Databases

Think your organization is too small to be a target for threat actors? Think again. In 2025, attackers no longer distinguish between size or sector. Whether you’re a flashy tech giant, a mid-sized auto dealership software provider, or a small startup, if you store data someone is trying to access it. As security measures around production environments strengthen, which they have, attackers are shifting left—straight into the software development lifecycle (SDLC). These less-protected and complex environments have become prime targets, where gaps in security can expose sensitive data and derail operations if exploited. That’s why recognizing the warning signs of nefarious behavior is critical. But identification alone isn’t enough—security and development teams must work together to address these risks before attackers exploit them. From suspicious clone activity to overlooked code review changes, subtle indicators can reveal when bad actors are lurking in your development environment. With most organizations prioritizing speed and efficiency, pipeline checks become generic, human and non-human accounts retain too many permissions, and risky behaviors go unnoticed. While Cloud Security Posture Management has matured in recent years, development environments often lack the same level of security. Take last year’s EmeraldWhale breach as an example. Attackers cloned more than 10,000 private repositories and siphoned out 15,000 credentials through misconfigured Git repositories and hardcoded secrets. They monetized access, selling credentials and target lists on underground markets while extracting even more sensitive data. And these threats are on the rise, where a single oversight in repository security can snowball into a large-scale breach, putting thousands of systems at risk. Organizations can’t afford to react after the damage is done. Without real-time detection of anomalous behavior, security teams may not even realize a compromise has occurred in their development environment until it’s too late. 5 Examples of Anomalous Behavior in the SDLC Spotting a threat actor in a development environment isn’t as simple as catching an unauthorized login attempt or detecting malware. Attackers blend into normal workflows, leveraging routine developer actions to infiltrate repositories, manipulate infrastructure and extract sensitive data. Security teams, and even developers, must recognize the subtle but telling signs of suspicious activity: 1. Pull requests merged without resolving recommended changes Pull requests (PRs) merged without addressing recommended code review changes may introduce bugs, expose sensitive information or weaken security controls in your codebase. When feedback from reviewers is ignored, these potentially harmful changes can slip into production, creating vulnerabilities attackers could exploit. 2. Unapproved Terraform deployment configurations Unreviewed changes to Terraform configuration files can lead to misconfigured infrastructure deployments. When modifications bypass the approval process, they may introduce security vulnerabilities, cause service disruptions or lead to non-compliant infrastructure settings, increasing risk of exposure. 3. Suspicious clone volumes Abnormal spikes in repository cloning activity may indicate potential data exfiltration from Software Configuration Management (SCM) tools. When an identity clones repositories at unexpected volumes or times outside normal usage patterns, it could signal an attempt to collect source code or sensitive project data for unauthorized use. 4. Repositories cloned without subsequent activity Cloned repositories that remain inactive over time can be a red flag. While cloning is a normal part of development, a repository that is copied but shows no further activity may indicate an attempt to exfiltrate data rather than legitimate development work. 5. Over-privileged users or service accounts with no commit history approving PRs Pull Request approvals from identities lacking repository activity history may indicate compromised accounts or an attempt to bypass code quality safeguards. When changes are approved by users without prior engagement in the repository, it could be a sign of malicious attempts to introduce harmful code or represent reviewers who may overlook critical security vulnerabilities. Practical Guidance for Developers and Security Teams Recognizing anomalous behavior is only the first step—security and development teams must work together to implement the right strategies to detect and mitigate risks before they escalate. A proactive approach requires a combination of policy enforcement, identity monitoring and data-driven threat prioritization to ensure development environments remain secure. To strengthen security across development pipelines, organizations should focus on four key areas: CISOs and engineering should develop a strict set of SDLC policies: Enforce mandatory PR reviews, approval requirements for Terraform changes and anomaly-based alerts to detect when security policies are bypassed. Track identity behavior and access patterns: Monitor privilege escalation attempts, flag PR approvals from accounts with no prior commit history and correlate developer activity with security signals to identify threats. Audit repository clone activity: Analyze clone volume trends for spikes in activity or unexpected access from unusual locations and track cloned repositories to determine if they are actually used for development.Prioritize threat investigations with risk scoring: Assign risk scores to developer behaviors, access patterns and code modifications to filter out false positives and focus on the most pressing threats. By implementing these practices, security and development teams can stay ahead of attackers and ensure that development environments remain resilient against emerging threats. Collaboration as the Path Forward Securing the development environment requires a shift in mindset. Simply reacting to threats is no longer enough; security must be integrated into the development lifecycle from the start. Collaboration between AppSec and DevOps teams is critical to closing security gaps and ensuring that proactive measures don’t come at the expense of innovation. By working together to enforce security policies, monitor for anomalous behavior and refine threat detection strategies, teams can strengthen defenses without disrupting development velocity. Now is the time for organizations to ask the hard questions: How well are security measures keeping up with the speed of development? Are AppSec teams actively engaged in identifying threats earlier in the process? What steps are being taken to minimize risk before attackers exploit weaknesses? A security-first culture isn’t built overnight, but prioritizing collaboration across teams is a decisive step toward securing development environments against modern threats.

Introduction It has long been a difficult task to generate precise SQL queries from customers' natural language inquiries (text-to-SQL). Understanding user queries, appreciating the structure and semantics of a particular database schema, and accurately producing executable SQL statements are some of the elements that contribute to the complexity. Large language models (LLMs) have opened up new avenues for text-to-SQL research. Superior natural language understanding skills are demonstrated by LLMs, and their scalability offers special chances to improve SQL creation. Methodology The implementation of LLM-based text-to-SQL methods primarily depends on two key paradigms: In-Context Learning (ICL): Instead of retraining the model, ICL allows LLMs to generate SQL queries by providing relevant examples or schema details in the prompt.Example: Giving the model table structures and a few question-SQL pairs as context before asking it to generate a query.This method leverages powerful pre-trained models like GPT-4 or LLaMA without additional fine-tuning. 2. Fine-Tuning: This involves training an LLM on a custom dataset of natural language queries and their corresponding SQL translations.Fine-tuning improves accuracy, especially for domain-specific databases or complex SQL queries.Open-source models like LLaMA, T5, and BART can be fine-tuned for better performance. Technical Challenges in Text-to-SQL Text-to-SQL models face several challenges: Understanding User Queries – Questions can be unclear or incomplete, requiring smart language processing.Mapping to the Database – The model must correctly match user questions to different database structures.Generating Accurate SQL – Creating correct and meaningful SQL queries is difficult, especially for complex cases.Adapting to New Databases – Many models struggle with new database formats without extra training.Efficiency and Scalability – Queries must be generated quickly and accurately, even for large databases. Evolution of Text-to-SQL Systems Text-to-SQL methods have evolved over time: Rule-Based and Template Methods – Early systems used fixed templates and rules to convert text into SQL, but they were inflexible and required a lot of manual work.Neural Network Approaches – Deep learning models improved SQL generation by learning from data instead of relying on fixed rules.Pre-Trained Language Models (PLMs) – Models like BERT and T5 were fine-tuned for text-to-SQL, improving accuracy using contextual understanding.Large Language Models (LLMs) – Advanced models like GPT-4 and PaLM handle complex queries better, thanks to their extensive training on diverse datasets. Advances in LLM-Based Text-to-SQL LLMs have greatly improved text-to-SQL generation by: Better Understanding – They grasp user intent more accurately, reducing confusion.Learning Without Extensive Training – LLMs can generate SQL with little to no fine-tuning using in-context learning.Adapting to Different Databases – They adjust to various database structures using smart prompts.Handling Complex Queries – They create more accurate SQL queries, even for difficult tasks.Supporting Conversations – LLMs can remember context across multiple interactions, improving responses in multi-turn dialogues. Challenges and Future Research Directions LLM-based text-to-SQL systems still have some challenges: Incorrect SQL (Hallucinations) – Sometimes, they generate SQL that looks right but doesn’t actually work.High Costs – Running large models requires a lot of computing power, making real-world use expensive.Data Privacy Concerns – Keeping user data secure and meeting regulations is a major challenge.Hard to Debug – Fixing mistakes in AI-generated SQL can be tricky.Real-World Integration – Adapting to different databases, especially in large companies, needs improvement. Unlocking Data with Language: Real-World Applications of Text-to-SQL Interfaces The ability to swiftly access and evaluate data has grown essential as firms work to become more data-driven. However, traditional methods frequently necessitate technical knowledge, which restricts database interaction. Here comes the revolutionary solution of text-to-SQL interfaces driven by large language models (LLMs), which enable users to query databases in plain English. Because these platforms allow non-technical users, such as marketers or sales managers, to ask questions like "What was our revenue last quarter by region?" without requiring IT help or SQL expertise, they are transforming business intelligence. Clinicians and researchers can streamline research and decision-making in the healthcare industry by extracting information such as "How many diabetic patients over 65 were admitted last year?" Natural language is useful for financial firms for analyzing client behavior or spotting fraud. Risk analysts would inquire, "Show suspicious transactions over $10,000 from last month," for instance. While educators use these technologies to easily check enrollment trends or student performance, teams in e-commerce can use them to investigate product trends, inventory status, or return rates with straightforward queries. In addition to analytics, text-to-SQL solutions are used by CRM and customer support teams to track patterns in support tickets or behavior related to churn. Asking questions like "What were the last 10 updates to the orders table?" might help even developers debug databases or query logs. Text-to-SQL technologies democratize data access across sectors by removing the SQL barrier. By making databases as approachable as a colleague, these tools lessen the need for data teams, speed up insights, and enable better informed decisions. Conclusion An important development in natural language database querying is the incorporation of LLMs into text-to-SQL systems. LLMs will be essential in bridging the gap between formal database interactions and natural language understanding as they develop further.

