Parquet/CSV Ingest
Learn how to load Parquet and CSV files into data warehouses and BI tools. Parquet is 10-100× faster for analytics queries.
Parquet/CSV Ingest: Loading Data for Query and Analysis
Overview
Parquet (columnar) and CSV (row-based) are common file formats for storing data. Ingestion means loading these files into a data warehouse or BI tool so they can be queried and analyzed. Parquet files organize data by column, letting queries read only the needed fields, whereas CSV files list records row-by-row. This makes Parquet much faster for analytics: it can be 10–100× faster than CSV because it scans far less data. Parquet also compresses data 2–5× better than CSV, saving storage and I/O. By contrast, CSV files are human-readable and universally supported (Excel, BI tools, etc.), but they incur higher storage and query costs.
Why Parquet?
Columnar layout speeds up aggregation queries and saves space. It’s a common choice in ETL pipelines, BI, and data warehouses, and is used by platforms like Spark, Hive, Athena, Snowflake and Databricks. Parquet files include schema metadata and support nested data types.
Why CSV?
CSV is simple and flexible. Almost any tool can export or read CSV data without special libraries. It’s good for small or ad-hoc datasets. However, CSV has no built-in schema or compression, so large datasets mean slow scans and higher costs.
Ingestion process
To ingest Parquet or CSV, you typically stage the files in object storage (like Amazon S3 or Azure Blob) and then load them into a warehouse. Many warehouses (e.g. Snowflake, Redshift, BigQuery) and BI tools can directly import Parquet/CSV. ETL/ELT tools (e.g. Fivetran, Airbyte) can automatically pull files from sources and load them into tables. After ingestion, the data is ready for querying. Parquet’s performance benefits make it ideal for large-scale analytics, while CSV remains useful for simpler data transfers.