Category: Analytics

The KIP-848 consumer protocol in Apache Kafka 4.0 redesigns consumer group rebalancing to eliminate stop-the-world pauses. This post explains how the consumer protocol works on Amazon MSK, how to enable it on MSK Standard and Express brokers, and how to diagnose and resolve slow rebalancing issues.

Announcing Spark Connect on Amazon EMR on EKS: build, test, and debug Spark applications from VS Code, PyCharm, Jupyter notebooks, Amazon SageMaker Unified Studio, or dbt, while running full-scale Spark operations on your existing Amazon EKS clusters.

Amazon SageMaker Unified Studio now supports custom permissions boundaries for the IAM roles its Tooling blueprint creates. Learn how to create a permissions boundary that restricts AI agent capabilities, configure it on the Tooling blueprint with the AWS CLI, and validate that every provisioned role carries the boundary.

Learn how Delivery Hero rebuilt its ad measurement pipeline from hourly batch processing to real time on Amazon Managed Service for Apache Flink, cutting the gap between an event and its recording from 61 minutes to 1.2 seconds and reducing monthly operational costs by about 57%.

Configuring VPC networking per project across a SageMaker Unified Studio domain creates inconsistent, hard-to-audit networks. This post shows administrators how to configure domain-level VPC networking once, so every new project automatically inherits consistent, private network isolation, then update existing projects and validate connectivity.

Amazon EMR on EC2 now supports Spark Connect, so you can develop and debug PySpark interactively from Amazon SageMaker Unified Studio Data Notebooks or your own IDE while Spark runs on your cluster. This post shows you how to get started from both a Data Notebook and a local IDE.

In Part 2 of this series, sign in as a data consumer, subscribe to enriched unstructured data assets in Amazon SageMaker Catalog, and query them using natural language through a no-code Amazon Bedrock chat agent and Amazon Bedrock model inference.

As consumer groups on Amazon MSK scale to thousands of members, the metadata record Kafka writes during rebalances can exceed the 1 MB limit and stall the group. Learn how to estimate metadata size, raise the topic-level limit safely, plan capacity, and apply complementary strategies for scaling large consumer groups.

Moeve standardized how it runs dbt across multiple data lakes by building a centralized, serverless launcher on Amazon Athena, AWS Step Functions, AWS Fargate, Amazon DynamoDB, and Amazon EventBridge, cutting new-project onboarding from days to about 15 minutes while keeping compute close to the data and orchestration loosely coupled.

Connect Snowflake to Amazon SageMaker Unified Studio to build a unified data catalog. Query federated Snowflake tables without moving data, publish enriched assets to SageMaker Catalog, and validate data quality with AWS Glue Data Quality, all while keeping data in Snowflake.