In my experience managing large-scale Kubernetes deployments across multi-cloud platforms, traffic control often becomes a critical bottleneck, especially when dealing with mixed workloads like APIs, UIs, and transactional systems. While Istio’s default ingress gateway does a decent job, I found that relying on a single gateway can introduce scaling and isolation challenges. That’s where configuring multiple Istio Ingress Gateways can make a real difference. In this article, I’ll walk you through how I approached this setup, what benefits it unlocked for our team, and the hands-on steps we used, along with best practices and YAML configurations that you can adapt in your own clusters. Why Do We Use an Additional Ingress Gateway? Using an additional Istio Ingress Gateway provides several advantages: Traffic isolation: Route traffic based on workload-specific needs (e.g., API traffic vs. UI traffic or transactional vs. non-transactional applications).Multi-tenancy: Different teams can have their gateway while still using a shared service mesh.Scalability: Distribute traffic across multiple gateways to handle higher loads efficiently.Security and compliance: Apply different security policies to specific gateway instances.Flexibility: You can create any number of additional ingress gateways based on project or application needs.Best practices: Kubernetes teams often use Horizontal Pod Autoscaler (HPA), Pod Disruption Budget (PDB), Services, Gateways, and Region-Based Filtering (via Envoy Filters) to enhance reliability and performance. Understanding Istio Architecture Istio IngressGateway and Sidecar Proxy: Ensuring Secure Traffic Flow When I first began working with Istio, one of the key concepts that stood out was the use of sidecar proxies. Every pod in the mesh requires an Envoy sidecar to manage traffic securely. This ensures that no pod can bypass security or observability policies. Without a sidecar proxy, applications cannot communicate internally or with external sources.The Istio Ingress Gateway manages external traffic entry but relies on sidecar proxies to enforce security and routing policies.This enables zero-trust networking, observability, and resilience across microservices. How Traffic Flows in Istio With Single and Multiple Ingress Gateways In an Istio service mesh, all external traffic follows a structured flow before reaching backend services. The Cloud Load Balancer acts as the entry point, forwarding requests to the Istio Gateway Resource, which determines traffic routing based on predefined policies. Here's how we structured the traffic flow in our setup: Cloud Load Balancer receives external requests and forwards them to Istio's Gateway Resource.The Gateway Resource evaluates routing rules and directs traffic to the appropriate ingress gateway: Primary ingress gateway: Handles UI requests.Additional ingress gateways: Route API, transactional, and non-transactional traffic separately.Envoy Sidecar Proxies enforce security policies, manage traffic routing, and monitor observability metrics.Requests are forwarded to the respective Virtual Services, which process and direct them to the final backend service. This structure ensures better traffic segmentation, security, and performance scalability, especially in multi-cloud Kubernetes deployments. Figure 1: Istio Service Mesh Architecture – Traffic routing from Cloud Load Balancer to Istio Gateway Resource, Ingress Gateways, and Service Mesh. Key Components of Istio Architecture Ingress gateway: Handles external traffic and routes requests based on policies.Sidecar proxy: Ensures all service-to-service communication follows Istio-managed rules.Control plane: Manages traffic control, security policies, and service discovery. Organizations can configure multiple Istio Ingress Gateways by leveraging these components to enhance traffic segmentation, security, and performance across multi-cloud environments. Comparison: Single vs. Multiple Ingress Gateways We started with a single ingress gateway and quickly realized that as traffic grew, it became a bottleneck. Splitting traffic using multiple ingress gateways was a simple but powerful change that drastically improved routing efficiency and fault isolation. On the other hand, multiple ingress gateways allowed better traffic segmentation for APIs, UI, and transaction-based workloads, improved security enforcement by isolating sensitive traffic, and scalability and high availability, ensuring each type of request is handled optimally. The following diagram compares a single Istio Ingress Gateway with multiple ingress gateways for handling API and web traffic. Figure 2: Single vs. Multiple Istio Ingress Gateways – Comparing routing, traffic segmentation, and scalability differences. Key takeaways from the comparison: A single Istio Ingress Gateway routes all traffic through a single entry point, which may become a bottleneck.Multiple ingress gateways allow better traffic segmentation, handling API traffic and UI traffic separately.Security policies and scaling strategies can be defined per gateway, making it ideal for multi-cloud or multi-region deployments. Feature Single Ingress Gateway Multiple Ingress Gateways Traffic Isolation No isolation, all traffic routes through a single gateway Different gateways for UI, API, transactional traffic Resilience If the single gateway fails, traffic is disrupted Additional ingress gateways ensure redundancy Scalability Traffic bottlenecks may occur Load distributed across multiple gateways Security Same security rules apply to all traffic shared Custom security policies per gateway Setting Up an Additional Ingress Gateway How Additional Ingress Gateways Improve Traffic Routing We tested routing different workloads (UI, API, transactional) through separate gateways. This gave each gateway its own scaling behavior and security profile. It also helped isolate production incidents — for example, UI errors no longer impacted transactional requests. The diagram below illustrates how multiple Istio Ingress Gateways efficiently manage API, UI, and transactional traffic. Figure 3: Multi-Gateway Traffic Flow – External traffic segmentation across API, UI, and transactional ingress gateways. How it works: Cloud Load Balancer forwards traffic to the Istio Gateway Resource, which determines routing rules.Traffic is directed to different ingress gateways: The Primary ingress gateway handles UI traffic.The API Ingress Gateway handles API requests.The Transactional Ingress Gateway ensures financial transactions and payments are processed securely.The Service Mesh enforces security, traffic policies, and observability. Step 1: Install Istio and Configure Operator For our setup, we used Istio’s Operator pattern to manage lifecycle operations. It’s flexible and integrates well with GitOps workflows. Prerequisites Kubernetes cluster with Istio installedHelm installed for deploying Istio components Ensure you have Istio installed. If not, install it using the following commands: Plain Text curl -L https://istio.io/downloadIstio | ISTIO_VERSION=$(istio_version) TARGET_ARCH=x86_64 sh - export PATH="$HOME/istio-$ISTIO_VERSION/bin:$PATH" Initialize the Istio Operator Plain Text istioctl operator init Verify the Installation Plain Text kubectl get crd | grep istio Alternative Installation Using Helm Istio Ingress Gateway configurations can be managed using Helm charts for better flexibility and reusability. This allows teams to define customizable values.yaml files and deploy gateways dynamically. Helm upgrade command: Plain Text helm upgrade --install istio-ingress istio/gateway -f values.yaml This allows dynamic configuration management, making it easier to manage multiple ingress gateways. Step 2: Configure Additional Ingress Gateways With IstioOperator We defined separate gateways in the IstioOperator config (additional-ingress-gateway.yaml) — one for UI and one for API — and kept them logically grouped using Helm values files. This made our Helm pipelines cleaner and easier to scale or modify. Below is an example configuration to create multiple additional ingress gateways for different traffic types: YAML apiVersion: install.istio.io/v1alpha1 kind: IstioOperator metadata: name: additional-ingressgateways namespace: istio-system spec: components: ingressGateways: - name: istio-ingressgateway-ui enabled: true k8s: service: type: LoadBalancer - name: istio-ingressgateway-api enabled: true k8s: service: type: LoadBalancer Step 3: Additional Configuration Examples for Helm We found that adding HPA and PDB configs early helped ensure we didn’t hit availability issues during upgrades. This saved us during one incident where the default config couldn’t handle a traffic spike in the API gateway. Below are sample configurations for key Kubernetes objects that enhance the ingress gateway setup: Horizontal Pod Autoscaler (HPA) YAML apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: ingressgateway-hpa namespace: istio-system spec: minReplicas: 2 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 80 scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: istio-ingressgateway Pod Disruption Budget (PDB) YAML apiVersion: policy/v1 kind: PodDisruptionBudget metadata: name: ingressgateway-pdb namespace: istio-system spec: minAvailable: 1 selector: matchLabels: app: istio-ingressgateway Region-Based Envoy Filter YAML apiVersion: networking.istio.io/v1alpha3 kind: EnvoyFilter metadata: name: region-header-filter namespace: istio-system spec: configPatches: - applyTo: HTTP_FILTER match: context: GATEWAY listener: filterChain: filter: name: envoy.filters.network.http_connection_manager subFilter: name: envoy.filters.http.router proxy: proxyVersion: ^1\.18.