Principle:DataTalksClub Data engineering zoomcamp Parquet Data Ingestion
| Page Metadata | |
|---|---|
| Knowledge Sources | DataTalksClub Data Engineering Zoomcamp |
| Domains | Data_Engineering, Batch_Processing |
| Last Updated | 2026-02-09 14:00 GMT |
Overview
Parquet data ingestion is the process of reading columnar file formats into a distributed data processing framework for efficient batch analytics.
Description
Columnar storage formats such as Parquet organize data by column rather than by row. This design provides several critical advantages for batch processing workloads:
- Schema preservation -- The file embeds its own schema metadata, so the reader automatically knows column names, data types, and nullability constraints without external schema definitions.
- Column pruning -- When a query only requires a subset of columns, the reader can skip entire columns that are not needed, dramatically reducing I/O.
- Predicate pushdown -- Filter conditions can be evaluated at the storage layer, allowing the reader to skip entire row groups that do not satisfy the filter, reducing the volume of data loaded into memory.
- Compression efficiency -- Columnar layout achieves superior compression ratios because values within a single column tend to be of the same type and exhibit similar patterns.
When ingesting data from multiple sources that represent different partitions or categories of the same domain (for example, different taxi service types), each source is read into a separate in-memory structure. This separation preserves the ability to apply source-specific transformations before later combining them.
Usage
Use parquet data ingestion when:
- Working with large-scale analytical datasets stored in columnar format
- The data has a well-defined schema that should be automatically inferred from the files
- Multiple input sources need to be read independently before being joined or unioned
- Performance is a concern and you need the benefits of column pruning and predicate pushdown
- The input data is partitioned across directories (e.g., by year or month) and glob patterns are used to read multiple partitions at once
Theoretical Basis
The ingestion of columnar data into a distributed framework follows this conceptual flow:
FUNCTION read_columnar_file(session, file_path):
metadata = read_file_footer(file_path)
schema = extract_schema(metadata)
row_groups = identify_row_groups(metadata)
dataframe = new DistributedDataFrame(schema)
FOR EACH row_group IN row_groups:
partition = load_row_group(row_group)
dataframe.add_partition(partition)
RETURN dataframe
dataset_a = read_columnar_file(session, path_to_source_a)
dataset_b = read_columnar_file(session, path_to_source_b)
The key insight is that Parquet files contain a footer with metadata about the schema and row group statistics. This footer is read first, enabling the framework to plan the read operation before any data bytes are loaded. Row groups are the unit of parallelism -- each row group can be read by a different worker node in the cluster.
When glob patterns are used (e.g., data/2020/*/), the framework expands the pattern to enumerate all matching files and distributes their row groups across available executors. This enables partition discovery, where directory structure is automatically interpreted as partitioning columns.