OneLake & Delta Lake
The one copy of data for the whole tenant — and how to keep its Delta tables fast and cheap.
OneLake is a single, tenant-wide data lake built on ADLS Gen2. Every workspace gets a folder, every lakehouse and warehouse writes Delta-Parquet into it, and every engine (Spark, SQL, Power BI Direct Lake, KQL) reads the same files.
That is the promise. The cost is that badly maintained Delta tables degrade every engine at once. This section is about keeping them healthy.
Pages
Delta table optimization
OPTIMIZE, bin-compaction, target file size, and when to partition vs. Z-order.
V-Order tuning
What V-Order costs on write, what it saves on read, and when to turn it off.
VACUUM & retention
Reclaiming storage safely without breaking time travel or Direct Lake.
Multi-cloud shortcuts
S3, ADLS, and GCS shortcuts — caching, cost, and consistency gotchas.
Database mirroring
Near-real-time replicas of Snowflake, Azure SQL, and Cosmos DB as Delta tables.
Mental model
OneLake (tenant)
└── Workspace
└── Lakehouse
├── Tables/ managed Delta tables (SQL endpoint + Direct Lake)
│ └── my_table/
│ ├── _delta_log/ JSON + checkpoint commits
│ └── part-*.parquet data files (+ deletion vectors)
└── Files/ unmanaged — raw/landing zoneThe _delta_log is the source of truth. File listing performance, checkpoint
frequency, and the number of small Parquet files under each table are the three
levers that decide how fast a cold query starts.
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