* patch: operation: INSERT_BEFORE value: name: envoy.filters.http.lua typed_config: '@type': type.googleapis.com/envoy.extensions.filters.http.lua.v3.Lua inlineCode: | function envoy_on_response(response_handle) response_handle:headers():add("X-Region", "us-eus"); end Step 4: Deploy Additional Ingress Gateways Apply the configuration using istioctl: Plain Text istioctl install -f additional-ingress-gateway.yaml Verify that the new ingress gateways are running: Plain Text kubectl get pods -n istio-system | grep ingressgateway After applying the configuration, we monitored the rollout using kubectl get pods and validated each gateway's service endpoint. Naming conventions like istio-ingressgateway-ui really helped keep things organized. Step 5: Define Gateway Resources for Each Ingress Each ingress gateway should have a corresponding gateway resource. Below is an example of defining separate gateways for UI, API, transactional, and non-transactional traffic: YAML apiVersion: networking.istio.io/v1alpha3 kind: Gateway metadata: name: my-ui-gateway namespace: default spec: selector: istio: istio-ingressgateway-ui servers: - port: number: 443 name: https protocol: HTTPS hosts: - "ui.example.com" Repeat similar configurations for API, transactional, and non-transactional ingress gateways. Make sure your gateway resources use the correct selector. We missed this during our first attempt, and traffic didn’t route properly — a simple detail, big impact. Step 6: Route Traffic Using Virtual Services Once the gateways are configured, create Virtual Services to control traffic flow to respective services. YAML apiVersion: networking.istio.io/v1alpha3 kind: VirtualService metadata: name: my-api-service namespace: default spec: hosts: - "api.example.com" gateways: - my-api-gateway http: - route: - destination: host: my-api port: number: 80 Repeat similar configurations for UI, transactional, and non-transactional services. Just a note that VirtualServices gives you fine-grained control over traffic. We even used them to test traffic mirroring and canary rollouts between the gateways. Resilience and High Availability With Additional Ingress Gateways One of the biggest benefits we noticed: zero downtime during regional failovers. Having dedicated gateways meant we could perform rolling updates with zero user impact. This model also helped us comply with region-specific policies by isolating sensitive data flows per gateway — a crucial point when dealing with financial workloads. If the primary ingress gateway fails, additional ingress gateways can take over traffic seamlessly.When performing rolling upgrades or Kubernetes version upgrades, separating ingress traffic reduces downtime risk.In multi-region or multi-cloud Kubernetes clusters, additional ingress gateways allow better control of regional traffic and compliance with local regulations. Deploying additional IngressGateways enhances resilience and fault tolerance in a Kubernetes environment. Best Practices and Lessons Learned Many teams forget that Istio sidecars must be injected into every application pod to ensure service-to-service communication. Below are some lessons we learned the hard way When deploying additional ingress gateways, consider implementing: Horizontal Pod Autoscaler (HPA): Automatically scale ingress gateways based on CPU and memory usage.Pod Disruption Budgets (PDB): Ensure high availability during node upgrades or failures.Region-Based Filtering (EnvoyFilter): Optimize traffic routing by dynamically setting request headers with the appropriate region.Dedicated services and gateways: Separate logical entities for better security and traffic isolation.Ensure automatic sidecar injection is enabled in your namespace: Plain Text kubectl label namespace <your-namespace> istio-injection=enabled Validate that all pods have sidecars using: Plain Text kubectl get pods -n <your-namespace> -o wide kubectl get pods -n <your-namespace> -o jsonpath='{.items[*].spec.containers[*].name}' | grep istio-proxy Without sidecars, services will not be able to communicate, leading to failed requests and broken traffic flow. When upgrading additional ingress gateways, consider the following: Delete old Istio configurations (if needed): If you are upgrading or modifying Istio, delete outdated configurations: Plain Text kubectl delete mutatingwebhookconfigurations.admissionregistration.k8s.io istio-sidecar-injector kubectl get crd --all-namespaces | grep istio | awk '{print $1}' | xargs kubectl delete crd Ensure updates to proxy version, deployment image, and service labels during upgrades to avoid compatibility issues. YAML proxyVersion: ^1.18.* image: docker.io/istio/proxyv2:1.18.6 Scaling down Istio Operator: Before upgrading, scale down the Istio Operator to avoid disruptions. Plain Text kubectl scale deployment -n istio-operator istio-operator --replicas=0 Backup before upgrade: Plain Text kubectl get deploy,svc,cm,secret -n istio-system -o yaml > istio-backup.yaml Monitoring and Observability With Grafana With Istio's built-in monitoring, Grafana dashboards provide a way to segregate traffic flow by ingress type: Monitor API, UI, transactional, and non-transactional traffic separately.Quickly identify which traffic type is affected when an issue occurs in Production using Prometheus-based metricsIstio Gateway metrics can be monitored in Grafana & Prometheus to track traffic patterns, latency, and errors.It provides real-time metrics for troubleshooting and performance optimization.Using PrometheusAlertmanager, configure alerts for high error rates, latency spikes, and failed request patterns to improve reliability. FYI, we extended our dashboards in Grafana to visualize traffic per gateway. This was a game-changer — we could instantly see which gateway was spiking and correlate it to service metrics. Prometheus alerting was configured to trigger based on error rates per ingress type. This helped us catch and resolve issues before they impacted end users. Conclusion Implementing multiple Istio Ingress Gateways significantly transformed the architecture of our Kubernetes environments. This approach enabled us to independently scale different types of traffic, enforce custom security policies per gateway, and gain enhanced control over traffic management, scalability, security, and observability. By segmenting traffic into dedicated ingress gateways — for UI, API, transactional, and non-transactional services — we achieved stronger isolation, improved load balancing, and more granular policy enforcement across teams. This approach is particularly critical in multi-cloud Kubernetes environments, such as Azure AKS, Google GKE, Amazon EKS, Red Hat OpenShift, VMware Tanzu Kubernetes Grid, IBM Cloud Kubernetes Service, Oracle OKE, and self-managed Kubernetes clusters, where regional traffic routing, failover handling, and security compliance must be carefully managed. By leveraging best practices, including: Sidecar proxies for service-to-service securityHPA (HorizontalPodAutoscaler) for autoscalingPDB (PodDisruptionBudget) for availabilityEnvoy filters for intelligent traffic routingHelm-based deployments for dynamic configuration Organizations can build a highly resilient and efficient Kubernetes networking stack. Additionally, monitoring dashboards like Grafana and Prometheus provide deep observability into ingress traffic patterns, latency trends, and failure points, allowing real-time tracking of traffic flow, quick root-cause analysis, and proactive issue resolution. By following these principles, organizations can optimize their Istio-based service mesh architecture, ensuring high availability, enhanced security posture, and seamless performance across distributed cloud environments. References Istio Architecture OverviewIstio Ingress Gateway vs. Kubernetes IngressIstio Install Guide (Using Helm or Istioctl)Istio Operator & Profiles for Custom Deployments Best Practices for Istio Sidecar InjectionIstio Traffic Management: VirtualServices, Gateways & DestinationRules

Google Cloud Workstations provide powerful, managed solutions for modern software development. By offering secure, consistent, and accessible cloud-based development environments, they tackle common frustrations associated with local setups, like configuration drift, dependency issues, and security concerns. Utilizing containerization and Google Cloud's scalable infrastructure, Workstations empower developers to code from anywhere with their favorite IDEs, guaranteeing a standardized and secure workflow. This approach simplifies developer onboarding, boosts collaboration, and significantly increases productivity by shifting the focus from environment management to writing code. Prerequisites Enable the Cloud Workstations API: Before you begin, ensure the necessary API is active. Go to the Google Cloud Console's APIs & Services section. Search for and enable the "Cloud Workstations API" if it is not currently enabled. A Virtual Private Cloud (VPC) acts as your isolated, private network space within Google Cloud, allowing you to manage your cloud resources securely. It's the bedrock of your cloud networking, giving you control over IP addressing and security rules. Subnets are regional segments within your VPC, each with a defined IP address range used by resources within that region. Navigate to VPC network ➡️ VPC networks in the Google Cloud Console.Click "CREATE VPC NETWORK".Name: Enter "dzone-custom-vpc".Subnet creation mode: Select "Custom".Under New subnet: Name: Enter "dzone-custom-subnet".Region: Select "us-central1".IPv4 range: Enter "10.0.101.0/24".Private Google Access: Select On (This allows instances without external IPs to reach Google APIs and services).Flow logs: Keep this Off.Hybrid Subnets: Keep this Off.Click "DONE" to finish defining the subnet.Leave