kendo UI Grid Kendo UI Grid is a JavaScript grid component from Telerik, designed for enterprise web applications, dashboards, and internal tools. It provides advanced features such as filtering, grouping, paging, and theming. Did you know? Kendo UI Grid has been around since 2009 and integrates tightly with Telerik’s reporting and charting ecosystem. It’s famous for being an all-in-one enterprise solution — powerful, but with a price tag that stings. Best for: enterprise web apps, corporate dashboardsKey features: Excel/PDF export, hierarchical grids, advanced filteringPerformance: Excellent performanceCustomization: rich built-in functionality + themingAccessibility: WCAG + Section 508Cost: part of Telerik Kendo UI bundle (from $899)DevX: excellent documentation, mature ecosystem, official support for Angular/React/Vue 7. Handsontable Handsontable is a JavaScript spreadsheet-like grid component designed to emulate Excel functionality within web applications. It is widely used in financial applications, data-entry forms, and any scenario where spreadsheet interactions are critical. Did you know? Handsontable is the closest thing to Excel inside a browser, complete with its own formula engine. It’s widely used in data-heavy apps ranging from financial tools to scientific research. Best for: Excel-like UI, finance, data-entry workflowsKey features: formulas, merge cells, conditional formatting, rich cell typesPerformance: optimized for medium datasetsCustomization: advanced editing and formulasAccessibility: improving but still behind leadersCost: Commercial from $899DevX: intuitive, low entry barrier 8. DevExtreme Data Grid DevExtreme Data Grid is a JavaScript grid component designed for enterprise web applications, dashboards, and analytics platforms. It excels at handling large datasets with virtual scrolling and offers advanced features such as grouping, filtering, and templating. Did you know? DevExtreme is one of the only grids offering a built-in PivotGrid component, bringing OLAP-style summaries to the web. It also maintains parity across React, Angular, Vue, and even jQuery. Best for: enterprise dashboards, analytics at massive scaleKey features: pivot grids, Excel/PDF export, master-detail, advanced templatingPerformance: near-instant scrolling with millions of rows (virtual rendering)Customization: advanced templating, grouping, custom editors, themingAccessibility: strong ARIA support, improved screen reader compatibilityCost: Commercial ~ $899/yearDevX: official React/Angular/Vue components, well-documented Comparison Table gridperformancecustomizationaccessibilityfreecost (PRO)best for Webix Highest High Good ✔️ $749 Enterprise apps Tabulator Medium (column-heavy slows down) High Medium ✔️ Free Startups, OSS AG Grid High Very high Excellent ✔️ $999+ Financial dashboards DHTMLX Fast (virtual scroll)MediumBasic ✔️$749 CRM, admin apps Bryntum Medium High (niche) Basic ❌ $850 Scheduling, Gantt Kendo UI High High Excellent ❌ $899 Enterprise apps Handsontable Medium High Medium ❌ $899 Excel-like apps DevExtreme Near-instant (virtual rendering) High Excellent ❌ ~$899/year Large datasets Recommendations for Choosing a Data Grid Need open-source and free → TabulatorMaximum performance at scale → WebixFinancial dashboards/analytics → AG GridExcel-like UI → HandsontableProject planning/Gantt → BryntumEnterprise with strict WCAG/508 → Kendo or DevExtreme Final Thoughts The right grid depends on your priority — performance, flexibility, licensing, or accessibility. The landscape is mature: from free Tabulator to premium enterprise solutions like AG Grid Enterprise and Webix.

Penetration testing — “pentesting” — still surprises teams. Some treat it as a checkbox before launch; others expect it to magically find every vulnerability. The truth sits in the middle: a well-planned penetration testing strategy turns a point-in-time assessment into a practical tool that reduces business risk, informs engineering priorities, and improves resilience over time. This article walks through how to build a penetration testing strategy that’s repeatable, cost-effective, and aligned with your business goals. It’s written for security leaders, engineering managers, and CISOs who want tests that do more than produce reports — they change behavior and reduce real risk. Why You Need a Strategy (Not Just a Test) A single pentest is useful, but insufficient on its own. Without a strategy, you get: One-off findings that reappear months later.Misaligned scope (tests that miss critical assets).Poor remediation follow-through (fixes that aren’t verified).Audit theater — reports that satisfy compliance but don’t block attackers. A strategy ensures tests are targeted, recurring, and integrated with your development and risk processes so the effort drives measurable security improvements. Pillars of a Good Penetration Testing Strategy 1. Align Tests to Business Risk Begin by asking which assets would cause the most damage if compromised: customer data, payment systems, internal admin consoles, or identity providers. Prioritize those assets for testing. Practical approach: Map assets by business impact (high/medium/low).Tie scope definitions to revenue, legal exposure, or customer trust.Schedule higher-frequency tests for high-impact systems. 2. Use a Layered Approach: Breadth + Depth Combine different test types to cover surface-level exposure and deep logic flaws: External pentest (black box) – attacker from the internet with no credentials. Great for public-facing apps, APIs, and cloud entry points.Internal pentest (gray box) – simulated attacker with some internal access. Good for lateral movement and segmentation checks.Web app/API pentest – focused manual testing on business logic, auth, injection flaws, BOLA/IDOR.Infrastructure/Network pentest – firewall, open ports, misconfigurations, and host hardening.Cloud pentest – misconfigurations, IAM, storage exposures in AWS/Azure/GCP.Red team exercise – broader, longer campaign simulating an advanced adversary (phishing, social engineering, persistence). Each has a place; use them according to maturity, compliance needs, and risk appetite. 3. Define Scope Clearly — and Keep It Realistic Vague scopes (“test everything”) lead to budget overruns or missed targets. A good scope answers: Exactly which domains, APIs, cloud accounts, and IP ranges are in scope.What is not included (internal networks, physical security, social engineering) unless explicitly agreed.Rules of engagement, business hours constraints, and acceptable impact boundaries. Clarity prevents surprises and enables fixed-price, predictable engagements. 4. Choose Testing Cadence Based on Risk and Change Velocity There’s no single “right” frequency. Consider: High-risk, fast-changing systems (e.g., public APIs) → quarterly or continuous targeted tests.Standard web apps → biannual or annual tests.Large distributed systems/enterprises → rolling schedule so different teams are tested throughout the year. When scope or architecture changes (major release, cloud migration), schedule a focused re-test. 5. Emphasize Manual Testing and Exploit Chaining Automated scanners find low-hanging fruit but create false positives and miss business logic issues. Human-led testing should focus on: Manual verification and exploitation.Chaining small flaws into a realistic attack path (e.g., auth bug → privilege escalation → data exfiltration).Proof-of-concept exploits, screenshots, and playback steps that engineers can reproduce. Proof beats a long list of unverified vulnerabilities. 