Filtering, grouping, and aggregating data

Transforming data in Azure Databricks means using Spark SQL or the DataFrame API (PySpark, Scala, SQL) to filter, group, and aggregate data as part of ETL pipelines, often inside Delta Live Tables or notebooks. These operations reshape raw or bronze-layer data into clean, aggregated silver/gold tables for analytics.

1 · Learn the must-know

• 1Filtering uses .filter() or .where() (interchangeable in DataFrame API) or a SQL WHERE clause, and predicate pushdown lets Spark skip reading unnecessary files/partitions when filters are applied early.
• 2Grouping is done with .groupBy() (DataFrame API) or GROUP BY (SQL), which must be paired with an aggregation function (count, sum, avg, min, max, etc.) or it only returns a GroupedData object, not a result.
• 3Multiple aggregations can be computed at once using .agg() with multiple functions or column expressions, and results can be aliased with .alias() for clarity in downstream tables.
• 4Window functions (via pyspark.sql.window.Window) enable aggregations over partitions without collapsing rows, useful for running totals, ranking, or comparisons within groups, unlike groupBy which reduces row count.
• 5Aggregations on Delta tables benefit from data skipping and file statistics, so filtering before grouping (predicate placement) can significantly improve performance on large datasets.
• 6SQL and DataFrame transformations are lazily evaluated in Spark, so filters, groupings, and aggregations only execute when an action (e.g., display(), collect(), write) triggers the job, which matters for debugging and pipeline design.

2 · Check your understanding