Choosing the Right Data File Format for Analytics, Integration, and Storage
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Data file formats define how your data behaves — how fast it moves, how much it costs to store, and how easily it integrates. Whether you’re working with APIs, loading data lakes, or exchanging documents with external systems, format choice is critical.
Why File Formats Matter
Compression → Storage cost & performance
Schema handling → Flexibility and version control
Tool compatibility → Interoperability across platforms
Read/write efficiency → Speed of ingestion, querying, and transformation
Human readability → Debugging and manual inspection
Core Format Categories
Structured Formats
CSV: Simple, readable, ubiquitous — but lacks schema, data types, or compression.
JSON: Popular for APIs and nested data; heavier and slower to parse.
XML: Verbose but highly structured, with strong schema validation.
Semi-Structured / Binary Formats
Avro: Row-based, efficient, schema-evolvable — ideal for Kafka and streaming.
Parquet: Columnar, highly compressed — built for big data analytics.
ORC: Columnar, great with Hive; often used in Hadoop environments.
Industry-Specific Interchange Formats
EDI: Legacy standard for B2B data exchange.
EDIFACT (EU/international)
X12 (US/retail/logistics)
HL7 (Healthcare)
Typically used in finance, logistics, healthcare, and procurement.
