Databricks can consume a variety of data formats, making it a versatile platform for data processing and analysis. Here are some of the key input formats supported by Databricks:
Delta Lake: The default protocol for reading and writing data and tables.
Parquet: A columnar storage format optimized for large-scale data processing.
ORC (Optimized Row Columnar): A storage format that provides efficient data compression and encoding schemes.
JSON: A lightweight data-interchange format that is easy for both humans and machines to read and write.
CSV (Comma-Separated Values): A common format for data exchange that is simple and widely supported.
Avro: A binary data format that is compact and efficient for serializing data.
Text: Plain text files that can be used for various data processing tasks.
Binary: Raw binary data, often used for images or other non-text data.
XML: Extensible Markup Language used for encoding documents in a format that is both human-readable and machine-readable.
MLflow: A platform for managing the machine learning lifecycle, including experiment tracking and model management.
Databricks also supports reading compressed files in many formats and provides options for unzipping compressed files if necessary
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