the Firewall rules, Dynamic routing mode, and Maximum transmission unit (MTU) settings at their defaults.Click "CREATE". Organizational policies enforce constraints across your Google Cloud resources. We need to adjust two policies for our workstation setup. Allow Non-Shielded VMs: By default, some organizations might require Shielded VMs for enhanced security. For flexibility with Workstation configurations, we'll disable this requirement temporarily or within the scope needed. Navigate to IAM & Admin ➡️ Organization Policies in the Google Cloud Console.In the filter bar, search for constraints/compute.requireShieldedVm. Click on the policy named Requires Shielded VM.Click MANAGE POLICY.Select Customize.Under Applies to, choose Customize.For Enforcement, select Off.Click SAVE. Confirm any prompts about the change. Allow External IP Access for VMs: This policy controls whether VM instances can be assigned external IP addresses. Navigate back to IAM & Admin ➡️ Organization Policies.Search for "constraints/compute.vmExternalIpAccess". Click on the policy named "Define allowed external IPs for VM instances".Click "MANAGE POLICY".Select "Customize".Under Applies to, choose "Customize".For Policy values, select "Allow all".For Enforcement, select "Replace". (Note: Depending on your organization's setup, you might choose 'Merge with parent' if other rules exist).Click "SAVE". Ensure your user account has the appropriate permissions. For this example, we'll grant the BigQuery Data Owner role (adjust roles based on your specific development needs). Navigate to IAM & Admin ➡️ "IAM".Click "GRANT ACCESS".In the New principals field, enter the email address of your Google Cloud user account.In the Select a role field, search for and select "BigQuery Data Owner".Click "SAVE". Creating the Google Cloud Workstation Setting up a Cloud Workstation involves three key components: Workstation Cluster: A regional resource that groups and manages your workstations, connecting them to your VPC.Workstation Configuration: A template defining the specifications for workstations created within that cluster (e.g., machine type, disk, environment image).Cloud Workstation: The actual virtual Google Cloud development environment instance used by a developer. Initiate the creation of a new cluster by clicking on CREATE WORKSTATION CLUSTER. Assign a name to the cluster, for example, "dzone-cloud-workstations-cluster" and expand the Network Settings. Select your pre-existing custom VPC (in our example it's "dzone-custom-vpc") and the desired subnetwork (in our example it's "dzone-custom-subnet"). Choose "Public Gateway" as the Gateway type to allow outbound internet access from the workstations. Click "CREATE". Cluster provisioning may take up to 20 minutes. Wait until the cluster status shows as "Ready" before proceeding. Workstation Configurations are templates that allow platform teams to define the exact specifications of a developer workstation. These templates define the VM type, storage, container images for development environments and IDEs, and access is managed through IAM. Go to Cloud Workstations ➡️ Workstation configurations and click "CREATE". Enter a name for the configuration, such as "dzone-cloud-workstations-configuration". Select the cluster created in the previous step, "dzone-cloud-workstations-cluster". Proceed to Machine Settings: To enable GPU support, click "GPUs", select "NVIDIA T4" or any other GPU Type you want to use and ensure the corresponding driver version is selected.Choose a suitable machine type (e.g., n1-standard-1).Specify the desired availability zones within the cluster's region (e.g., us-central1-a and us-central1-c).Configure cost-saving options: set Auto-sleep (e.g., After 30 minutes of inactivity) and Auto-shutdown (e.g., After 6 hours) In the Advanced options, ensure Disable SSH access is unchecked if you require direct SSH connectivity. Configure the Environment settings: Under Code editors on base images, select an editor like "Base Editor" (Code OSS for Cloud Workstations).Choose the Compute Engine default service account (or a custom one with appropriate permissions) for GCP interactions.In the Persistent disk settings, opt to "Create a new empty persistent disk". Select a Disk type (e.g., Balanced) and specify the desired Disk size (e.g., 200GB). Optionally, configure Users and permissions to grant specific users or groups the ability to create workstations using this configuration. Click "CREATE". Wait for the configuration status to become "Ready". Cloud Workstations are fully managed development environments in the cloud. They provide secure, pre-configured workspaces that developers can access anytime, anywhere, to build and deploy applications without worrying about any local setup or maintenance. Go to to Cloud Workstations ➡️ Workstations and Click on "CREATE WORKSTATION". Provide a name for your workstation (e.g., "dzone-cloud-workstation"). Select the configuration template created earlier ("dzone-cloud-workstations-configuration"). Click "CREATE". Once the workstation is provisioned, click "START" to boot it up. After it starts, click "LAUNCH" to open the Code OSS web-based IDE in your browser. Installing the NVIDIA CUDA Toolkit To utilize the GPU, install the CUDA toolkit within the workstation environment. Inside the launched workstation's Code OSS interface, open a new terminal (Menu ➡️ Terminal ➡️ New Terminal). Verify the base Linux distribution version by running: lsb_release -a. Notice the Ubuntu version is 24.04. To obtain the appropriate installation commands for the desired CUDA toolkit version (e.g., 12.8) compatible with your workstation's OS from the official NVIDIA CUDA Toolkit documentation. For Ubuntu 24.04 and CUDA 12.8, the commands typically resemble the following: Shell wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb sudo dpkg -i cuda-keyring_1.1-1_all.deb sudo apt-get update sudo apt-get -y install cuda-toolkit-12-8 sudo apt-get install -y nvidia-open After installation, verify the GPU and driver are recognized by running the command: nvidia-smi Notice that the CUDA toolkit is available for the Cloud Workstations. If other users need access to this specific workstation instance, you can add them in the Google Cloud Console. Click "ADD USERS". Enter the email addresses of the users and assign them the workstations user "IAM" role for that specific workstation. Click "SAVE". Setting up Python and Connecting to BigQuery Install Python Extension: In the workstation's Code OSS editor, navigate to the Extensions view (usually on the left sidebar) and search for and install the official Python extension. Create Python Virtual Environment: In the workstation terminal: Check your Python 3 version: python3 -V (e.g., 3.12.3)Install the venv package if missing (adjust version number if needed): sudo apt update && sudo apt install python3.12-venvCreate a virtual environment: python3 -m venv .venvActivate the environment: source .venv/bin/activateInstall BigQuery Client Library: While the virtual environment is active, install the library: pip install google-cloud-bigqueryUsing the Code OSS editor, create a new file (File ➡️ New File),Navigate to File ➡️ New File in the Cloud Workstation. Select Python as the language. Paste the code below to the Python File Created: Python from google.cloud import bigquery def query_bigquery_from_workstation(query): """ Runs a BigQuery query from a Google Cloud Workstation. Args: query: The SQL query to execute. Returns: A list of rows from the query result, or None if an error occurs. """ try: # On a Google Cloud Workstation # should work automatically. client = bigquery.Client() query_job = client.query(query) results = query_job.result() return list(results) except Exception as e: print(f"An error occurred: {e}") return None # Sample Query query = """ SELECT COUNT(*) as total_rows FROM `bigquery-public-data.geo_us_boundaries.zip_codes` """ results = query_bigquery_from_workstation(query) if results: for row in results: print(f"Total rows in zip_codes tables: {row.total_rows}") Save the file as "bq.py". If successful, the script will authenticate using the ADC you just set up and print the total number of rows found in the public zip codes table. To allow your script to access Google Cloud services, authenticate using Application Default Credentials (ADC). Run the command : gcloud auth application-default login Open the provided URL in a browser, authenticate with your Google account that has permissions for BigQuery, and paste the verification code back into the terminal. Execute your Python script. python3 bq.py If successful, the script will authenticate using the ADC you just set up and print the total number of rows found in the public zip codes table in BigQuery. Summary Google Cloud Workstations offer a powerful solution for developers by providing managed, secure, and customizable cloud-based development environments. This approach standardizes setups across teams and removes the burden of local machine configuration. This guide detailed the practical steps for deploying a high-performance Cloud Workstation equipped with GPU capabilities (NVIDIA T4) within a specific Google Cloud network (custom VPC and subnet). It walked you through creating the necessary workstation cluster and configuration template, launching the Google Cloud workstation instance, installing the NVIDIA CUDA toolkit for GPU acceleration, and setting up a Python environment to interact with Google BigQuery. By authenticating using Application Default Credentials, the workstation successfully queried BigQuery datasets, demonstrating a complete workflow suitable for developers or data scientists needing robust, cloud-based computational resources for demanding tasks.

