The choice between Parquet and Delta formats for storing data in Azure Data Lake Gen 2 is a decision that requires careful consideration. Both formats have their own distinct features and benefits.

Parquet is a widely-used columnar storage format that offers efficient compression and high query performance, making it suitable for analytical workloads.

On the other hand, Delta format provides additional capabilities such as a transaction log and the ability to update data over time. Delta tables can be read as of a specific point in time and offer advanced features like file pruning and ZOrder.

However, it is important for users to assess whether they truly require the advanced features of Delta or if Parquet is sufficient for their needs.

It should be noted that ML models handling Delta format as input may require additional conversion during pre-processing.

This article aims to compare the two formats, discuss their features and benefits, and highlight important considerations for users when deciding between Parquet and Delta in Azure Data Lake Gen 2.

Comparison

When comparing the Parquet and Delta formats in Azure Data Lake Gen 2 store, it is important to consider their differences in terms of features and compatibility.

Parquet is a widely adopted format that is compatible with almost every data system, making it a versatile choice for data storage and analysis. However, it does not offer the advanced features and flexibility provided by Delta.

Delta, on the other hand, adds an additional layer over Parquet, incorporating a transaction log and enabling updates to the data over time. It also offers features such as file pruning and ZOrder, which can enhance query performance. While Delta has wider adoption, it may not be universally compatible with all data systems.

Therefore, when deciding between Parquet and Delta, it is crucial to assess whether the advanced features and compatibility of Delta are necessary for the specific use case.

Features and Benefits

Regarding the features and benefits, it is important to consider the advanced capabilities and flexibility provided by the additional layer that Delta format offers over the underlying storage format.

Delta format, built on top of Parquet files, introduces a transaction log and ACID (Atomicity, Consistency, Isolation, Durability) transaction capabilities, enabling more robust and reliable data management.

It allows for efficient updates, deletes, and merges on existing data, making it suitable for scenarios where data evolves over time.

Delta tables can be read as of a specific point in time, providing temporal querying capabilities.

Additionally, Delta format offers features like file pruning and ZOrder on Databricks, enhancing data organization and query performance.

However, it is important to note that ML models handling Delta format as input may require conversion during pre-processing.

Considerations

In evaluating the choice between the two storage formats, it is important to consider various factors and considerations.

One consideration is the potential fragmentation that can occur with Delta format due to frequent updates. However, Azure Data Lake Store Gen2 is not optimized for large IO, so fragmentation may not significantly impact performance.

Another factor to consider is the compatibility of the formats with different data systems. Parquet is compatible with almost every data system, while Delta has wider adoption but not universal compatibility. Additionally, ML models that handle Delta format as input may require pre-processing for conversion.

On the other hand, Delta format offers advanced features such as transaction log, versioned Parquet files, and the ability to read data as of a given point in time.

Ultimately, the choice between Parquet and Delta formats depends on the specific requirements and preferences of the user.