Materialized views in Databricks SQL are designed for exactly this scenario: optimizing repetitive aggregation queries on large datasets. They store pre-computed results and can be refreshed incrementally, providing fast query performance and reducing compute costs compared to running the full aggregation on the raw data each time.

The Databricks optimizer uses column statistics to create better query plans, especially for filter operations. The `ANALYZE TABLE…COMPUTE STATISTICS FOR COLUMNS` command explicitly gathers these statistics (like min/max values) for the specified column, enabling potential data skipping and more efficient pruning of files that don’t contain relevant data for the filter.

The ‘Run as Dashboard Widget’ feature (also known as query result caching) is designed for this exact use case. When enabled, the query results are cached and automatically refreshed on the schedule you set (e.g., every hour). Dashboard viewers then retrieve the cached results instantly, providing fast load times and preventing repeated, expensive executions of the underlying query.

Delta Lake supports standard DML operations like `DELETE`. This command will correctly and efficiently remove only the rows matching the predicate (`batch_id = ‘2024-05-27-error’`). The operation is transactional, recorded in the Delta log, and can be audited or even rolled back using time travel, making it the precise and safe choice for this scenario.

While Databricks optimizes joins automatically, explicitly caching a small, frequently used lookup table in memory ensures it is readily available on all nodes of the cluster. This strongly encourages the optimizer to use a BroadcastHashJoin, where the small table is sent to all nodes joining with the large `orders` table, significantly improving join performance. The other options (ZORDERing a small table, creating a view, or setting a non-existent property) do not directly influence the join strategy in this way.

The Data Explorer in the Databricks platform includes a dedicated ‘Lineage’ tab for tables. This feature automatically visualizes upstream sources (the silver tables) and downstream dependencies of a selected table, providing clear, native documentation of data flow without manual effort. This is a key tool for data governance.

A large `ORDER BY` operation (sort) requires a single partition to sort all data globally, which can cause memory pressure. Adding a `DISTRIBUTE BY` clause on the same column (or a related one) pre-partitions the data. This allows the sort (`ORDER BY`) to happen within each partition, which is more memory-efficient and can prevent the ‘out of memory’ error, especially when followed by a `LIMIT`.

In Unity Catalog (or the Databricks SQL permission model), permissions can be granted at the schema level. Granting `USE SCHEMA` allows them to see and switch to the schema, and granting `SELECT` on the schema automatically applies that permission to all current and future tables within it. This is far more manageable than granting permissions on each table individually and is the correct principle of least privilege for this scenario.

Window functions are ideal for comparing rows with related rows in the same dataset. Using a function like `LAG(total_sales, 1) OVER (ORDER BY month)` would allow you to place the previous month’s value directly next to the current month’s value in the same result row, making the calculation and comparison simple, efficient, and readable within a single query pass.

Materialized views in Databricks SQL are designed for exactly this scenario: optimizing repetitive aggregation queries on large datasets. They store pre-computed results and can be refreshed incrementally, providing fast query performance and reducing compute costs compared to running the full aggregation on the raw data each time.0