Introduction Cloud providers offer baseline landing zone frameworks, but successful implementation requires strategic customization tailored to an organization’s specific security, compliance, operations, and cost-management needs. Treating a landing zone as a turnkey solution can lead to security gaps and operational inefficiencies. Instead, enterprises should carefully design and continuously refine their landing zones to build a secure, scalable, and efficient foundation for cloud adoption. Planning Factors for Enterprise Cloud Landing Zone When designing a cloud landing zone, organizations must carefully evaluate the following key factors to establish a robust and efficient foundation before deploying business applications to the new cloud platform: Organizational Structure A landing zone must establish a cloud organizational structure tailored to the needs of departments and business units, environment segmentation, data security requirements, operational demands, compliance mandates, and application access patterns. This structure should be designed to ensure that applications hosted within it can be effectively governed by applying common policies and guardrails to organizational units. Compliance Standards Enterprises must conduct a thorough assessment of the compliance standards relevant to their business domain, such as HIPAA, HITRUST, NIST, PCI DSS, ISO, and GDPR. Based on this assessment, they should implement appropriate security guardrails, monitoring, and observability controls within the cloud environment. Additionally, mechanisms must be in place to demonstrate that compensating controls are applied in alignment with compliance requirements, ensuring auditors' expectations are met. Enterprise System Integration The landing zone must facilitate seamless integration between applications across multi-cloud and on-premises environments. Achieving this requires careful planning of data flows, network connectivity, integration methods, ETL processes, and identity federation to support both current operations and future migration strategies. Network Architecture A robust network design is the backbone of a landing zone. When building it, factors such as internet access management, DNS resolution, IP address allocation, private networking, and cross-network connectivity must be carefully considered. The Transit Gateway model is often preferred by mid-sized and large enterprises for its benefits in centralized network management and simplified connectivity across multi-cloud and hybrid environments. Additionally, the network architecture must address routing strategies, access controls, and secure access patterns for diverse workload types. Security Framework The security strategy should include Cloud Security Posture Management (CSPM), Security Information and Event Management (SIEM), and comprehensive IAM policies that follow the least privilege model. Organizations often benefit from integrating existing enterprise security tools with their cloud environments to maintain centralized visibility and governance. At the same time, cloud-native services can be leveraged when enterprise tools are unavailable. Financial Operations A robust FinOps framework is essential for an enterprise landing zone, as it promotes transparent cost allocation, accurate budgeting, enhanced visibility into cloud expenditures, and effective resource optimization. Critical components of this approach include establishing a detailed financial tagging strategy, leveraging cloud-native tools to automate cost optimization, and implementing alert systems to proactively track and manage spending across multiple cloud environments. On-Premises Connectivity As many enterprises operate hybrid environments due to compliance, licensing, cost, and latency considerations, the landing zone must ensure reliable connectivity between cloud and on-premises environments. A combination of connectivity options such as dedicated private connections, VPNs, transit gateways, and API integrations should be considered to establish seamless connectivity, with planning tailored to the organization’s specific requirements for latency, security, and reliability in on-premises connectivity. Implementation Blueprint for Enterprise Cloud Landing Zone A well-designed cloud landing zone requires careful attention to several critical implementation aspects: Shift-Left Security Implement proactive guardrails to prevent non-compliant resource deployments, embedding security and compliance early in the development lifecycle. Prioritize preventive controls over reactive ones to strengthen the security posture through continuous validation and enforcement. Automation and Infrastructure as Code Automation paired with Infrastructure as Code (IaC) plays a pivotal role in enabling uniform, standardized deployments and facilitating rapid scalability. Adopting this methodology simplifies change management processes, supports thorough automated testing, expedites cloud infrastructure provisioning, and ensures dependable disaster recovery procedures. Identity and Access Management Adhere to the principle of least privilege when defining roles and assigning permissions to users and systems. Leverage Role-Based Access Control (RBAC), Zero Trust methodologies, Single Sign-On (SSO), and just-in-time access mechanisms to securely manage access within the cloud environment. Utilize break-glass accounts exclusively in emergency scenarios, ensuring tight controls and comprehensive auditing. CI/CD Management Create dedicated CI/CD accounts that maintain restricted access to DevSecOps teams while implementing token-based temporary access mechanisms for enhanced security. Establish least-privilege service roles and hardened deployment pipelines to ensure well-managed and secure application deployments across environments. Guardrail Testing and Deployment Strategy Establish comprehensive guardrail testing and deployment practices using dedicated testing environments integrated within automated pipelines. Ensure environment-specific policies are thoroughly implemented and validated via continuous testing and proactive monitoring before deployment to production environments. Exception Management Create a controlled environment specifically designed to host workloads that do not align with the standard organizational structure. Although such exceptions should remain temporary, establish a clear, governed process for managing them, including well-defined migration timelines and robust governance controls. Environment Strategy and Organizational Structure Design a comprehensive cloud environment hierarchy that supports both operational needs and security requirements through distinct organizational units. Structure the landing zone with specialized environments: Production Environments Establish stringent controls, such as restricted console access, enforced change management processes, and detailed audit logging. Apply rigorous security guardrails and continuous compliance monitoring to safeguard business-critical workloads effectively. Development and Testing Environments Establish distinct organizational units for Development, UAT, and Non-Production environments, each equipped with tailored guardrails. Implement cost-effective controls, including restrictions on compute instance sizes, enforced resource shutdown schedules, and flexible deployment practices, all while maintaining robust security standards. Sandbox Environment Establish dedicated spaces for experimentation, proof-of-concepts, and training initiatives. Implement strict cost controls, resource limitations, and isolation while providing enough flexibility for innovation. These environments should be time-bound with predefined budgets and clear objectives and isolated from other environments. Suspended Environment Management Maintain a secure organizational unit for deactivated accounts with complete access restrictions. Implement defined cool-off periods and proper decommissioning procedures to ensure proper resource cleanup while preserving necessary historical data. Shared Services Organization Centralize common infrastructure components in a dedicated organizational unit, including security and compliance tools, network services, identity management, monitoring and observability solutions, cost management systems, backup and disaster recovery capabilities, and image pipelines. This centralized approach ensures standardized operations, efficient resource utilization, and consistent service delivery across the organization while reducing operational overhead and maintaining security controls. Conclusion A well-architected cloud landing zone provides a dynamic foundation essential for an organization's cloud journey. Rather than a static, one-time technical deployment, it should serve as a living framework, continuously adapting to evolving business needs. Organizations adopting a comprehensive and tailored approach to landing zone design position themselves for scalable and successful cloud adoption. While cloud providers offer foundational blueprints, true value emerges through thoughtful customization and ongoing refinement aligned closely with an enterprise’s unique objectives.

Introduction Running machine learning (ML) workloads in the cloud can become prohibitively expensive when teams overlook resource orchestration. Large-scale data ingestion, GPU-based inference, and ephemeral tasks often rack up unexpected fees. This article offers a detailed look at advanced strategies for cost management, including: Dynamic Extract, Transfer, Load (ETL) schedules using SQL triggers and partitioningTime-series modeling—Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet—with hyperparameter tuningGPU provisioning with NVIDIA DCGM and multi-instance GPU configurationsIn-depth autoscaling examples for AI services Our team reduced expenses by 48% while maintaining performance for large ML pipelines. This guide outlines our process in code. Advanced ETL Management With SQL Partitioning and Triggers Partitioned ETL Logs for Cost Insights A typical ETL costs table might suffer from slow queries if it grows unbounded. We partitioned usage logs by month to accelerate cost analysis. SQL CREATE TABLE etl_usage_logs ( pipeline_id VARCHAR(50) NOT NULL, execution_time INTERVAL, cost NUMERIC(10, 2), execution_date TIMESTAMP NOT NULL ) PARTITION BY RANGE (execution_date); CREATE TABLE IF NOT EXISTS etl_usage_jan2025 PARTITION OF etl_usage_logs FOR VALUES FROM ('2025-01-01') TO ('2025-02-01'); This setup ensures queries run efficiently, even when the dataset is massive. Dynamic ETL Trigger Based on Data Volumes Instead of blindly scheduling hourly ETL runs we used an event-based trigger that checks data volume thresholds. SQL CREATE OR REPLACE FUNCTION run_etl_if_threshold() RETURNS TRIGGER AS $$ BEGIN IF (SELECT COUNT(*) FROM staging_table) > 100000 THEN PERFORM run_etl_pipeline('staging_table'); END IF; RETURN NEW; END; $$ LANGUAGE plpgsql; CREATE TRIGGER etl_threshold_trigger AFTER INSERT ON staging_table FOR EACH STATEMENT EXECUTE FUNCTION run_etl_if_threshold(); This logic only executes the ETL when the staging_table surpasses 100,000 rows. We observed a 44% reduction in ETL costs. Advanced Time-Series Cost Forecasting SARIMA With Custom Seasonality Our cost dataset exhibited weekly and monthly seasonality. Basic SARIMA might overlook multiple cycles, so we tuned multiple seasonal orders. Python import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.statespace.sarimax import SARIMAX #Assuming this is where store the cloud costs data cost_data = pd.read_csv('cloud_costs.csv', parse_dates=['date'], index_col='date') cost_data['diff_cost'] = cost_data['cost'].diff(1).dropna() model = SARIMAX( cost_data['diff_cost'].dropna(), order=(1,1,1), seasonal_order=(0,1,1,7), trend='n' ) results = model.fit(disp=False) forecast_steps = 30 prediction = results.get_forecast(steps=forecast_steps) forecast_ci = prediction.conf_int() Our