6. Require Developer-Friendly Deliverables Good reports translate into action. Each deliverable should include: Executive summary with business impact and risk prioritization.Reproducible technical findings: clear steps, payloads, code snippets, and screenshots.Root-cause analysis and prioritized remediation steps (short-term fix + long-term prevention).Mapping to controls (SOC 2, PCI, ISO) when relevant.A retest or validation step is included (free or time-bound). Actionable reports speed remediation and reduce friction between security and engineering teams. 7. Include Remediation and Retesting in the Process Testing without verification is incomplete. Your strategy should include a clear remediation window and retesting policy: Critical fixes retested within 1–2 weeks.Medium findings validated within the next test cycle.Maintain a “fix-to-closure” workflow tracked in your ticketing system. This closes the loop and prevents “find-fix-forget” cycles. 8. Measure Outcomes, Not Just Findings Track metrics that show progress and risk reduction: Mean time to remediate (MTTR) for critical issues.Number of exploitable findings over time.Percentage of tests with chained exploits vs. standalone CVEs.Time between discovery and retest/closure. These KPIs tell you whether testing is making the environment safer. 9. Integrate Testing With SDLC and CI/CD Shift left testing where possible: Include security gates or automated scans in CI for unit-level issues.Use scheduled pentests as part of pre-release checklists.Feed findings back into secure development training and patterns. When developers see pentest outputs as learning (not blame), security improves faster. 10. Consider Third-Party and Supply Chain Exposures Often, risk comes from vendors and libraries. Strategy should include: Testing integrations with third-party services.Reviewing SBOMs for vulnerable components.Holding vendors to contractual security standards and proof of testing. Supply chain blind spots are common and high impact. Practical Rollout: A Simple 90-Day Plan If you’re starting or refreshing strategy, a 90-day program can show quick wins: Days 0–14: Asset mapping and risk prioritization.Days 15–45: Run focused external pentest on top 1–2 critical assets.Days 46–75: Remediation sprint, developer handoff, and retest of critical issues.Days 76–90: Expand scope to APIs or cloud, formalize cadence, and define KPIs. This phased approach delivers value quickly while building momentum. Budgeting and Vendor Selection Tips Prefer human-led testers with strong evidence practices over purely automated vendors. Look for fixed-scope pricing for standard tests; use quotes for complex environments. Ask for tester credentials (OSCP, OSCE), red-team case studies, and non-disclosure practices. Verify retest policy and whether remediation support is included. A clear SOW (statement of work) avoids surprises and aligns expectations.

Data modeling has transformed beyond recognition. We have moved from a simple entity-relationship diagram to sophisticated cloud architectures, and honestly, it is not just about shinier technology — it is a complete rethink of how we handle data. I learned the basics of ERwin back when it ruled the enterprise world. All industries used it, including banks, hospitals, and government agencies. The tool did wonders to standardize and document, which made CFOs and compliance officers happy [1]. The vast database designs across massive organizations could be counted on. Though I will admit, the licensing costs were brutal — especially for smaller teams who just needed basic modeling capabilities. Those days feel like the past now. Tools like ERwin were good at what they were doing, but still very slow. It worked for firms because there were no other solutions present for these problems. Companies were ok spending time just to make a small change, and we are talking about 8 to 12 weeks for a moderately complex project. This includes having the requirements firsthand, doing the logical data model design, physical design, generating the scripts (used to take hours), and then having everything documented. Here, the workflow to make a simple change, let’s say adding a new column, would go something like two to three days just to make model updates, then you generate the script along with the documentation related to it. I used to spend an entire day merging a model with another, doing a manual diff, and, on top of that, the tool would sometimes crash while running on your laptop, making you redo all the work. Cloud platforms turned the world upside down. Let’s take an example of AWS Glue Data Catalog, where it can handle diverse data types seamlessly, opting for a less formal and often expedited process. The changes that used to take weeks of planning and execution can now be done in hours. I see a huge gain in productivity, which is not just incremental but is revolutionizing the entire process. There may be a learning curve involved with these cloud tools if you’re coming from the old ERwin world. Modern data modeling extends far beyond traditional databases. Now, companies are working on ML features, stores, and IoT data pipelines, and investing in more real-time analytics engines. With this type of data stream, legacy tool simply provides the required flexibility that comes with data lakes and mesh architectures. You have fundamental limitations with the old tools to handle semi-structured data and streaming requirements. Maybe we have swung too far from the traditional discipline that ERwin enforced. Version control represents perhaps the starkest contrast. As I mentioned before, manually doing a model diff was painstaking, and I have seen my fellow colleagues nearly cry while trying to reconcile the conflicting changes. But now, we have native integration with GIT repositories and workflows as an example. Multiple folks can now collaborate at the same time without stepping on each other’s toes. The boon of automatic conflict resolution, along with tracking the changes, comes as a standard that was a luxury dream in the ERwin era. From a purely financial perspective, the verdict is decisive: Traditional Method Timeline (Best-Case Scenario) Building and updating models: 4–8 hrs Creating deployment scripts: 2–4 hrs Approvals: 24–48 hrs Strategic planning with resources: 8 hrs System deployment in production: 4–8 hrs Cloud approach: With automated testing doing all the heavy lifting, all it would take is an hour or so. It may vary depending on the complexity of the changes, but the time saving and speed are undeniable. Remember, data governance, which used to be an afterthought, is becoming mission-critical. PII classifications, security controls, and compliance methodologies are baked into the platform itself. Classifications like CCPA and GDPR are all automated through built-in data lineage tracking, having the right access controls and audit mechanisms. You don’t have to use separate tools for this and manual processes