Delta Lake vs Impala: What are the differences?

Introduction

In this Markdown document, we will be discussing the key differences between Delta Lake and Impala.

1. Performance: Delta Lake is designed to provide optimized query performance on top of cloud data lakes, enabling high-speed analytics even on large datasets. On the other hand, Impala is a massively parallel processing (MPP) SQL query engine specifically built for Apache Hadoop, which provides interactive and fast query execution.

2. Data Consistency: Delta Lake offers ACID (Atomicity, Consistency, Isolation, Durability) transactional capabilities for both batch and streaming data, ensuring data consistency and reliability. In contrast, Impala does not provide built-in ACID transactions and instead focuses on providing fast query execution.

3. Data Reliability: Delta Lake provides built-in schema enforcement and schema evolution capabilities, allowing for data integrity and compatibility, even with evolving schemas. Impala, on the other hand, does not have built-in mechanisms for schema enforcement, making it more flexible in terms of data exploration but potentially less reliable.

4. Streaming Data Support: Delta Lake supports both batch and streaming data, enabling the ingestion and processing of real-time data. It allows for transactional streaming writes and offers scalable and reliable processing of real-time events. Impala, on the other hand, focuses more on batch processing and does not have native support for streaming data.

5. File Format Compatibility: Delta Lake is compatible with Apache Parquet and Apache Avro file formats, enabling efficient and optimized storage and query performance. Impala, on the other hand, supports a wide range of file formats, including Parquet, Avro, ORC, and more, offering more flexibility in terms of data ingestion and storage.

6. Integration and Ecosystem: Delta Lake is tightly integrated with Apache Spark and provides seamless integration with the Spark ecosystem. It leverages the power of Spark's distributed processing capabilities for efficient data processing. On the other hand, Impala is closely integrated with the Hadoop ecosystem, making it a suitable choice for organizations already leveraging the Hadoop ecosystem.

In summary, Delta Lake provides optimized query performance, data consistency, reliability, streaming data support, compatibility with Parquet and Avro file formats, and tight integration with Apache Spark. Impala, on the other hand, offers fast query execution, flexibility in file format support, integration with the Hadoop ecosystem, and a focus on batch processing.