advanced SARIMA approach detected weekly cost spikes (typically Monday usage surges). We used differencing for daily fluctuations and a separate seasonal term for weekly patterns. Enhanced Prophet With Capacity and Holidays Prophet can incorporate capacity constraints (upper and lower bounds) and custom holiday events. Python from fbprophet import Prophet prophet_df = cost_data.reset_index()[['date', 'cost']] prophet_df.columns = ['ds', 'y'] holidays = pd.DataFrame({ 'holiday': 'cloud_discount', 'ds': pd.to_datetime(['2025-02-15', '2025-11-25']), 'lower_window': 0, 'upper_window': 1 }) m = Prophet( growth='logistic', holidays=holidays, ) prophet_df['cap'] = 30000 prophet_df['floor'] = 0 m.fit(prophet_df) future = m.make_future_dataframe(periods=60) future['cap'] = 30000 future['floor'] = 0 forecast = m.predict(future) We recognized that after a certain point, departmental budgets capped daily spending. By modeling these constraints, Prophet produced more realistic forecasts. GPU Provisioning With NVIDIA DCGM and MIG GPU Monitoring We used NVIDIA DCGM for comprehensive monitoring of GPU memory, temperature, and SM usage. This script logs DCGM metrics to a time-series database (e.g InfluxDB): Shell while true; do dcgmi diag -r 3 | grep "SM Active" >> /var/log/gpu_sm_usage.log dcgmi diag -r 3 | grep "Memory" >> /var/log/gpu_mem_usage.log sleep 60 done Multi-Instance GPU (MIG) On NVIDIA A100 or newer GPUs, MIG enables partitioning the GPU into multiple logical instances. This approach helps allocate just enough GPU resources per inference job. Shell nvidia-smi mig -i 0 -cgi 19g.2g nvidia-smi mig -i 0 -cci We reconfigured our inference service so that smaller models run on smaller MIG partitions. This yielded a 30% cost reduction versus running multiple large GPU instances. Autoscaling AI Services Kubernetes Custom Metrics We often need more than CPU usage to scale ML microservices. Using a Kubernetes Metrics Server or Prometheus, we can scale on custom GPU or memory metrics. YAML apiVersion: autoscaling.k8s.io/v2beta2 kind: HorizontalPodAutoscaler metadata: name: ai-inference-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: ai-inference minReplicas: 2 maxReplicas: 20 metrics: - type: Pods pods: metric: name: "gpu_util" target: type: AverageValue averageValue: "60" This autoscaler scales up if average GPU utilization across pods exceeds 60. Spot Instances for Non-Critical Tasks For cost-conscious experiments, we scheduled some model training on AWS Spot Instances or Google Cloud Preemptible VMs: Shell aws ec2 request-spot-instances --spot-price "0.20" --instance-count 2 --type persistent --launch-specification file://spot_specification.json Loss of these instances won’t disrupt mission-critical inference, but saves significant budget for training or batch tasks. Real-World Results After implementing advanced partitioning, time-series forecasting, GPU partitioning, and custom autoscaling, our monthly cloud bill dropped by 48%. This was while supporting 10 million daily inferences and daily ETL on a 5 TB dataset. SQL Partitioning and Triggers Achieved a 44% reduction in ETL costsRemoved idle runs by automating table partitionsSARIMA + Prophet Decreased overall cloud spending by 48% Forecasted usage spikes to preempt over-allocationMIG GPU Partitioning Lowered GPU expenses by 30%Allocated smaller GPU slices for less intensive models Spot/Preemptible VMs Reduced training overhead by 60%Used transient instances where interruptions posed minimal risk Conclusion Controlling cloud spending for ML workloads isn’t just about imposing surface-level cost caps. By integrating predictive analytics, database partitioning, targeted GPU provisioning, and well-chosen VM types, teams can capture substantial savings without downgrading model performance. Our experience demonstrates that a methodical blend of SQL triggers, time-series forecasting, and flexible GPU resources delivers measurable financial benefits. If your ML budget seems unwieldy, these techniques can help you tighten spending while still meeting ambitious performance goals.

As enterprises accelerate AI adoption, their cloud strategy determines whether they can efficiently train models, scale workloads, and ensure compliance. Given the computational intensity and data sensitivity of AI, businesses must choose between hybrid cloud and multi-cloud architectures. While both hybrid cloud and multi-cloud approaches offer distinct advantages, understanding their nuances is crucial for organizations aiming to build robust AI infrastructure. This article explores the key differences between these strategies and provides practical guidance for enterprises preparing for AI adoption. Understanding Modern AI Infrastructure Requirements AI infrastructure has evolved significantly, demanding advanced computing power, data management, and networking capabilities. Organizations must consider these key elements to ensure AI readiness: High-Performance Computing (HPC) AI workloads, especially deep learning models, require substantial computational power. Enterprises need access to specialized hardware accelerators like GPUs, TPUs, and FPGAs to train AI models efficiently. Hybrid and multi-cloud solutions allow businesses to scale their computing resources dynamically based on AI workload demands. Data Storage and Management AI models require massive amounts of structured and unstructured data. Enterprises need to implement scalable storage solutions, such as object storage and distributed databases, to manage large datasets efficiently. Data localization and compliance requirements further influence whether businesses choose on-prem, hybrid, or multi-cloud storage. Low-Latency Networking Real-time AI applications, such as autonomous systems and financial trading models, rely on ultra-low-latency networking to process data instantly. In AI model training, fast data transfers between cloud environments reduce bottlenecks and enhance iterative learning. Technologies like edge computing, software-defined networking (SDN), and digital interconnection enhance data transmission speeds and security. Digital interconnection reduces latency by enabling direct, high-speed connections between enterprises, cloud providers, and AI workloads, bypassing the public internet. Services like private cloud exchanges and direct interconnection platforms optimize AI data processing across environments, making digital interconnection essential for hybrid and multi-cloud strategies. Security and Compliance AI infrastructure must adhere to stringent security protocols, ensuring data privacy, encryption, and regulatory compliance. Industries like finance and healthcare must balance AI innovation with adherence to GDPR, HIPAA, and other legal frameworks, influencing their choice of cloud strategy. Scalability and Cost Efficiency AI projects evolve rapidly, requiring flexible infrastructure that scales on demand. Enterprises must evaluate pay-as-you-go cloud models versus on-prem investments to optimize costs. Multi-cloud strategies enable cost optimization by selecting the most competitive AI services across cloud providers. Hybrid Cloud vs. Multi-Cloud at a Glance Security and compliance: Hybrid cloud offers better control over sensitive AI data, while multi-cloud requires additional security policies across providers. Performance: Hybrid cloud minimizes latency for mission-critical workloads, while multi-cloud depends on provider-specific optimizations. Scalability: Multi-cloud scales more flexibly across cloud vendors, whereas the hybrid cloud is constrained by on-prem resources. AI tools: Multi-cloud enables access to a diverse set of AI tools, while hybrid cloud may require custom AI infrastructure. Choosing the Right Cloud Strategy for AI Workloads Hybrid Cloud for AI A hybrid cloud approach is often preferred by enterprises that handle large-scale AI workloads with stringent security and compliance requirements. Advantages Data sovereignty and compliance: Keeps sensitive AI data on-premises while leveraging the cloud for AI model training. Latency optimization: Reduces data transfer times by keeping critical workloads closer to users. Cost control: Balances on-prem and cloud resources to optimize costs. Custom AI infrastructure: Allows enterprises to integrate custom AI hardware like GPUs, TPUs, and FPGAs on-premises. Challenges Complex integration between private and public cloud components. Requires significant investment in infrastructure and management tools. Multi-Cloud for AI A multi-cloud approach benefits enterprises that prioritize flexibility, scalability, and access to diverse AI tools from multiple cloud providers. Advantages Avoids vendor lock-in: Enterprises can select AI services from different cloud vendors. High availability and redundancy: AI workloads can fail over between clouds. Cost optimization: Enables pricing comparisons and workload distribution to reduce costs. Best-of-breed AI tools: Provides access to unique AI services (e.g., Google Cloud’s TensorFlow AI tools, AWS SageMaker, and Azure ML). Challenges Managing interoperability between cloud providers can be complex. Security and compliance consistency across multiple platforms is a challenge. FactorHybrid Cloud for AIMulti-Cloud for AI Security and Compliance High – Retains sensitive data on-premises Medium – Requires strong multi-cloud security policies Performance Low latency for mission-critical workloads Varies – Dependent on cloud provider infrastructure Scalability Limited by on-premises infrastructure High – Can leverage multiple cloud providers Cost Control More predictable with CAPEX investment OPEX-based, flexible pricing but potentially higher long-term costs Flexibility Moderate – Tied to on-prem resources High – Ability to switch providers based on needs AI-Ready Services Requires custom AI stack Access to diverse AI platforms & tools Real-World Industry Trends and Future AI-Cloud Strategies As AI workloads evolve, enterprises are increasingly moving towards a hybrid multi-cloud model, combining the security of hybrid cloud with the flexibility of multi-cloud AI services. Key Emerging Trends Confidential computing: AI model training on multi-cloud while keeping sensitive data encrypted (e.g., Google’s confidential VMs). Hybrid multi-cloud convergence: Enterprises using hybrid cloud for regulated data and multi-cloud for AI processing (e.g., financial services firms balancing security and scalability). Edge AI and 5G integration: AI inference happening closer to end-users with hybrid cloud edge nodes (e.g., autonomous vehicle manufacturers deploying AI at the edge). Case Studies Commerzbank: Aims to run 85% of its decentralized applications in the cloud by 2024, utilizing a hybrid multi-cloud approach. IBM: Uses hybrid cloud infrastructure to support large-scale AI model training, ensuring scalability and flexibility. Conclusion The choice between hybrid cloud and multi-cloud strategies for AI readiness depends on various factors specific to each organization. A hybrid approach may be more suitable for organizations with significant existing infrastructure and strict data governance requirements. In contrast, a multi-cloud strategy might better serve organizations looking to leverage best-of-breed AI services and maintain maximum flexibility. Some enterprises opt for a hybrid multi-cloud model, combining the security of hybrid cloud with the flexibility of multi-cloud AI services. This approach allows organizations to maintain strict governance while leveraging best-of-breed AI tools across providers. What’s Next? Organizations should evaluate their current IT infrastructure, regulatory constraints, and AI workload requirements before committing to a specific cloud strategy. Investing in cloud-native AI solutions, edge computing, and high-speed interconnection can further enhance AI-readiness in today’s digital landscape. Thank you for reading! Feel free to connect with me on LinkedIn.

