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Latest in the feed
Data Engineering ·
Kafka Rebalancing on Amazon MSK: Moving Beyond Stop-the-World Pauses
Kafka consumer recovery is moving from a global pause model toward incremental, broker-directed coordination.
Modern Data Stack ·
Data Platforms Are Bringing Operability Closer to the Workload
The latest changes across the data-platform landscape are not simply adding more features. They are bringing development, security, resilience and governance closer to the workloads that data teams operate every day.
Data Engineering ·
Delivery Hero’s Flink Rebuild: From Hourly Measurement to Near Real Time
Streaming modernisation can improve both decision speed and operating cost when it replaces repeated batch processing with a carefully designed stateful pipeline.
Data Engineering ·
Moeve’s Shared dbt Launcher: Standardising Execution Without Centralising Every Pipeline
Data teams can standardise how transformations run while keeping orchestration and domain-owned data loosely coupled.
AI for Data Teams ·
SageMaker Catalog: Governed Unstructured Data Becomes Queryable by Conversation
Enterprise unstructured-data workflows are moving toward the same catalogue, ownership and approval patterns used for governed analytical data.
Data Governance ·
SageMaker Tooling Blueprints: Permissions Boundaries Reach Provisioned Roles
Governed data and AI workspaces are beginning to inherit enterprise permission limits when they are created.
Data Engineering ·
Spark Connect on Amazon EMR: Local Development Meets Remote-Scale Execution
Distributed data development is separating the developer interface from the cluster where execution happens.
BI & Analytics ·
BigQuery augmented analytics: investigating metrics in SQL
Analytical workflows that previously required separate statistical notebooks or specialised tooling are increasingly becoming callable warehouse functions.
Modern Data Stack ·
BigQuery Graph: connected data in the warehouse
Graph workloads no longer necessarily require organisations to move warehouse data into a separate graph database.
Data Engineering ·
Databricks Auto CDF: deriving changes at query time
Change-data processing is becoming less dependent on configuring every table correctly before changes occur.