Discover the file format used for storing Delta Lake tables in the Databricks ecosystem. Learn how this format optimizes data storage and enables powerful features for data engineering and analytics. Prepare for the Databricks Certified Data Engineer Associate exam with our comprehensive explanation.
Table of Contents
Question
Which file format is used for storing Delta Lake Table?
A. CSV
B. Parquet
C. JSON
D. Delta
Answer
B. Parquet
Explanation
Delta Lake tables are stored using the Parquet file format. Parquet is a columnar storage format that provides efficient compression and encoding schemes, enabling faster query performance and reduced storage costs compared to row-based formats like CSV or JSON.
When you create a Delta Lake table, the data is stored in Parquet files under the hood. Delta Lake leverages the benefits of Parquet, such as its ability to efficiently read and write data in a columnar manner, while adding additional features and capabilities on top of it.
Some key advantages of using Parquet as the storage format for Delta Lake tables include:
- Columnar storage: Parquet stores data in a columnar format, meaning that values from the same column are stored together. This allows for efficient compression and encoding schemes, reducing storage costs and enabling faster query performance by minimizing I/O operations.
- Schema evolution: Delta Lake supports schema evolution, allowing you to easily add, modify, or remove columns in a table without the need for costly data migrations. Parquet’s schema flexibility enables Delta Lake to handle schema changes seamlessly.
- 3Time travel and versioning: Delta Lake provides time travel capabilities, allowing you to query and analyze data as it existed at a specific point in time. Parquet’s immutable nature, combined with Delta Lake’s transaction log, enables efficient storage and retrieval of historical data versions.
- Compatibility with big data tools: Parquet is widely supported by various big data processing frameworks and tools, such as Apache Spark, Hive, and Presto. By using Parquet as the storage format, Delta Lake tables can be easily integrated with these tools, enabling seamless data processing and analysis across different systems.
It’s important to note that while Delta Lake uses Parquet as the underlying storage format, it extends Parquet’s capabilities with additional features like ACID transactions, data versioning, and schema enforcement. These features make Delta Lake a powerful solution for building reliable and scalable data pipelines in the Databricks ecosystem.
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