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Apache Flink Agents 0.4.0 brings AI agents into streaming pipelines, providing distributed execution and fault tolerance. The update adds sub-agents with durable execution, dynamic model routing, and the unified Decision Model API for scoring against business logic.

Key data engineering releases include Apache Kafka 4.4, DuckDB 2.0, Flink CDC 3.7, and Confluent's official Rust client for Kafka. Engineering highlights cover stream processing patterns like deduplication in Flink SQL, session-ordered Go pipelines, and scaling shuffle with Apache Celeborn.

TELIXO is a real-time M-Pesa transaction streaming pipeline designed to ingest, process, and analyze fintech data via event-driven architecture. It leverages Docker Compose across multi-language runtimes, including Python and Java, to maintain scalable stream processing and data pipelines.

JD.com built a Lakestream using Apache Fluss and Apache Hudi to unify streaming and lakehouse data. Fresh data in Fluss tables tiers directly into Hudi, cutting batch compute costs over 80% and storage by 50% while ending duplicate pipelines and reducing wasted network bandwidth from row-based

Apache Fluss enables crash-safe retries for side-effecting workers by combining an append-only log table for replay with a primary key table for state lookups. Workers persist intent before external calls, allowing restarted jobs to reconcile pending actions against external service ledgers.

Netflix rebuilt its Ads Event Publisher around stateful stream processing with Apache Flink. To handle massive live traffic spikes, it replaced synchronous key-value store writes with a real-time streaming join of Kafka streams using a custom KeyedCoProcessFunction over a 60-minute window.

🎙️ The Data Flowcast by Astronomer — Astrafy optimizes enterprise pipelines using Apache Airflow, dbt, and Google Cloud. The team combines Airflow datasets and Pub/Sub to trigger event-driven processing, link separate Airflow environments, and eliminate polling overhead for responsive, large-scale orchestration.

🎙️ The Data Flowcast by Astronomer — Airflow 3 introduces native support for event-driven orchestration to overcome the scalability limits of sensor-based workflows. Astrafy leverages event-driven assets instead of time-based scheduling, enabling data pipelines to react directly to real-time data changes across modern cloud stacks.

Build cross-account Kinesis pipelines by isolating producer, stream, and consumer into separate AWS accounts. KPL assumes a cross-account IAM role to write records, while KCL reads via stream resource policies and manages DynamoDB leases locally. Encrypt streams using customer-managed KMS keys.

Apache Flink CDC 3.7.0 adds AI-powered stream processing with OpenAI-compatible model transforms and ordered async execution. It adds inline Python UDFs, SQL Server and Fluss pipeline sources, plus schema expansion for Paimon and Fluss sinks.

FlareDB is a Rust-based streaming database that uses Apache Beam as its interface. Its unified streams-and-tables architecture executes batch and streaming pipelines, letting PCollections transition between streams in motion and materialized table state within a single engine.

Apache Doris 5.0 uses the Fluss Catalog to run unified SQL queries across real-time streaming logs and historical data tiered to Apache Paimon. This union read pattern eliminates separate ingestion jobs, letting engineers query fresh events alongside historical data without manual reconciliation.

Implement the streamhouse pattern locally using Apache Fluss, Apache Flink, and Apache Iceberg to enable direct querying of streaming data. This architecture integrates Fluss for real-time storage with Flink processing and Iceberg lakehouse tables.

Databricks Lakeflow complements Fivetran and dbt within a unified architecture. Fivetran automates SaaS and database ingestion into open formats like Delta Lake, Lakeflow processes Kafka event streams with Spark Declarative Pipelines, and dbt models SQL business logic with built-in testing.

Streamhouse shifts data architectures from topic-centric pipelines to shared, table-centric storage. Powered by Lakestream, it unifies real-time streaming and historical lakehouse data into single logical tables, preventing redundant state maintenance across operational and analytical workloads.

redb.Route is a .NET integration framework acting like Apache Camel. It uses a C# DSL to build From-Process-To pipelines connecting Kafka, RabbitMQ, Redis, SQL, and S3 without message-bus lock-in. It supports 29 external transports, hot-reloading, and token streaming.

Apache Kafka 4.0 on Amazon MSK introduces the KIP-848 consumer rebalance protocol, shifting coordination logic from clients to brokers. This incremental, server-driven rebalancing removes global synchronization barriers, letting unaffected consumers process data continuously during group changes.

The edition highlights Kafka and stream processing developments, focusing on broker architectures and Flink SQL patterns. Notable releases include Apache Fluss v1.0 and Khaos for failure testing, while new alternatives like blob-stream target cost over latency.

Delivery Hero replaced its batch ad measurement pipeline with Amazon Managed Service for Apache Flink. The streaming architecture resolved processing-time skew, synchronous API enrichment bottlenecks, and costly document database mutations, cutting infrastructure expenses by over half.

Real-time ETL spans batch, micro-batch, log-based CDC, and event streaming via tools like Kafka. Selection hinges on required latency rather than raw speed: log-based CDC efficiently reads database transaction logs, while true streaming continuously processes events for sub-second workloads.

Scaling Kafka consumer groups on MSK can exceed the internal 1 MB metadata record limit, causing a RecordTooLargeException during rebalances. Prevent rebalance retry loops by raising max.message.bytes on consumeroffsets and keeping replica.fetch.max.bytes matched.

Build a local real-time pipeline by publishing raw events to Kafka, deduplicating them with Flink SQL, and streaming the results into DuckLake. A consumer writes batches every 50 records or 5 seconds, using DuckLake row inlining to avoid small Parquet files.

Apache Fluss 1.0 delivers real-time storage for streaming and AI workloads. It adds a Rust-based HTTP Gateway for REST operations and lakehouse ingestion, native Python clients for feature lookups, and support for Flink, Spark, Paimon, and Apache Hudi.

The Streamhouse Working Group introduced an open architecture pattern designed to serve continuously updated enterprise state to production apps and AI agents. It relies on CDC, event streams, stream processing, and open table formats rather than periodic batching.

A technical meetup focused on real-time stream processing covers Apache Flink architectures and the streaming ecosystem. Sessions explore managing Flink state schema evolutions for bot detection, running real-time ad pacing workloads, and using Apache Fluss as a modern streaming analytics layer.