Delta Lake vs Presto: What are the differences?

Introduction

Delta Lake and Presto are two widely used technologies in the field of big data and analytics. While both serve similar purposes, they have some key differences that make them distinct from each other. In this article, we will explore these differences in detail.

1. Data Storage and Processing: Delta Lake is an open-source storage layer that combines data lake and data warehousing capabilities. It stores data in Apache Parquet format and maintains transaction log files for its operations. On the other hand, Presto is a distributed SQL query engine that allows querying and processing of data across various data sources, including Hadoop Distributed File System (HDFS), Cassandra, and Elasticsearch. While Delta Lake focuses on storage and transaction management, Presto focuses on processing and querying.

2. Data Consistency: Delta Lake provides ACID (Atomicity, Consistency, Isolation, Durability) transactions, which ensure data consistency and integrity. It allows multiple concurrent writers and provides transactional guarantees for data modification operations. In contrast, Presto does not provide built-in support for ACID transactions. It allows concurrent reads and writes but does not guarantee strong consistency for data modifications.

3. Query Language: Delta Lake supports querying using traditional SQL, as well as Spark SQL, which extends SQL with additional features for working with structured and semi-structured data. On the other hand, Presto uses a subset of ANSI SQL for querying data, which is compatible with most SQL databases and allows users to write complex queries using standard SQL syntax.

4. Data Catalog Integration: Delta Lake integrates well with Apache Hive, allowing users to query and manage Delta tables using Hive's metastore. It also supports integration with other data catalogs like AWS Glue and Databricks Data Catalog. On the other hand, Presto does not have its own data catalog but can integrate with external catalogs like Hive, Apache Cassandra, and PostgreSQL for accessing metadata and enabling table discovery.

5. Performance Optimization: Delta Lake provides various performance optimization techniques like data skipping, predicate pushdown, and z-ordering, which improve query performance by reducing data scan and filtering operations. It also supports automatic and manual optimization of data layout for better query execution. Presto, on the other hand, optimizes query execution by pushing down filters and projections to the data source and using techniques like pipelining and parallelism. It also supports dynamic filtering to further improve performance.

6. Scalability and Resource Management: Delta Lake can scale horizontally across multiple nodes and handle large volumes of data efficiently. It leverages distributed computing capabilities of Apache Spark for processing and analytics. Presto is designed to be highly scalable and can handle petabytes of data across thousands of nodes in a distributed cluster. It uses a sophisticated resource manager to allocate resources dynamically based on query requirements.

In summary, Delta Lake focuses on data storage, consistency, and transaction management, while Presto is a distributed SQL query engine that focuses on querying and processing data across various data sources. Delta Lake provides ACID transactions, supports SQL and Spark SQL, integrates well with data catalogs, provides performance optimization techniques, and scales horizontally using Apache Spark. Presto, on the other hand, supports a subset of ANSI SQL, integrates with external catalogs, optimizes query execution using filter pushdown and parallelism, and can scale to handle petabytes of data across a distributed cluster.