if you were using them before. Though let's be honest, people accustomed to older compliance methods still feel more confident verifying it with printed reports. With the advancements towards agentic AI, the trajectory is surely leaning in that direction now. By that I mean, we see new optimized emerging platforms that analyze patterns and can themselves suggest what should be the optimal schema, which sometimes may be overlooked by the engineers. How do they do it? By learning from data access patterns, we are able to propose data structures that are optimal for both maintainability and performance. If you ask me, I am OK using agentic AI to propose the changes, but I would still verify on my own. The suggestions are getting surprisingly good and fast. In my opinion, right now, organizations are at a critical swing point. To measure the success metrics, we need to consider the full picture. It should not be just the technical performance, but we also need to make sure how fast the teams can adopt and deliver, learn and innovate, and respond to changing business needs rapidly. Success demands both cultural and technological transformation. Data modeling has evolved from a specialized, siloed function to an integral component of broader data strategy. Modern teams blend data architects, engineers, analysts, and business stakeholders in collaborative workflows. The old model of throwing requirements over the wall to a modeling team is obsolete. Good riddance, honestly. Training becomes paramount. Teams need hands-on experience with cloud platforms and must reconceptualize modeling within end-to-end data pipelines rather than isolated database design exercises. The hardest part isn't learning the new tools — it's unlearning the old habits. Why talk about habits? The landscape is changing fast. We now have Graph databases, for example, that can handle complex relationships, which we can’t, or let’s say most traditional databases struggle with. You have Temporal data models that work with time-series data in a natural way. With growing machine learning workloads, we can now have data models that specialize in creating models apt for training and driving inference from them. Sometimes, it feels like I am hearing a new "revolutionary" database technology every month, but again, the core principles remain surprisingly consistent. The scale of things now is amplifying every challenge with it. As the data volume grows in this digital age, our modeling decisions that were more academic in the past are now part of performance and cost implications. I think efficiency is no longer optional. I think gone are the days of over-engineering (my experience), where a simple model change cost us a lot of budget because we didn't consider actual query behavior. With this architectural evolution, the transition currently represents more than just a tool migration. It is not just about storing the data, and the organizations that are embracing cloud-native products are able to position themselves to extract genuine value from data as well. The companies that adapt will thrive. Companies that are sticking to old legacy systems will find themselves behind the curve while the competitors move at cloud speed. The writing is on the wall. Traditional tools are of the past and have served their purpose, but I think the future of any company belongs to cloud native platforms and solutions that can match the pace and scale of modern business. Everyone is trying to reinvent companies like ERwin for the cloud era as well, though I am not sure how quickly it can be done. References Quest Software. (2023). ERwin Data Modeler: Enterprise data modeling solutions.Amazon Web Services. (2024). AWS Glue: Simplify data integration and ETL processes.AWS Glue: Simplify data integration and ETL processes

PDFs remain the most widely used format for distributing structured reports — financial statements, regulatory filings, research documents, fund fact sheets, and more. Yet despite their structured appearance, PDFs are not machine-readable. Extracting tables reliably is famously error-prone and often requires hours of manual cleanup. This is especially true in finance and enterprise environments where analysts rely on Excel for modeling and reporting. To address this challenge, I built an open-source Python package: pdf-tables-to-excel A tool designed to detect, extract, and export clean, analysis-ready Excel tables from any PDF — powered by Docling’s state-of-the-art document parsing. Install it in seconds: Python pip install pdf-tables-to-excel This article walks through the motivation, engineering decisions, architecture, and practical workflows behind the tool. Why I Built This (The Real Motivation) I’ve spent years working with technical users in financial services — quant teams, credit analysts, portfolio researchers, operations, and data engineering groups. Across all of them, I repeatedly observed one universal pain point: Extracting tables from PDFs feels like manual data entry. Even when using open-source libraries like Camelot, Tabula, pdfplumber, or PyPDF2, most tools stop at returning a Pandas DataFrame. Then analysts still need to: Fix column alignmentsConvert text percentages to real numeric Excel percentagesHandle negative values shown as (1.3%)Unmerge headersManually build and style Excel sheetsSplit multi-table PDFs into separate DataFrames and sheetsApply proper bordersAuto-fit column widthsPreserve header formattingHandle OCR for scanned PDFs Every team reinvented the same 200 lines of conversion code. Some tools extract data, but none produce an Excel that a human can immediately use, without additional cleanup. Additionally: Many finance documents contain complex layouts.Merged headers and percentage columns fail silently.Currency formats lose precision.Most libraries misdetect table boundaries.Scanned PDFs require unreliable OCR workflows. This motivated me to build a unified tool, where table extraction + Excel formatting are bundled into a single operation. Why Docling? The Accuracy Advantage Out of all PDF parsing libraries available today, Docling stands out in terms of: Layout-Awareness Docling understands the document structure, not just the text. Table Geometry Detection It identifies rows, columns, spans, merges, and alignment. OCR Integration Scanned PDFs are handled via RapidOCR or EasyOCR. High Accuracy on Complex Documents This includes financial tables with: multi-line headersmerged spansnested table regionsfootnotes and annotationsalternating row patterns Robust Against Inconsistent Table Borders Docling detects tables even when: Borders are missingCells are visually misalignedFonts varyWhitespace is inconsistent This means the extracted DataFrames are significantly cleaner than what most legacy tools produce. Internally, the pipeline looks like this: Python PDF → Docling Layout Analyzer → Table Structure Detection → TableItem → Pandas DataFrame → Excel Formatting Engine → Styled Workbook (.xlsx) And that’s where this package shines. Why Extracting Tables from PDFs Is Harder Than It Looks Although PDFs appear structured, they are fundamentally graphic layout containers, not semantic documents. A “table” inside a PDF is often just text placed at aligned coordinates. There is no real concept of: RowsColumnsSpansCell typesNumeric formats This is why most extractors fail when dealing with: 1. Merged Headers Financial tables frequently contain two or three header rows representing categories and subcategories. Traditional extractors flatten them incorrectly, losing context. 