Spark SQL and DataFrame engineering · Lesson 1 of 3
Spark DataFrames and schema
Represent distributed records as typed columns and inspect their schema.
Lesson
Spark DataFrames represent distributed data as rows with named columns and a schema. The schema describes field names, types, and nullability, giving Spark information to validate operations and plan execution. Prefer explicit types at important ingestion boundaries because automatic inference can sample incomplete or inconsistent files and produce an unsuitable type.
Select only required columns, cast deliberately, and inspect nested fields before flattening them. A schema change should have a policy: accept a compatible new field, quarantine malformed input, or stop the pipeline when the change violates a contract. Practice task: define the expected type and null behavior for an event timestamp and customer identifier.
Applying the pattern: Inspect the source schema, project needed columns, apply explicit casts, and validate representative null and malformed values.
Checks and failure modes: Schema inference is not a data-quality contract. A column that is numeric in one file and text in another needs an intentional policy.
Before production, test both a representative input and a boundary case. Confirm the expected row grain, permissions, recovery behavior, and observable output for the task. Record the configuration needed to reproduce the run and compare its result with the agreed data contract.
Practice task: Why define a schema at an ingestion boundary?
Visual guide
Shape data into a contract
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Step 01
Inspect
Review fields and observed values.
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Step 02
Define
Set types and null expectations.
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Step 03
Transform
Cast and select required columns.
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Step 04
Validate
Check invalid and missing values.
Code examples
Project and cast columns
PYTHON
from pyspark.sql import functions as F
clean = source.select(
F.col('customer_id').cast('long'),
F.to_timestamp('event_time').alias('event_ts')
)Expected output
Projects two fields and explicitly converts their types.
Key points
- Inspect the source schema, project needed columns, apply explicit casts, and validate representative null and malformed values.
- Schema inference is not a data-quality contract. A column that is numeric in one file and text in another needs an intentional policy.
- Re-run the included practice scenario after changing source data or transformation logic.
Check your understanding
Practice
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