Azure Synapse Analytics is a strong tool for processing large amounts of data. It does have some scaling challenges that can slow things down as your data grows. There are also a few built-in restrictions that could limit what you’re able to do and affect both performance and overall functionality. So, while Synapse is powerful, it’s important to be aware of these potential roadblocks as you plan your projects. Data Distribution and Skew Data skew remains a significant performance bottleneck in Synapse Analytics. Poor distribution key selection can lead to: 80-90% of data concentrated on 10% of nodesHotspots during query executionExcessive data movement via TempDB You can check for data skew by checking how rows are distributed across the distribution_id (which typically maps 1:1 to compute nodes at maximum scale). SQL SELECT distribution_id, COUNT(*) AS row_count FROM [table_name] GROUP BY distribution_id ORDER BY row_count DESC; If you see that a few (distribution_id)s have a much higher (row_count) than others, this indicates skew. To mitigate this: Use high-cardinality columns for even distributionMonitor skew using DBCC PDW_SHOWSPACEUSEDRedistribute tables with CREATE TABLE AS SELECT (CTAS) Resource Management and Scaling 1. SQL Pools You do not have any control over the built-in pool configuration. For a dedicated pool, the defaults are: Maximum DWU: Gen1: DW6000, Gen2: DW30000cScaling requires manual intervention using SQL commands To manually scale your dedicated SQL pool, you use the following ALTER DATABASE command. Here’s how you do it: SQL ALTER DATABASE [your_database] MODIFY (SERVICE_OBJECTIVE = 'DW1000c'); When you scale a Synapse pool, it goes into “Scaling” mode for a little while, and once it’s done, it switches back to “Online” and is ready to use. Key Points Scaling is not automatic, so you must run the command yourself.The SQL pool must be online to scale.You can also scale using PowerShell or the Azure portal, but the SQL command is a direct way to do it. 2. Apache Spark Pools Scale-up triggers if resource utilization exceeds capacity for 1 minute.Scale-down requires 2 minutes of underutilization. 3. Integration Runtimes Manual scaling through the Azure portal and not from the Synapse workspace. 4. Concurrency Limits Maximum 128 concurrent queries; any further excess queries are queued.Concurrent open sessions: 1024 for DWU1000c and higher, 512 for DWU500c and lower. Query and Data Limitations 1. SQL Feature Gaps No support for triggers, cross-database queries, or geospatial data typesLimited use of expressions like GETDATE() or SUSER_SNAME()No FOR XML/FOR JSON clauses or cursor support 2. Data Size Restrictions Source table row size limited to 7,500 bytes for Azure Synapse Link for SQLLOB data > 1 MB not supported in initial snapshots for certain data types 3. Query Constraints Maximum 4,096 columns per row in SELECT resultsUp to 32 nested subqueries in a SELECT statementJOIN limited to 1,024 columns 4. View Limitations Maximum of 1023 columns in a view. If you have more columns, view restructuring is needed. SQL Error: CREATE TABLE failed because column 'VolumeLable' in table 'QTable' exceeds the maximum of 1024 columns. To get around this, you’ll just need to break your view up into a few smaller ones, each with fewer than 1,024 columns. For example: SQL -- First view with columns 1 to 1023 CREATE VIEW dbo.BigTable_Part1 AS SELECT col1, col2, ..., col1023 FROM dbo.BigTable; -- Second view with the remaining columns CREATE VIEW dbo.BigTable_Part2 AS SELECT col1024, col1025, ..., col1100 FROM dbo.BigTable; SQL -- Combine views SELECT * FROM dbo.BigTable_Part1 p1 JOIN dbo.BigTable_Part2 p2 ON p1.PrimaryKey = p2.PrimaryKey; Limited Data Format Support ORC and Avro formats are not supported which are common file formats in Enterprise data. Moving to parquet or deltalake format is recommended. Integrated with the very old version of Deltalake, which does not support critical features like column-mapping, column renaming, etc. Azure Synapse Spark Pool showing Delta Lake version Access Limitations When you try to set up Azure Synapse Link for SQL, you might run into an error if the database owner isn’t linked to a valid login. Basically, the system needs the database owner to be tied to a real user account to work properly. If it’s not, Synapse Link can’t connect and throws an error. Workaround To fix this, just make sure the database owner is set to a real user that actually has a login. You can do this with a quick command: SQL sqlALTER AUTHORIZATION ON DATABASE::[YourDatabaseName] TO [ValidLogin]; Replace (YourDatabaseName) with your actual database name and (ValidLogin) with the name of a valid server-level login or user. This command changes the ownership of the database to the specified login, ensuring that the database owner is properly mapped and authenticated. Performance Optimization Challenges 1. Indexing Issues Clustered Columnstore Index (CCI) degradation due to frequent updates or low memoryOutdated statistics leading to suboptimal query plans 2. TempDB Pressure Data movement from skew or incompatible joins can quickly fill TempDBMaximum TempDB size: 399 GB per DW100c 3. IDENTITY Column Behavior Distributed across 60 shards, leading to non-sequential values Backup and Recovery Limitations No offline .BAK or .BACPAC backups with dataLimited to 7-day retention or creating database copies (incurring costs) Conclusion Azure Synapse Analytics is a powerful tool for handling big data, but it’s not without its quirks. You’ll run into some scaling headaches and built-in limits that can slow things down or make certain tasks tricky. To get the best performance, you’ve got to be smart about how you distribute your data, manage resources, and optimize your queries. Keeping an eye on things and tuning regularly helps avoid bottlenecks and keeps everything running smoothly. Basically, it’s great — but you’ll need to work around some bumps along the way to make it really shine.

In logistics, orchestration involves more than connecting systems; it means managing moving parts, legal boundaries, and operational failures in real time. This discipline involves managing transportation logistics, documentation requirements, and timing responsibilities to handle exceptions between geographical locations, vendor relationships, and regulatory areas. In such an environment, APIs act as operational and compliance-critical interfaces, shaping the flow of legal documentation and orchestrating logistics. Online platforms frequently discover that the APIs they developed work correctly for ideal "happy path" scenarios; however, such scenarios rarely appear in real-world operations. Logistics never functions this way. Your API must be strong and meticulously understand logistical challenges when handling customs delays, truck breakdowns, weight discrepancies, and regulatory document failures. Logistics Orchestration Is Not Just Another Data Flow Logistics APIs must do more than pass structured JSON or XML between systems. They often operate in volatile, compliance-sensitive domains like Dangerous Goods Declarations (DGD), tariff classification, carrier SLAs, and proof-of-delivery workflows. In one large global retail distribution firm, there were API calls to third-party freight forwarders with custom documentation to be embedded at the end of the API call, dependent on the destination country. This wasn’t just metadata; this was compliance content that would be subject to customs inspections. If this failed mid-journey, it was something more than a technical error. It was a regulatory one. It instructed the team that logistics orchestration is more than a supply chain visibility problem. It’s a legal interface. This means that APIs should, in turn, reflect this weight in their design from the schema to the logging strategy. Designing for Compliance Isn’t Optional In many organizations, logistics platforms evolve into systems of record due to audit and traceability requirements. An API you call from a warehouse in Texas may need to serve a subpoena two years from now. In other words, auditability and data retention should be treated as core functions of every orchestration API. For example, if you deal with European supply chain partners, your API logs might be covered by the GDPR. It includes handling the shipper information consent status and redacting fields from trace logs when sent to a vendor. Flexport’s public APIs allow integration with customs documentation workflows and shipment tracking across global regions, supporting operational transparency. But that’s only one aspect. APIs also need to reflect the regulatory state. This may involve embedding timestamps in customs clearance responses or retaining shipping manifest records in WORM (Write Once, Read Many) storage for international transport authorities. Exceptions Aren’t Anomalies: They’re the Norm Error handling in a generic fallback is one of the most common mistakes in logistics APIs. This fails quickly in real deployments, and exceptions are the primary workflows used in practice. In a multiregion 3PL provider, the API contract was too rigid to deal with carrier no-shows. The delivery confirmation schema did not support this event, as it was common during regional port congestion. Teams adapted their applications but had to do so outside the API. The result was eroded traceability, with bypasses later failing their SLA promise. After some time, the platform recreated the schema with a carrier exception model so that they could record structured reasons such as “no show,” “equipment fault,” and “incomplete pickup” directly in the orchestration pipeline. This made the exception queryable and auditable and tied it to compensation logic downstream. Any logistics API that cannot structurally accommodate exceptions becomes an operational liability. If the 'on-time delivery' path is your only success path, you will quickly discover that you have created a fantasy, not a system. Edge Case Architecture: State Machines and Event Stores Despite microservices and event-driven architectures becoming popular, logistics has some inherent complexity: we must deal with the happenings (events) and the order in which things happen (sequence). One shipping API provider realized that delivery locations shift mid-shipment when creating container orchestration tools to support a primary maritime shipping provider. Under these edge cases, their old REST APIs broke. However, they modeled the flow using event sourcing. This gave them a sequence history of all changes, not just what was left. Event stores enabled them to provide API consumers who might have to ‘replay’ state history (for audit, billing disputes, etc.) using event store queries. It also made them resilient to partial failures or message duplication across systems. Another common approach is to use state machines to represent the change of the shipment status, such as Created → In-Transit → Delayed → Re-Routed → Delivered. These machines ensure valid transitions and print error messages when the operation does not adhere to sequencing logic. You Don’t Just Build APIs, You Build Contracts with the Real World Orchestration APIs in logistics aren’t just surface-level tools; they’re the visible interface of a complex chain of real-world responsibilities. A 500 error doesn't mean a contract has been breached. We know it happened because a truck never came, a customs form didn’t pass, or a customer never received the goods, sometimes on time. That’s why legal resilience design, exception visibility, and conformity to compliance are not “premium features,” but essential. They are pivotal to system correctness. Moreover, they remove all manual effort associated with each jurisdiction, carrier exception, and route permutation, so you don’t have to replicate everything when the platform scales. Your API becomes the front line in increasingly digital-first supply chains. Conclusion You can’t orchestrate logistics with a naïve API. You can’t afford a system that only models when things go right. When APIs are built for unsightly paths, such as missed pickups, customs delays, route changes, and compliance records, they don’t just handle exceptions; they drive operational trust. Real-world logistics requires orchestration platforms that are aware of regulatory nuance, treat exceptions as signals, and build feedback into their design. APIs that do that are not only good software; they are strategic infrastructure.