2. Parentheses for Negative Numbers Accountants often express negatives as (123) instead of -123. OCR and text-based extractors usually treat this as text. 3. Lack of Borders Some PDFs remove table lines for better readability, making geometric detection unreliable. 4. Complex Cell Spanning A single header may span 4–5 columns; most tools misalign these structures. 5. Scanned PDFs OCR introduces noise, misread digits, and extra whitespace. By integrating Docling and adding post-processing layers for numeric parsing, this library removes a majority of these obstacles, producing consistently structured DataFrames that convert cleanly into Excel. How this library differs from existing tools: FeatureTypical PDF table extractorspdf-tables-to-excelTable detectionOften inconsistentDocling-based, high accuracyOutputPandas DataFrame onlyFully formatted Excel fileMulti-table supportManualAutomatic, one sheet per tableBorders & formattingNoYes (clean, minimal Excel formatting)Auto column widthNoYesNumeric parsingLimited or noneCurrency, percentages, negativesCLI supportRareYes (pdf2styledexcel)OCR supportOptional, unreliableBuilt-in via Docling’s OCR layersFinance-ready?❌✔ This isn’t “just another wrapper.” It’s opinionated software created specifically to solve an end-to-end workflow problem. Technical Architecture 1. Table Extraction Engine (Docling) Docling outputs TableItem objects, each containing: cell geometryspanstext contentheader blocksrow alignmentsconfidence scores These are converted into Pandas DataFrames with robust normalization. 2. Normalization Layer This is where the tool outperforms general-purpose extractors. Converts 21.8% → 0.218Converts (4.3%) → -0.043Converts $1,234 → 1234.0Detects negatives in parenthesesHandles thousands separatorsCleans whitespaceSupports missing values gracefully This ensures Excel receives real numeric values, not text strings. 3. Excel Formatting Engine Built using XlsxWriter, the tool: Creates one sheet per tableApplies bold header stylingAuto-resizes columnsAdds thin borders to actual table areaFreezes header rowSupports two naming modes: sequential → Table 1, Table 2, ...by_page → Page 1 Table 1 The goal is to deliver Excel files that analysts actually want to use. Design Principles Behind the Library When designing pdf-tables-to-excel, I followed three core principles: 1. Zero Manual Cleanup Tools that return DataFrames still leave analysts to fix formatting. This library makes the Excel output the final product, with: Clean numeric typesPercent/currency conversionAuto column widthsProper bordersConsistent sheet naming 2. Predictable Behavior Given the same PDF, the output should always be deterministic. Many extractors produce different results if whitespace changes even slightly. By using Docling’s structured layout model, extraction becomes far more stable. 3. Batteries Included Analysts should not need to write helper scripts for each report. Everything — from table detection to Excel styling — is packaged into a single function call. This keeps the API clean while still allowing advanced customization through optional parameters. Extended Code Example Basic Usage Python from pdf_tables_to_excel import convert_pdf_to_excel convert_pdf_to_excel( input_pdf="Annual_Report.pdf", output_xlsx="extracted_tables.xlsx", sheet_naming="by_page", include_empty=False, ) Example workflow: Python tables = extract_tables("Earnings_Release.pdf") for t in tables: print(t.source_page, t.df.head()) Real-World Use Cases 1. Financial Services Extract tables from: Earnings releasesTrustee reportsLoan servicing tapesRegulatory disclosures (10-K, 10-Q)Fund factsheets 2. Data Science / ML Pipelines Convert PDF datasets into structured inputs for feature engineering and modeling. 3. Enterprise Automation Integrate in: ETL workflowsRPA pipelinesDocument intelligence systems

Ensuring data quality has become much harder because contemporary systems generate data at high volume, high velocity, and high variety. Ensuring data consistency, completeness, and accuracy is harder as large-scale data pipelines often pull data from different sources in different formats. Traditional manual review processes simply can't keep up as the datasets are constantly being expanded and updated. Manual checks not only cause delays but also rely heavily on human judgment, and when the workload is either too big or too fast, the checks are usually no longer applicable. This situation in large-scale environments results in a lack of anomaly identification, inconsistent validation, and increased operational risk. Automated validation and anomaly detection eliminate these drawbacks by carrying out a systematic, repeatable, and real-time quality check throughout the entire data pipeline. With these techniques, companies can detect errors at an early stage, apply standards across a large area, and reduce human involvement. This is a structured article that introduces a scalable framework for automated data quality assurance. It describes the principles, techniques, and structure necessary to provide operational high-quality data in the case of growing systems. Understanding Data Quality Challenges at Scale Data quality at scale is the condition of keeping data accurate, consistent, complete, and trustworthy throughout very large systems that manage incredibly large volumes in varied formats and practical applications. As data sizes grow, tiny discrepancies that are easy to manage in smaller environments become magnified. Moreover, large data pipelines lead to data drift and delayed detection, and they also make it quite difficult to maintain the same standards across geographically distributed sources. Poor data quality is a common issue with large systems. Changes to the schema can occur without prior notice, resulting in the disappearance of certain fields, changes to their type, or even changes in their meaning. Corrupt partitions in storage systems can render data partially or make it completely unreadable. Late-arriving data disrupts time-based aggregations and downstream computations. Pipeline failures, such as broken transforms, resource constraints, or dependency outages, result in incomplete or inconsistent outputs. When anomalies go undetected in such environments, they will spread very quickly to downstream analytics and machine learning workflows. The defective metrics, incorrect dashboards, and tainted training data will accumulate quickly, affecting decision-making and causing model performance to deteriorate. In high-scale pipelines, even a short period of quality issues can cause widespread notification delays, underscoring the need for automated detection and control mechanisms. Core Dimensions of Scalable Data Quality 1. Accuracy Accuracy is a measure of how much the data corresponds to the actual values that it represents. Maintaining accuracy becomes more difficult when data volume is large, due to many scattered ingestion points, diverse data sources, and parallel processing. Mismatched transformations or partial failures in distributed systems can create subtle, hard-to-locate, inaccurate cases that are difficult to repair. 