April 2026 monthly summary for awslabs/data-on-eks. Key feature delivered: EKS and VPC cleanup order enhancement in the cleanup script to ensure EKS resources are removed before VPC resources, preserving NAT gateway and VPC endpoints during pod cleanup. Commit 7462369ff204eef45119fbb8b07547bbef83d174 implements the change (PR #1030; Signed-off-by: Manabu McCloskey). No major bugs fixed this month. Impact: reduces teardown risk, improves reliability of automated maintenance, and smooths CI/CD workflows. Technologies include EKS resource lifecycle management, AWS networking (VPC, NAT Gateway, VPC Endpoints), scripting/automation, and Git-based collaboration.

March 2026 (2026-03) focused on simplifying the stack, strengthening security, and stabilizing deployments while enabling scalable analytics and improved observability. Key outcomes include the deprecation and removal of legacy data processing blueprints and orchestration components (Trino, Flink, Kafka, NiFi, Spark Streaming, CloudNativePG, Argo Workflows), security enhancements for Celeborn via IRSA integration with conditional role creation, stabilization of deployments through pinned Celeborn versions and network/release safeguards, the initial deployment of a ClickHouse stack on EKS for scalable analytics, and improvements to monitoring and CI/CD practices with updated namespaces, documentation, and pinned GitHub Actions. These changes reduce maintenance burden, improve security posture, and enable more reliable, scalable data analytics.

February 2026 monthly summary for awslabs/data-on-eks focusing on infrastructure cleanup, performance tuning, and documentation improvements. Highlights include removing deprecated blueprints, upgrading core infra components, security hardening, and expanding guidance for platform usage.

January 2026 monthly summary for awslabs/data-on-eks: Delivered end-to-end data workloads on AWS EKS, including a comprehensive data stack (Airflow, Flink, Spark) with a scalable base/overlay architecture, deployment scripts, configuration files, and example jobs. Introduced a Pinot data stack on EKS for real-time analytics (Kafka and S3 DeepStore) and subsequently deprecated the Pinot blueprint and related configurations as part of architecture cleanup. Completed major documentation and tooling improvements, including benchmark data updates, improved navigation and NAU docs, CI/website build path adjustments, and modernization of Python packaging from requirements.txt to pyproject.toml. Significant fixes to docs and build pipelines improved reliability and developer experience.

November 2025: Delivered two high-impact capabilities across two repositories, enhancing observability, AI-assisted performance analysis, and benchmarking for Spark-based workloads. The MCP Server for Kubeflow Spark enables AI agents to analyze runtime behavior and extract actionable insights from Spark jobs. The Celeborn benchmark suite with Spark provides a structured performance framework with metrics, configurations, and visualizations to inform tuning and resource planning. These workstreams together shorten feedback loops, reduce performance risk, and support scalable AI-driven data processing.

Month: 2025-10 Concise monthly summary for the aws-mwaa/upstream-to-airflow repository focusing on a targeted bug fix that improved reliability of DAG bundle retrieval from S3. The change ensures only actual DAG files are processed by skipping directory-like keys. A test covering subdirectory cases was added to prevent regressions. The work aligns with MWAA expectations around robust DAG discovery and reduces potential processing errors in S3-based DAG sources.

August 2025: Delivered IaC-based IAM policy enabling AWS Bedrock Claude 3 Sonnet access for JupyterHub via the spark-k8s-operator; included a Terraform linting improvement. Established secure, scalable groundwork for model inference in data-on-eks.

July 2025 — ModelContextProtocol/Inspector Key features delivered: - Bug fix: MCP Proxy now logs explicit 404 Not Found messages, distinguishing 404s from other server errors and logging a specific message when an endpoint is not found, improving error reporting and observability. Major bugs fixed: - Implemented explicit 404 logging in the MCP Proxy (commit 5868c6bad6075e7c9dd49c3eb9b4ef946ceed53f). This change reduces ambiguity in failure cases and accelerates triage and resolution. Overall impact and accomplishments: - Enhanced observability and reliability of the MCP proxy, enabling faster diagnosis of missing endpoints and degraded routes. - Clearer customer-visible error reporting reduces support escalations and improves SLA adherence. Technologies/skills demonstrated: - HTTP proxy behavior, structured logging, and error handling. - Change traceability through commit references and clear commit messages.

June 2025 monthly summary: Delivered features and fixes that improve documentation quality, dashboard stability, and error observability while strengthening cross-repo collaboration. Key outcomes include a documentation overhaul that enhances navigation for data-on-eks users, a stable dashboard reference by pinning to known commits, and a focused bug fix to improve MCP proxy error reporting. These efforts reduce operational risk, accelerate onboarding, and reinforce reproducibility across repos.

Month: 2025-05 — Focused on stabilizing the documentation build and improving the accuracy of developer-facing docs for awslabs/data-on-eks. There were no new features released this month; primary work centered on a critical documentation fix that resolved a website build issue and ensured correct rendering of the Spark job instruction placeholders. These changes improve onboarding, reduce build-time failures, and enhance customer trust through reliable docs.

April 2025: Delivered Iceberg REST Catalog API integration for S3-backed tables in awslabs/data-on-eks, updated examples to use the REST catalog API, and established secure Spark connectivity with authentication configurations. This enables robust, catalog-driven data management and smoother onboarding for users deploying Iceberg-backed tables on S3.

March 2025 monthly summary for awslabs/data-on-eks: Delivered an end-to-end Spark + S3 Tables workflow through an example notebook. The notebook demonstrates Spark configuration for S3 Tables, namespace creation, loading CSV data from S3, writing to S3 Tables with Iceberg, and performing metadata queries plus basic analysis with DuckDB. Provides a ready-to-use, reproducible guide to adopt Spark + S3 Tables workflows, accelerating onboarding and adoption of lakehouse patterns. Commit reference: eb8d31b1fcccec166367f2694fa8673407363928 (feat: Add an example notebook for S3 Tables (#771)).

January 2025: Delivered JupyterHub on EKS with S3 Tables, enabling interactive data analysis on S3-backed data within a managed EKS environment. Implemented Terraform-based deployment for JupyterHub on EKS, a Dockerfile for a Spark notebook with S3 table support, and a sample notebook demonstrating S3 table operations; updated docs for setup and access. No major bugs fixed this month as focus was on feature delivery and documentation. Business impact: accelerates data science workflows, improves reproducibility, and simplifies onboarding for analysts.