Delta Lake best practices for lakehouses

Delta Lake brings ACID transactions, schema enforcement and time travel to a data lake. These are the practices I keep coming back to in production.

Partitioning

CREATE TABLE events
USING DELTA
PARTITIONED BY (date, region)
AS SELECT * FROM raw_events

• ✦Partition on columns people filter on often
• ✦Aim for files of 1 GB or more per partition
• ✦Avoid columns with lots of unique values

Z-ordering

Lay out the data to match the queries you run most:

OPTIMIZE events
ZORDER BY (customer_id, event_type)

Data quality

Let the schema grow safely when new columns appear:

df.write.format("delta")
  .option("mergeSchema", "true")
  .mode("append")
  .save("/data/events")

And stop bad rows at the door:

ALTER TABLE events
ADD CONSTRAINT valid_amount CHECK (amount > 0)

Maintenance

-- Compact small files
OPTIMIZE events

-- Clean up old versions
VACUUM events RETAIN 168 HOURS

Delta Lake gives you the reliability of a warehouse with the flexibility of a lake. These habits keep it fast and easy to maintain.