2. Completeness Completeness ensures the presence of all mandatory fields, records, and partitions. In big data pipelines, the reasons for missing batches, dropped events, or incomplete partitions are often high-throughput ingestion and real-time streaming. Late-arriving data and partial writes are typical causes of large-scale completeness problems. 3. Consistency Consistency is all about synchronizing meaning, formatting, and structure between the datasets. In cases involving large amounts of data, different teams might publish data with evolving schemas, leading to misalignment. When data is stored and processed in separate locations, it is common to have multiple versions of the same dataset, especially when updates propagate unevenly across nodes or regions. 4. Timeliness Timeliness is a measure of how quickly data can be made available for use. High-speed streams, multi-stage pipelines, and large batch jobs can all add latency risks. Network congestion, queue backlogs, and distributed job scheduling can all contribute to delays in data delivery, leading to downstream analytics using outdated or incomplete data snapshots. 5. Validity Validity is a guarantee that data adheres to the rules, formats, ranges, and business constraints that are defined. The major problem with the scale is that real-time streams and schema evolution increase the likelihood of invalid values being introduced into the system. Some of the rules (or formulae) can be applied as follows: Completeness rate: Accuracy error rate: Code to check core quality dimensions: Scale-specific context: The quality of data in distributed systems is largely determined by factors such as parallelism, consistency at last, and replication across multiple regions. On the other hand, the real-time flows of data come with unanticipated event ordering, messaging inconsistencies, and micro-batching, which may all together alter the data patterns that were expected. The high-capacity ingestion of data magnifies all the dimensions of the quality because it raises the number of errors, and at the same time, it cuts down the time for manual correction. Timeliness lag: Building a Scalable Data Quality Framework Step 1: Define Scalable Quality Rules Create protocols that will operate smoothly in both batch and streaming environments. Choose to use flexible and parameterized validation logic that can be applied across the different datasets and stages of the pipeline. The rules that are scalable should take care of issues like schema acceptance, value ranges, completeness thresholds, and business constraints without the need for manual rewrites for each new source. Step 2: Profile and Assess Data at Scale Utilize distributed profiling techniques to reveal the data's characteristics, its distribution, and the presence of any anomalies, even when dealing with huge volumes of data. The likes of Spark, Snowflake, and BigQuery are the type of tools that enable concurrent profiling workloads. Use statistical sampling for rapid insight and periodic full scans to detect rare issues that might be hidden by the sampling method. Step 3: Automate Validation in Pipelines Integrate the rule-based validation checks right into ETL/ELT processes and streaming pipelines. The execution cycle for the pipeline will include automated schema validation, null checks, regex-based format checks, and threshold-driven rule application. Scalable frameworks optimized for distributed systems include Great Expectations, Deequ, and dbt tests, which are tools that come into play. Step 4: Implement Anomaly Detection Statistical and machine learning techniques are employed to detect deviations from expected patterns, including data drift, volume anomalies, distribution shifts, and missing or corrupt partitions. The automated large-scale anomaly detection, aligned with rule-based validation, is supported not only by using platforms such as Monte Carlo, Bigeye, and Anomalo but also by custom Spark-based jobs. Step 5: Real-Time Monitoring and Alerts Real-time dashboards should be created to continuously monitor quality metrics, including data freshness, completeness rates, and anomaly frequency. These dashboards can be integrated with event-driven alerting systems that send out instant notifications to the appropriate teams whenever any anomalies are detected. Consider observability practices such as lineage, metadata tracking, and health metrics as more important than individual validation checks to gain a comprehensive view of data behavior. Step 6: Create Continuous Feedback Loops Governance workflows and automated issue-tracking systems should be used to handle detected anomalies directly. To ensure no one person bears the responsibility alone, both data producers and consumers should participate in the remediation process. The feedback loop improves rule accuracy, making anomaly detection more reliable, and supports continuous trust across very large-scale data ecosystems. Batch validation pseudocode: Streaming anomaly detection pseudocode: Tools and Technologies for Automated Validation and Anomaly Detection In the case of such massive data volumes, quality is maintained through the use of specialized tools that have been automated for the detection and validation of abnormalities, and thus do not cause any delays to the pipeline. The technologies are divided into two main categories: rule-based validation tools and data observability/anomaly detection platforms. 1. Rule-Based Validation Tools These instruments guide you in setting up crystal clear criteria for what comprises "good" data. They are best suited for validating schemas, checking for nulls, enforcing thresholds, and other structured constraints at most. Great Expectations The framework defined by a Python-based tool enables you to not only set and run expectations about your data but also keep records of them. With its ability to handle both batch and streaming validation, it is a perfect fit for distributed pipelines. AWS Deequ It is a library based on Apache Spark that provides an environment for large-scale, rule-based data quality checks. The stringency of constraints can be defined, metrics can be computed for extensive data, etc., and the whole process would be carried out efficiently in a distributed setup. Soda Core/Soda Cloud It is a service that monitors SQL-based data validation along with an observability dashboard. For instance, Soda Cloud provides the alerting and trend analysis features. dbt Tests Despite being a transformation framework, dbt allows data tests such as uniqueness, null checks, and referential integrity, which are integrated directly into your ETL/ELT workflows, to be performed declaratively through its functionalities. A complete architecture implementing all the above tools can be seen below: 2. Data Observability and Anomaly Detection Platforms These tools primarily focus on detecting odd behaviors, changes in distribution, and other anomalies that may not be detected by rigid rules. Monte Carlo, Bigeye, and Anomalo: These are cloud data observability platforms that are always