Greenplum Database vs Vertica: What are the differences?

Introduction

Greenplum Database and Vertica are both columnar database management systems used for big data analytics. While they share some similarities, there are several key differences between them that make each unique in its own way.

1. Architecture: Greenplum Database is based on PostgreSQL and uses a master-slave architecture, where a single master node coordinates multiple segment nodes. Vertica, on the other hand, has a shared-nothing architecture, where each node in the cluster is independent and self-sufficient. This architectural difference leads to variations in scalability, fault tolerance, and query performance.

2. Data Distribution: In Greenplum Database, data is distributed across segments in a round-robin fashion, ensuring an even distribution of data among all segment nodes. Vertica, however, uses a more sophisticated data distribution strategy based on projections and data segmentation, allowing it to optimize query execution based on the distribution of data.

3. Compression: Greenplum Database provides multiple compression options, including block-level compression, column-level compression, and table-level compression. Vertica also offers various compression techniques such as run-length encoding, dictionary encoding, and delta compression. However, Vertica's compression techniques are generally more advanced and can achieve higher compression ratios compared to Greenplum Database.

4. Concurrency Control: Greenplum Database uses a modified version of PostgreSQL's MVCC (Multi-Version Concurrency Control) to handle concurrent transactions. Vertica, on the other hand, utilizes a different approach called "Optimized Row Columnar" (ORC), which provides efficient parallel query processing and concurrency control optimized for columnar data storage.

5. Data Storage Format: Greenplum Database stores data in a row-based format, similar to traditional relational databases. Vertica, on the other hand, stores data in a columnar format, where each column is stored separately. This columnar storage enables more efficient compression, faster query performance for analytical workloads, and better data compression ratios.

6. Integration with Ecosystem: Greenplum Database has strong integration with the Hadoop ecosystem, allowing users to leverage Hadoop's distributed file system (HDFS) and interact with data stored in Hadoop. Vertica, on the other hand, provides integration with various big data tools and frameworks such as Apache Kafka, Apache Spark, and Apache HBase, allowing seamless data ingestion and analysis from multiple sources.

In summary, Greenplum Database and Vertica differ in their architecture, data distribution strategies, compression techniques, concurrency control methods, data storage formats, and integration with the wider big data ecosystem. These differences make them suitable for different use cases and offer users various options based on their specific requirements.