Managing data lakes that use modern table formats, such as Delta Lake and Apache Iceberg, has evolved from simple file management into a systematic process similar to database management. The Spark engine of AnalyticDB for MySQL provides lifecycle management for data lake tables. This enables query performance on your data lake that is comparable to a data warehouse. This topic describes the key capabilities of data lake lifecycle management.
Data mutation (DML)
The managed Spark engine of AnalyticDB for MySQL supports insert, delete, and update operations on data lake tables that are stored on immutable media such as Object Storage Service (OSS).
Core capability | Technical details | Importance |
Upsert (merge) | Similar to the SQL | A core capability for Change Data Capture (CDC) synchronization. This is essential for processing change logs (binary logging) from sources like Salesforce or MySQL to build a real-time data lake. |
Logical deletion (delete) | Even on append-only storage, specific rows can be deleted by marking them or rewriting files. This supports requirements such as the "right to be forgotten" under the General Data Protection Regulation (GDPR). | Corrects incorrect data or responds to user requests to delete data. |
Overwrite | Atomically replaces the data of an entire partition or table. | Idempotence. If a data processing task fails or produces incorrect results, the ETL task can be rerun to automatically overwrite old data and prevent data duplication. |
Storage layout optimization
Storage layout optimization is key to improving query performance from hours to seconds and is the core solution to the "small file problem". With the managed Spark engine of AnalyticDB for MySQL and Alibaba Cloud's managed scheduling tools (DataWorks or DMS Airflow), you can regularly optimize the file layout. This improves query performance and provides an analytics experience on your data lake that is comparable to a data warehouse.
Core capability | Technical details | Importance |
Small file compaction (compression/bin-packing) | Merges thousands of small, kilobyte-sized files into larger files, such as 128 MB to 1 GB. | Reduces I/O latency. Object storage is sensitive to |
Data clustering (Z-ordering) | Physically stores related data, such as records with the same | Data Skipping skips 90% of files during queries, greatly reducing the amount of data scanned. |
File cleanup (vacuuming/expiring snapshots) | Physically deletes old version history files that are no longer needed. | Cost control. Prevents historical data versions from causing unlimited growth in OSS costs. It also deletes data that must be physically removed for compliance. |
Metadata and schema management (data governance)
Core capability | Technical details | Importance |
Schema evolution | Supports adding new columns, renaming columns, or changing column types without rewriting historical data. | Adapts to business changes. When business requirements change and new fields are needed, the data lake can adapt automatically. This prevents frequent pipeline failures. |
Schema enforcement | Rejects writes of data that do not conform to the schema, such as data with mismatched types. | Data quality gatekeeping. Prevents dirty data from contaminating "gold" or "silver" tables. This avoids errors when downstream analysts query data using SQL. |
Partition management | Dynamically generates new partitions or replaces data in specific partitions. | Query pruning. Ensures that a |
Time travel and versioning
With the managed Spark engine of AnalyticDB for MySQL, you can query specific historical snapshots of data lake tables and even roll back data to a previous version.
Core capability | Technical details | Importance |
History rollback (time travel) | Queries the state of a table at a specific point in the past, such as "yesterday at 2:00 PM", using syntax like | Disaster recovery and debugging. If user error corrupts data, a quick rollback to a correct version allows for investigation or recovery. |
Audit log | Records commit logs for data change operations, including the user, time, and content of the operation. | Security and traceability. Tracks the history of data changes to investigate the root cause of data anomalies. |
References
Manage the lifecycle of Apache Iceberg tables using Spark SQL
Manage the lifecycle of Delta Lake tables using Spark SQL