monitoring the data pipelines for any anomalies; their alerts are also tracked through lineage, and they constantly inform the teams beforehand. They use both statistical methods and machine learning techniques to discover not only volume but also schema and distribution's surprising changes.Stream Processors (Kafka, Flink): The high-throughput pipelines will not suffer from any delayed detection of problems in the live data streams, as the real-time stream processing frameworks can carry out validation and anomaly detection logic together with the high-throughput pipelines. Key Takeaways Total coverage is guaranteed by merging rule-based validation with observability/anomaly detection. Tools would need to work together effortlessly with both batch and streaming pipelines. Scalability, automation, and alerting are very important features for reducing the overhead of manual monitoring. Common Pitfalls and How to Avoid Them Even with automated validation and anomaly detection in place, large-scale data systems face recurring challenges. Understanding these pitfalls helps teams design more resilient frameworks. 1. Overloading Pipelines With Expensive Validation Tasks Problem: Even if one checks various records or entire batches, it could still result in pipeline slowing, increased costs, and a bottleneck. Solution: Apply sampling to datasets that have a very high volume, give first place to in-stream light tests, and reserve heavy computations for previously arranged batch runs. By using parameterization, let validations run either conditionally or asynchronously. Validation cost formula: Validation detector pseudocode: 2. Depending Only on Rule-Based Checks (No Drift Detection) Problem: Static checks can identify past issues, but cannot detect gradual changes in data distribution or schema drift. Solution: Use a mixture of rule-based checks and statistical or ML-driven anomaly detection to identify not only distribution changes but also volume spikes as well as unexpected patterns. 3. Ignoring Metadata (Lineage, Schema Changes) Problem: Teams are unable to grasp the repercussions of changes upstream or the failure of data pipelines that are not causing any disruptions, which, in turn, delays quick detection and troubleshooting. Solution: Have solid lineage, schema versioning, and metadata catalogs as part of the data management process. Employ tools that will not only detect schema changes automatically but also notify affected parties. 4. No SLA/SLO Definitions for Quality Problem: A precise assortment of service level agreements (SLAs) or objectives (SLOs) for data quality has not been determined so far, which has led to the situation where no performance standard is recognized universally, and varying degrees of application of rules are taking place. Solution: Clearly define the quality requirements for on-time, complete, correct, and legitimate. Notifications and corrective actions should be determined based on numerical thresholds. 5. Not Closing the Loop With Automated Remediation Problem: Detecting issues without a feedback mechanism leaves errors unresolved and increases downstream impact. Solution: Implement automated remediation where possible (e.g., reprocessing failed batches, triggering data producer alerts) and feed detected anomalies back into governance and improvement workflows. Key Takeaways Don’t depend solely on one technique; instead, apply a combination of rule-based, statistical, and ML-based verification methods. Integrate observability, metadata, and feedback loops into your strategy to reduce operational expenses and eliminate recurring errors. Define the quality targets explicitly to create accountability and achieve measurable improvement. Conclusion The increasing volume, speed, and diversity of data make it very difficult and unreliable to maintain data quality through manual controls. Thus, machine learning is no longer a choice; it is a necessity. By combining rule-based validation and anomaly detection, companies can stay one step ahead in their monitoring and actively manage data quality issues across both batch and streaming pipelines. The creation of scalable and automated quality frameworks makes data quality a strategic enabler rather than a reactive burden. Moreover, it gives companies the trust and the ability to use their data effectively.

Atomic writes used to be one of the important reasons we stuck with relational databases. The rule was simple: either all your updates succeed, or they all fail. But as we moved to NoSQL databases in distributed systems, we often traded that safety for scale. Now, the pendulum is swinging back. Developers building microservices and server-less apps are realizing that writing manual undo logic (compensating transactions) is a nightmare, and they want their NoSQL databases to handle that heavy lifting again. However, atomicity isn't standard across the cloud. AWS, Azure, GCP, and Alibaba all offer transaction capabilities, but they have wildly different rules regarding locking, limits, idempotency, and consistency guarantees. When building multi-cloud Java applications with NoSQL dependencies, you face the challenge of normalizing divergent provider semantics to ensure true portability. This is where MultiCloudJ comes in. In this article, we’ll break down exactly how atomic writes work inside AWS DynamoDB, Google Firestore, Azure Cosmos DB, and Alibaba Tablestore. Then, we will show how MultiCloudJ abstracts away these vendor-specific complexities to provide a unified, consistent way to handle atomic writes across any cloud. Atomic Writes vs. Batch Writes: A Critical Distinction Before diving in, it’s important to clarify terminology. Many developers mistakenly assume a batch operation is atomic. That assumption leads to subtle data corruption and unexpected partial states. Let's understand the core differences between batched writes and atomic write: Featurebatch writeatomic writes(Transactions)Primary GoalPerformance. Reduces network latency by combining requests.Consistency. Ensures data integrity (ACID). Failure ModePartial. Some items may succeed while others fail.All-or-Nothing. If one item fails, the entire group rolls back.RiskCan lead to data corruption or "zombie" records if error handling isn't perfect.Higher latency and cost, but guarantees a clean consistent state. Provider-by-Provider Comparison of Atomic Write Features Here’s a crisp comparison across the four major cloud NoSQL services: ProviderAtomic Writes supportGranularityIdempotency SupportAWS DynamoDBYesAcross TablesYesGCP FirestoreYesAcross CollectionsYesAzure Cosmos DBYesWithin Partition KeyYesAlibaba TablestoreYesWithin Partition KeyPartial Let's delve deeper into this: AWS DynamoDB: DynamoDB sets a high bar for NoSQL consistency, supporting true ACID transactions across multiple tables and items within the same AWS Region. As defined in the official AWS TransactWriteItems documentation: "TransactWriteItems is a synchronous and idempotent write operation that groups up to 100 write actions in a single all-or-nothing operation." Key Capabilities: Scope: Supports up to 100 operations in a single transaction (increased from the original 25) across different tables in the same region and account.Flexibility: Allows a mix of