Apache Spark vs Delta Lake: What are the differences?

Introduction

Apache Spark and Delta Lake are two popular big data technologies used for processing and analyzing large datasets. While both technologies are often used together, they have some key differences that set them apart.

1. Data Storage and Processing: The key difference between Apache Spark and Delta Lake lies in their approach to data storage and processing. Apache Spark is primarily a distributed computing system that provides an interface for processing data in parallel. It supports various data formats and can process both batch and streaming data. On the other hand, Delta Lake is an open-source storage layer that operates on top of existing data lakes and provides ACID (Atomicity, Consistency, Isolation, Durability) transactions. It allows data engineers and data scientists to handle large-scale datasets with reliability and consistency.

2. Data Consistency and Reliability: Delta Lake provides atomic writes and reads, as well as schema enforcement and schema evolution capabilities. This ensures that data is written and read in an all-or-nothing manner, making it easier to maintain data consistency and reliability. Apache Spark, on the other hand, does not provide built-in support for data consistency and reliability. Data engineers and developers need to implement custom logic to ensure data consistency and reliability.

3. Data Versioning and Time Travel: Delta Lake is designed to provide built-in versioning and time travel capabilities. It allows users to query and access older versions of data, making it easier to track changes over time. In contrast, Apache Spark does not provide built-in support for data versioning and time travel. Data engineers need to implement custom logic or use additional tools to achieve similar functionality.

4. Data Quality Management: Delta Lake includes features like schema validation and data quality checks, which help ensure that data remains consistent and of high quality. It provides mechanisms to enforce schema evolution rules and perform data validation during write operations. Apache Spark does not have built-in support for data quality management. Data engineers need to implement custom logic to validate and enforce data quality rules.

5. Optimized Performance: Delta Lake optimizes data reads and writes by using various techniques like Z-ordering, data skipping, and caching. These optimizations improve query performance and reduce the amount of data scanned during read operations. Apache Spark also provides optimizations for data processing, but it does not have the same level of built-in optimization capabilities as Delta Lake.

6. Ecosystem Integration: Apache Spark has a vibrant ecosystem and supports integration with various data processing and analytics tools. It provides connectors for different data sources and supports integration with popular frameworks like Apache Hadoop, Apache Kafka, and Apache Hive. Delta Lake, being a storage layer built on top of data lakes, can seamlessly integrate with Apache Spark and leverage its ecosystem.

In summary, Apache Spark is a distributed computing system primarily focused on data processing, while Delta Lake is a storage layer that provides reliability, consistency, and versioning capabilities on top of existing data lakes. Delta Lake's built-in support for data consistency, reliability, versioning, and data quality management sets it apart from Apache Spark.