Flink resources for data engineers

Iceberg delete file types are determined by the engine implementation, not user configs. Spark batch writes only position deletes, while Flink writes both. Because equality deletes cannot be directly compacted, maintain them using rewritedatafiles with delete-file-threshold to purge stale rows.

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.

Apache Iceberg 1.12.0 expands semi-structured and geospatial data capabilities. Flink and Kafka Connect can now write shredded Variant columns alongside Spark. The release also adds a Flink maintenance task to convert equality deletes into positional deletion vectors for faster streaming reads.

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.

Data engineering is shifting from rigid ETL toward autonomous pipelines and open lakehouses. Teams are adopting formats like Apache Iceberg and Delta Lake alongside Kafka and Flink for unified streaming, reducing manual SQL scripting in favor of data reliability engineering and FinOps.

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.

Engineers explore scalable architectures, bridging online streaming logic with offline replay across millions of events. Discussions focus on Spark 4.0 features like Real-Time Mode and transformWithState versus Apache Flink, alongside managing high-volume AI agent workloads on modern data

Stream Forward covers production Apache Flink engineering, addressing state management, checkpoint tuning, backpressure debugging, and job graph optimization. Sessions explore streamhouse patterns pairing Flink compute with Fluss real-time storage and Paimon, alongside real-time feature pipelines.

Stream Forward covers production Apache Flink engineering, addressing state management, checkpoint tuning, backpressure debugging, and job graph optimization. It details modern patterns like streamhouse architectures using Flink, Fluss, and Paimon, alongside real-time AI and stream ETL migrations.

Engineers explore Apache Flink in production for real-time streaming architectures. Adyen shares how Flink powers real-time feature computation for machine learning in payment processing.

🎙️ Kai Lentit — An interview satire explores big data engineering and architecture concepts projected into 2026. The discussion highlights ecosystem tools and patterns including Apache Iceberg, Hive tables, Flink, and Airflow graphs, alongside Kubernetes, Docker, Databricks, and cloud observability.

Flink Kubernetes Operator 1.16.0 updates autoscaling with a new BALANCED default parallelism alignment, avoiding blocked rescales and over-provisioning. It also adds pluggable autoscaler SPIs and supports native Kubernetes pod ResourceRequirements for TaskManagers and JobManagers.

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.

Lyft migrated its streaming fleet from an in-house Kubernetes operator to the open-source Apache Flink Kubernetes Operator to eliminate custom code debt. The shift unlocked native autoscaling and robust reconciliation loops without disrupting existing stateful job workflows.

Technical deep dives highlight parallel Apache Spark tuning, stream processing migrations via the Apache Flink Operator at Lyft, and caching storage optimization using Zstandard and Pingora. New tool releases include dbt doctor and the MLPerf Storage v3.0 benchmarks for data workloads.

Kafka architectures often sink real-time events to S3 for long-term retention. However, raw files fail to behave like database tables for updates and consistent reads. Open table formats like Apache Iceberg bridge this gap by adding schema, snapshot, and partition metadata over the data.

Key updates highlight optimizing SQL index design to cut query times from 17ms to 0.04ms, comparing Flink autoscalers, and handling automated Apache Kafka migrations. Tool launches spotlight DuckDB v2.0 parser improvements and DataFusion integrations with Cloudflare R2 SQL.

August data engineering roundups highlight streaming, query engines, and platform patterns. Apache Fluss became a top-level project, DuckDB 2.0 was previewed, and Netflix detailed streaming joins in Flink alongside its real-time distributed graph architecture.

Netflix is transitioning from an in-house Flink autoscaler to the Apache Flink Autoscaler to handle complex, stateful DAGs. Unlike coarse cluster metrics, the open-source engine estimates each operator's true processing rate to tune vertex-level parallelism, orchestrated via Temporal workflows.

Rednote migrated its real-time indexing pipeline from Kafka to Apache Fluss to solve read amplification from wide tables. Fluss uses columnar streaming via Apache Arrow and bypasses brokers for cold-data replays, isolating historical scans from real-time traffic to reduce bandwidth and compute

Netflix evolved its ads event architecture for live streaming traffic surges by replacing a synchronous database write on the ad-serving critical path with stateful stream processing.

CDC does not eliminate dual-writes; it merely shifts them downstream. Updating targets and committing pipeline offsets are independent, non-transactional acts. Without idempotent receivers, dual-write failure modes persist.

Apache Fluss provides lakehouse-native streaming storage, uniting message queues, key-value stores, and lakehouses into one substrate. It externalizes stream state and offers Arrow-based streaming with Iceberg, Paimon, and Lance integrations.

Iceberg v3 introduces the Variant type for semi-structured data, encoding values as a binary Parquet group with metadata and payload subfields. Variant is as flexible as JSON but stores data in a compact, typed binary form. Spark and Flink support it for handling evolving, schema-free event schemas.

This architecture detects streaming price anomalies using Kafka, Flink, and Spark. Flink processes Kafka event streams, calculates rolling Z-scores using historical baselines built by Spark, and routes anomalies to Claude to generate human-readable explanations displayed in Streamlit.

This end-to-end recommendation platform processes e-commerce events via PostgreSQL, MinIO, Debezium, and Kafka. Spark builds Iceberg tables while Flink processes real-time streams. Airflow orchestrates pipelines, feeding Feast with PostgreSQL offline and Redis online feature stores.

Apache Fluss graduated to an ASF Top Level Project, providing unified streaming storage for real-time lakehouses. Built on Apache Arrow, it enables server-side column and partition pruning to cut data transfer costs and bridge real-time streams with historical tables.

The streaming cold tier processes up to 1,250 reads per second globally, delivering a p50 latency of ~150 ms and a p99 of ~500 ms. Once a time slice crosses the user-configured cold-tier boundary, TimeSeries stops accepting writes for that time slice making it immutable.

The July 2026 data ecosystem round-up highlights key event streaming developments. Atlassian detailed transitioning from Kinesis to Kafka for 145 billion daily events, while Confluent covered cutting Flink OOMKills by 91.2%. Other topics include Apache Fluss graduating to an ASF top-level project.