Put, Update, Delete, and ConditionCheck actions across different tables.Safety: Built-in idempotency tokens (ClientRequestToken) ensure that if a network error occurs during a retry, the write isn't applied twice, a critical feature for financial ledgers. However, note that the idempotency is guaranteed with the same token for just 10 minutes. Limitations: TransactWriteItems do not directly contains the read requests as part of transaction. In fact, there is TransactionReadItems for that purpose.Limit to just 25 items in TransactionWriteItems. Google Cloud Firestore: Firestore supports atomic multi-document transactions across collections. Its client SDKs manage the transaction loop for you (including automatic retries on contention), whereas DynamoDB exposes transactional APIs (TransactWriteItems, TransactGetItems) where you typically handle retries and error handling explicitly in the application code. Key Capabilities Global Scope: Supports atomic writes across any number of documents and collections within the database instance.Read and Writes in the same transaction: Support all types of get and put operations in the same transaction. However, read should be before the write for the item.Automatic Retries: If a write conflict occurs (e.g., concurrent modification), the client SDK automatically re-attempts the entire write operation.No hard limit on number of documents in transaction but can contain up-to 500 transformations to a single document in a transaction. Limitations Client-Side Dedup: No built-in idempotency token. You must implement your own logic to prevent duplicate processing if a network error occurs during the commit.Online Only: Atomic transactions fail immediately if the client is offline; they cannot be queued. Azure Cosmos DB: Cosmos DB prioritizes speed and predictability. To achieve this, it offers full ACID support via TransactionalBatch, but with a strict architectural boundary: The Logical Partition. Key Capabilities: Partition-Scoped Atomicity: Transactions can include up to 100 operations, but every item in the batch must share the same Partition Key.Performance: Because transactions never span across physical servers (shards), they are extremely fast and retain Cosmos DB's low-latency guarantees. Limitations No Cross-Partition Transactions: If your business logic requires atomically updating two items with different partition keys, Cosmos DB cannot do it natively.No Native Idempotency and Retries: Developers must build their own safety nets for network retries and timeouts. Applications are responsible for handling 429 (Throttling) errors and ensuring writes aren't duplicated during retries. Alibaba Cloud Tablestore: Tablestore (OTS) also provides local transactions bound to a single partition key-similar to Cosmos DB. It adopts a traditional "interactive" transaction model but confines it to a single partition key. Instead of sending all writes in one packet, you open a transaction, receive a Transaction ID, and use that ID for subsequent writes before finally committing. Key Capabilities: Interactive Lifecycle: Uses StartLocalTransaction, CommitTransaction, and AbortTransaction APIs, giving the client control over the logic flow.Isolation: Operations performed with the active Transaction ID are isolated from other readers until committed.60-Second Validity: The Transaction ID remains valid for up to 60 seconds. This allows for complex client-side logic (calculations between reads and writes) while holding the lock. Limitations Scope Limit:Strictly limited to a single partition key. Cross-partition updates must be handled asynchronously.Time-Bound Risk: If your client crashes or pauses (e.g., a long Java GC pause) and exceeds the 60-second window, the transaction expires and data is lost.No "Fire-and-Forget": Because it is stateful, you must explicitly handle the Abort signal if a step fails, otherwise you risk leaving locks open until they timeout. Cross-Cloud Design Lessons: What Developers Should Care About If you are building multi-cloud architectures, portable NoSQL abstractions, or systems that may migrate between clouds, instead of dealing with cloud specific features, you can rely on MultiCloudJ for semantics and keep the following in mind: Granularity Is Not Consistent Across Providers DynamoDB and Firestore: Cross-collection/tableCosmos and Tablestore: Single partition only You cannot assume multi-partition atomicity unless you stay inside AWS or GCP Firestore. Therefore, if if you want to make your application truly portable across multiple clouds, you shouldn't design for cross-partition atomic writes. Idempotency Guarantees Vary DynamoDB: Server-side idempotency tokensOthers: No native idempotency For high-volume workloads, add your own idempotency keys (UUIDs, request IDs, etc.). Retry Semantics Differ DynamoDB: Built-in deduplicationFirestore: Client-driven retry loopsCosmos/Tablestore: Must implement manual retry logic Your application should standardize retry behavior if targeting multiple clouds. Cross-Cloud Abstractions Cross-Cloud Abstractions Must Target the Lowest Common Denominator for portability. In MultiCloudJ, the least common denominator at partition scope is supported, this is the safest universal model. Example using atomic writes in MultiCloudJ: Java @AllArgsConstructor @Data @NoArgsConstructor static class Book { private String title; private Person author; private String publisher; private float price; private Map<String, Integer> tableOfContents; private Object docRevision; } CollectionOptions collectionOptions = new CollectionOptions.CollectionOptionsBuilder() .withTableName(tableName) .withPartitionKey(KEY_TITLE) .withSortKey(KEY_PUBLISHER) .withRevisionField(REVISION_FIELD) .withAllowScans(true) .build(); DocStoreClient client = DocStoreClient.builder("aws") .withRegion(REGION) .withCollectionOptions(collectionOptions) .build(); // Create multiple books with atomic writes client.getActions() .enableAtomicWrites() // <- set this and all subsequent writes will be atomic .create(new Document(new Book("RedBook", SAMPLE_PERSON, "CA", 4.99f, null, null))) .create(new Document(new Book("GreenBook", SAMPLE_PERSON, "NY", 5.99f, null, null))) .create(new Document(new Book("BlueBook", SAMPLE_PERSON, "TX", 6.99f, null, null))) .run(); for detailed example: you can ref examples in MultiCloudJ. Conclusion: Atomic Writes Are Universal in Need, But Not in Implementation Every major cloud NoSQL database now supports atomic writes or transactions, but not in the same way: AWS DynamoDB: Cross-table, idempotent, strongest semanticsGCP Firestore: Cross-collection, but client-driven retriesAzure Cosmos DB: Fast, partition-scoped atomicityAlibaba Tablestore: Partition-scoped atomicity with temporary transaction IDs The success of a multi-cloud application hinges on its data strategy. If you don't account for differences in partition keys and idempotency across providers, you risk unpredictable behavior and data inconsistency. Solving this usually means writing complex boilerplate code to bridge different provider SDKs. MultiCloudJ solves this friction. It provides a standardized interface for transaction management, meaning you don't have to learn the low-level intricacies of every provider to achieve atomic writes.

