Synchronizing streaming data with batch pipelines involves creating a unified system that handles both real-time and historical data processing. The key is to design pipelines that share common data sources, processing logic, and storage layers while addressing the inherent differences in latency and throughput. This typically requires a combination of infrastructure design, data partitioning strategies, and metadata management.
First, a common storage layer acts as the foundation. Both streaming and batch pipelines write to and read from the same storage system, such as a data lake (e.g., Amazon S3, Azure Data Lake) or a distributed file system (e.g., HDFS). For example, a streaming pipeline might ingest real-time sensor data into a “raw” directory partitioned by event time, while a daily batch job processes the same directory to compute aggregates. To avoid conflicts, timestamps or watermarks track which data has been processed by each pipeline. Tools like Apache Kafka can store streaming data temporarily, while batch jobs pull from Kafka topics or read directly from persistent storage after a retention period.
Second, processing logic alignment ensures consistency. For instance, if a streaming pipeline calculates hourly averages using a tool like Apache Flink, the batch pipeline should replicate the same logic (e.g., SQL queries or code) when reprocessing historical data. Frameworks like Apache Beam allow developers to write code once and deploy it in both streaming and batch modes. Metadata tables (e.g., Apache Hudi or Delta Lake’s transaction logs) help track which data partitions have been processed, preventing redundant work. For example, a batch job might skip partitions already handled by the streaming pipeline, or merge incremental updates from streaming with bulk batch outputs.
Finally, handling late-arriving data and versioning is critical. Streaming systems often use windowing mechanisms (e.g., sliding windows in Apache Spark Structured Streaming) to manage out-of-order events. Batch pipelines can complement this by periodically reprocessing data to correct errors or incorporate late updates. For example, a daily batch job might recompute the previous day’s metrics using the latest data, overriding earlier results if needed. Tools like Delta Lake support ACID transactions, enabling both pipelines to safely update the same datasets. Monitoring tools like Prometheus or custom logging ensure discrepancies are detected, allowing for manual or automated reconciliation.
By integrating storage, aligning logic, and managing data lifecycle, developers can synchronize streaming and batch pipelines effectively, ensuring accurate and consistent results across both systems.