Streaming Data Lake Ingestion with Apache Flink and Apache Iceberg
This article explains how Apache Flink integrates with data lake architectures, especially using Apache Iceberg as a table format, to enable real‑time streaming ingestion, CDC processing, near‑real‑time lambda architectures, and future enhancements like automatic file merging and row‑level deletes.
Apache Flink is a popular unified stream‑batch engine in the big‑data ecosystem, and data lakes are emerging as a cloud‑native storage architecture. This article explores how Flink interacts with data lakes, focusing on Apache Iceberg as the table‑format layer.
Data lakes store raw data from diverse sources, support multiple compute models, provide comprehensive data‑management capabilities (schema, partition, access control), and rely on cheap distributed storage such as S3, OSS or HDFS.
The typical open‑source data‑lake stack consists of four layers: (1) distributed file system, (2) data‑acceleration layer (e.g., Alluxio, Jindofs), (3) table‑format layer (Delta, Iceberg, Hudi), and (4) compute engines (Spark, Flink, Presto, Hive). Iceberg is chosen because it decouples compute from storage and offers ACID, snapshot, and schema evolution.
Several classic use cases are presented: (1) real‑time data pipelines that ingest Kafka streams into Iceberg tables, (2) CDC ingestion from relational databases using Flink CDC connectors, (3) near‑real‑time lambda architectures where Flink writes to Iceberg for both incremental and batch processing, (4) bootstrapping new Flink jobs with historical Iceberg data plus Kafka increments, and (5) using Iceberg as a low‑cost historical store to correct streaming results.
Why Iceberg? It is engine‑agnostic, provides strong table‑format semantics, and aligns with Flink’s stream‑batch design. Its community support and production usage by companies like Netflix, Apple, LinkedIn, and Tencent further validate its robustness.
Implementation details: Flink 0.10.0 already supports streaming and batch reads/writes to Iceberg. The sink is split into two operators— IcebergStreamWriter (writes records to Avro/Parquet/ORC files and creates Iceberg DataFiles) and IcebergFilesCommitter (collects DataFiles per checkpoint and commits a transaction). The committer maintains a state map Map<Long, List<DataFile>> to handle failed checkpoint retries.
Future roadmap includes automatic small‑file merging (Iceberg 0.11.0), a streaming reader for Iceberg, and row‑level delete / upsert support (Iceberg 0.12.0), enabling full CDC pipelines.
The article ends with author information and a promotional call‑to‑action for Alibaba Cloud’s Flink commercial offering, including giveaways and discounts.
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