Transform your MCP server into an HTTP API that anyone can access from anywhere The Goal You have an MCP server running locally. You want others to use it via HTTP calls. Before: Only works on your machine via stdio After: Works from anywhere via HTTP requests Tech Stack Architecture Plain Text ┌─────────────────┐ ┌───────────────────────────────────┐ │ Internet │ │ Your Machine │ │ Users │ │ │ │ │ ngrok tunnel │ ┌─────────────┐ stdio pipes │ │ Mobile │◄──────────────────►│ │ Express.js │◄────────────────► │ │ Browser │ │ │ HTTP API │ ┌─────────────┐ │ │ API Calls │ │ │ (Port 3000) │ │ MCP Server │ │ │ │ │ └─────────────┘ │ (Your Code) │ │ └─────────────────┘ │ └─────────────┘ │ └───────────────────────────────────┘ HTTP Requests MCP Protocol Messages MCP Tools (GET/POST) ←────────→ (stdin/stdout) ←────► (Unchanged) Communication Flow Internet → ngrok: HTTP requests from anywherengrok → Express.js: Tunneled to your local machineExpress.js → MCP: JSON-RPC via stdio pipesMCP → Express.js: Results via stdioExpress.js → Internet: HTTP responses back to caller How MCP Communication Works Your MCP server uses the Model Context Protocol, which can communicate via different transports: Common transports: stdio (stdin/stdout)  –  Most common for local serversServer-Sent Events (SSE)  –  HTTP-based communicationWebSockets  –  Real-time bidirectional communication For this guide, we assume your MCP server uses stdio, which is the most typical setup for local MCP servers. The key concept: Our Express.js wrapper handles all protocol translation, regardless of the specific MCP message format your server uses. The Bridge: HTTP to MCP Pseudo code flow: Plain Text START MCP_SERVER_PROCESS ON HTTP_REQUEST: - Convert HTTP to MCP message format - Send to MCP process via stdin - Wait for response from stdout - Convert MCP response to HTTP - Return to client Create server.js: JavaScript const express = require('express'); const { spawn } = require('child_process'); const app = express(); app.use(express.json()); let mcp = null; let requestId = 1; const pending = new Map(); let buffer = ''; // Start MCP server function startMCP() { // Replace with your MCP server command mcp = spawn('uvx', ['your-mcp-package']); mcp.stdout.on('data', (data) => { buffer += data.toString(); // Process complete JSON messages const lines = buffer.split('\n'); buffer = lines.pop(); // Keep incomplete line lines.forEach(line => { if (line.trim()) { try { const response = JSON.parse(line); // TODO: Adjust this to match your MCP server's response format // This assumes responses have an 'id' field - modify as needed const requestId = response.id; // Change this line for your format const request = pending.get(requestId); if (request) { pending.delete(requestId); request.resolve(response); } } catch (error) { console.log('MCP output:', line); } } }); }); mcp.stderr.on('data', (data) => { console.error('MCP error:', data.toString()); }); mcp.on('close', (code) => { console.log(`MCP server closed with code ${code}`); // Auto-restart in production setTimeout(startMCP, 1000); }); } // Send request to MCP async function callMCP(tool, arguments) { if (!mcp) throw new Error('MCP server not running'); const id = requestId++; // TODO: Replace this with YOUR MCP server's exact message format // This is just an example - check your MCP server's documentation const request = { id, method: "your_method_name", // Replace with actual method params: { tool: tool, args: arguments // Adjust structure to match your server } }; return new Promise((resolve, reject) => { pending.set(id, { resolve, reject }); mcp.stdin.write(JSON.stringify(request) + '\n'); // Timeout after 30 seconds setTimeout(() => { if (pending.has(id)) { pending.delete(id); reject(new Error('Request timeout')); } }, 30000); }); } // API Endpoints app.post('/api/:tool', async (req, res) => { try { const result = await callMCP(req.params.tool, req.body); // TODO: Adjust error checking based on your MCP server's response format // This is just an example - modify based on how your server indicates errors if (result.error) { return res.status(400).json({ error: result.error }); } // Return the full response - adjust as needed for your format res.json(result); } catch (error) { res.status(500).json({ error: error.message }); } }); app.get('/health', (req, res) => { res.json({ status: mcp ? 'running' : 'stopped', uptime: process.uptime() }); }); // Test interface app.get('/', (req, res) => { res.send(` <h2>MCP API Test</h2> <form onsubmit="test(event)"> <input name="tool" placeholder="Tool name" required><br><br> <textarea name="args" placeholder='{"key": "value"}'></textarea><br><br> <button>Execute</button> </form> <pre id="output"></pre> <script> async function test(e) { e.preventDefault(); const form = new FormData(e.target); const tool = form.get('tool'); const args = JSON.parse(form.get('args') || '{}'); try { const res = await fetch('/api/' + tool, { method: 'POST', headers: {'Content-Type': 'application/json'}, body: JSON.stringify(args) }); const result = await res.json(); document.getElementById('output').textContent = JSON.stringify(result, null, 2); } catch (error) { document.getElementById('output').textContent = 'Error: ' + error.message; } } </script> `); }); // Graceful shutdown process.on('SIGINT', () => { if (mcp) mcp.kill(); process.exit(0); }); // Start everything startMCP(); app.listen(3000, () => { console.log('API running on http://localhost:3000'); console.log('Test interface available at the root URL'); }); Important: Customize for Your MCP Server The code above is a template. You must customize these parts: Spawn command (line ~15): Replace ['uvx', ['your-mcp-package']] with your server's start commandMessage format (line ~45): Replace the request object with your server’s expected formatResponse handling (line ~25): Adjust response.id to match your server's response structureError checking (line ~75): Modify based on how your server indicates errors To find your format, run your MCP server manually and observe the exact JSON messages it expects/returns. Dependencies JSON { "name": "mcp-api-wrapper", "dependencies": { "express": "^4.18.2" } } Setup and Test Shell # 1. Install dependencies npm install # 2. Update the spawn command in server.js to match your MCP server: # spawn('node', ['your-server.js']) # spawn('python', ['server.py']) # spawn('uvx', ['your-package']) # 3. Start the API node server.js # 4. Make it public (new terminal) npx ngrok http 3000 Usage Examples Test in browser: Visit your ngrok URL Call from code: Shell curl -X POST https://your-url.ngrok.io/api/your_tool \ -H "Content-Type: application/json" \ -d '{"param": "value"}' From mobile: Same URL works anywhere Production Deployment Replace ngrok with proper hosting: Railway/Render: Push to GitHub, auto-deployVPS: Docker + nginx reverse proxyCloud Run: Containerized deployment Add rate limiting, authentication, and monitoring as needed. The core pattern works with any MCP server. Just change the spawn command.