The cloud revolution has transformed how businesses deploy, scale, and manage data streaming solutions. While Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS) cloud models are often used interchangeably in marketing, their distinctions have significant implications for operational efficiency, cost, and scalability. In the context of data streaming around Apache Kafka and Flink, understanding these differences and recognizing common misconceptions—such as the overuse of the term “serverless”—can help you make an informed decision. Additionally, the emergence of Bring Your Own Cloud (BYOC) offers yet another option, providing organizations with enhanced control and flexibility in their cloud environments. The Data Streaming Landscape: Kafka, Flink, Cloud, and More The Data Streaming Landscape 2025 highlights how data streaming has evolved into a key software category, moving from niche adoption to a fundamental part of modern data architecture. With frameworks like Apache Kafka and Flink at its core, the landscape now spans self-managed, BYOC, and fully managed SaaS solutions, driving real-time use cases, unifying transactional and analytical workloads, and enabling innovation across industries. If you’re still grappling with the fundamentals of stream processing, this article is a must-read: "Stateless vs. Stateful Stream Processing with Kafka Streams and Apache Flink." What Is SAAS in Data Streaming? SaaS data streaming solutions are fully managed services where the provider handles all aspects of deployment, maintenance, scaling, and updates. SaaS offerings are designed for ease of use, providing a serverless experience where developers focus solely on building applications rather than managing infrastructure. Characteristics of SAAS in Data Streaming Serverless Architecture: Resources scale automatically based on demand. True SaaS solutions eliminate the need to provision or manage servers.Low Operational Overhead: The provider manages hardware, software, and runtime configurations, including upgrades and security patches.Pay-As-You-Go Pricing: Consumption-based pricing aligns costs directly with usage, reducing waste during low-demand periods.Rapid Deployment: SaaS enables users to start processing streams within minutes, accelerating time-to-value. What Is PaaS in Data Streaming? PaaS offerings sit between fully managed SaaS and infrastructure-as-a-service (IaaS). These solutions provide a platform to deploy and manage applications but still require significant user involvement for infrastructure management. Characteristics of PaaS in Data Streaming Partial Management: The provider offers tools and frameworks, but users must manage servers, clusters, and scaling policies.Manual Configuration: Deployment involves provisioning VMs or containers, tuning parameters, and monitoring resource usage.Complex Scaling: Scaling is not always automatic; users may need to adjust resource allocation based on workload changes.Higher Overhead: PaaS requires more expertise and operational involvement, making it less accessible to teams without dedicated DevOps resources. Examples of PaaS in Data Streaming (Kafka, Flink) PaaS offerings in data streaming, while simplifying some infrastructure tasks, still require significant user involvement compared to fully serverless SaaS solutions. Below are three common examples, along with their benefits and pain points compared to serverless SaaS: Apache Flink (Self-Managed on Kubernetes Cloud Service like EKS, AKS or GKE) Benefits: Full control over deployment and infrastructure customization.Pain Points: High operational overhead for managing Kubernetes clusters, manual scaling, and complex resource tuning.Amazon Managed Service for Apache Flink (Amazon MSF) Benefits: Simplifies infrastructure management and integrates with some other AWS services.Pain Points: Users still handle job configuration, scaling optimization, and monitoring, making it less user-friendly than serverless SaaS solutions.Amazon Managed Streaming for Apache Kafka (Amazon MSK) Benefits: Eases Kafka cluster maintenance and integrates with the AWS ecosystem.Pain Points: Requires users to design and manage producers/consumers, manually configure scaling, and handle monitoring responsibilities. MSK also excludes support for Kafka if you have any operational issues with the Kafka piece of the infrastructure. SaaS vs. PaaS: Key Differences SaaS and PaaS differ in the level of management and user responsibility, with SaaS offering fully managed services for simplicity and PaaS requiring more user involvement for customization and control. The big benefit of PaaS over SaaS is greater flexibility and control, allowing users to customize the platform, integrate with specific infrastructure, and optimize configurations to meet unique business or technical requirements. This level of control is often essential for organizations with strict compliance, security, or data sovereignty requirements. SaaS Is Not Always Better Than PaaS! Be careful: The limitations and pain points of PaaS do NOT mean that SaaS is always better. A concrete example: Amazon MSK Serverless simplifies Apache Kafka operations with automated scaling and infrastructure management but comes with significant limitations, including the lack of fully-managed connectors, advanced data governance tools, and native multi-language client support. Amazon MSK also excludes Kafka engine support, leading to potential operational risks and cost unpredictability, especially when integrating with additional AWS services for a complete data streaming pipeline. I explored these challenges in more detail in my article "When Not To Use Apache Kafka (Lightboard Video)." Bring Your Own Cloud (BYOC) as Alternative to PaaS BYOC offers a middle ground between fully managed SaaS and self-managed PaaS solutions, allowing organizations to host applications in their own VPCs. BYOC provides enhanced control, security, and compliance while reducing operational complexity. This makes BYOC a strong alternative to PaaS for companies with strict regulatory or cost requirements. While BYOC complements SaaS and PaaS, it can be a better choice when fully managed solutions don’t align with specific business needs. I wrote a detailed article about this topic: "Deployment Strategies for Apache Kafka Cluster Types." “Serverless” Claims: Don’t Trust the Marketing Many cloud data streaming solutions are marketed as “serverless,” but this term is often misused. A truly serverless solution should: Abstract Infrastructure: Users should never need to worry about provisioning, upgrading, or cluster sizing.Scale Transparently: Resources should scale up or down automatically based on workload.Eliminate Idle Costs: There should be no cost for unused capacity. However, many products marketed as serverless still require some degree of infrastructure management or provisioning, making them closer to PaaS. For example: A so-called “serverless” PaaS solution may still require setting initial cluster sizes or monitoring node health.Some products charge for pre-provisioned capacity, regardless of actual usage. Do Your Own Research When evaluating data streaming solutions, dive into the technical documentation and ask pointed questions: Does the solution truly abstract infrastructure management?Are scaling policies automatic, or do they require manual configuration?Is there a minimum cost even during idle periods? By scrutinizing these factors, you can avoid falling for misleading “serverless” claims and choose a solution that genuinely meets your needs. Choosing the Right Model for Your Data Streaming Business: SaaS, PaaS, or BYOC When adopting a data streaming platform, selecting the right model is crucial for aligning technology with your business strategy: Use SaaS (Software as a Service) if you prioritize ease of use, rapid deployment, and operational simplicity. SaaS is ideal for teams looking to focus entirely on application development without worrying about infrastructure.Use PaaS (Platform as a Service) if you require deep customization, control over resource allocation, or have unique workloads that SaaS offerings cannot address.Use BYOC (Bring Your Own Cloud) if your organization demands full control over its data but sees benefits in fully managed services. BYOC enables you to run the data plane within your cloud VPC, ensuring compliance, security, and architectural flexibility while leveraging SaaS functionality for the control plane . In the rapidly evolving world of data streaming around Apache Kafka and Flink, SaaS data streaming platforms provide the best of both worlds: the advanced features of tools like Apache Kafka and Flink, combined with the simplicity of a fully managed serverless experience. Whether you’re handling stateless stream processing or complex stateful analytics, SaaS ensures you’re scaling efficiently without operational headaches.

By Kai Wähner CORE