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HBase vs Vertica: What are the differences?


HBase and Vertica are two popular database management systems in the industry. While they both serve the purpose of storing and managing data, there are key differences between the two. In this article, we will explore the six main differences between HBase and Vertica.

  1. Data Model: HBase is a NoSQL database that follows a column-oriented data model. It stores data in tables, with rows and columns. On the other hand, Vertica is a SQL-based database that follows a traditional relational data model. It organizes data into tables, with rows and columns as well. However, the data modeling approach in HBase is more flexible and schema-less compared to Vertica's strict schema definition.

  2. Scalability: HBase is designed to handle massive amounts of data and can scale horizontally by adding more commodity servers to a cluster. It offers automatic sharding and distribution of data across nodes in a distributed environment. Vertica, on the other hand, can also handle large data volumes but scales vertically by adding more resources to a single server. It leverages high-performance hardware to deliver fast query processing.

  3. Consistency: HBase offers eventual consistency, where data may not be immediately consistent across all nodes in a distributed setup. It prioritizes availability and partition tolerance over strong consistency. Vertica, on the other hand, provides strong consistency, ensuring that data is always up-to-date and consistent across all nodes. This is achieved by replicating data synchronously or asynchronously.

  4. Data Compression: HBase utilizes compression techniques such as LZO, Snappy, or GZip to reduce the storage footprint and improve query performance. This is especially beneficial when dealing with large volumes of data. Vertica also supports data compression but employs its own advanced columnar compression algorithms, which provide efficient storage and enable fast query execution.

  5. Processing Speed: HBase is optimized for high read and write throughput, which makes it suitable for real-time applications that require low latency. It is capable of handling millions of operations per second. Vertica, on the other hand, is engineered for high-speed analytics and provides advanced query optimization techniques. It excels in complex analytical queries and aggregations, making it ideal for data warehousing and business intelligence use cases.

  6. Query Language: HBase uses HBase shell or programming APIs like Java or Python to interact with the database. It does not provide a declarative query language. Vertica, on the other hand, supports SQL, allowing users to write expressive, declarative queries to retrieve and manipulate data easily. This SQL compatibility enables seamless integration with existing data tools and frameworks.

In Summary, HBase and Vertica have distinct differences in their data models, scalability, consistency, data compression, processing speed, and query language. HBase offers a flexible schema-less data model, horizontal scalability, eventual consistency, compression techniques, high read/write throughput, and no declarative query language. On the other hand, Vertica follows a traditional relational data model, scales vertically, provides strong consistency, advanced columnar compression algorithms, high-speed analytics, and supports SQL for querying.

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Pros of HBase
Pros of Vertica
  • 9
  • 5
  • 1
    Fast Point Queries
  • 3
    Shared nothing or shared everything architecture
  • 1
    Reduce costs as reduced hardware is required
  • 1
    Offers users the freedom to choose deployment mode
  • 1
    Flexible architecture suits nearly any project
  • 1
    End-to-End ML Workflow Support
  • 1
    All You Need for IoT, Clickstream or Geospatial
  • 1
    Freedom from Underlying Storage
  • 1
    Pre-Aggregation for Cubes (LAPS)
  • 1
    Automatic Data Marts (Flatten Tables)
  • 1
    Near-Real-Time Analytics in pure Column Store
  • 1
    Fully automated Database Designer tool
  • 1
    Query-Optimized Storage
  • 1
    Vertica is the only product which offers partition prun
  • 1
    Partition pruning and predicate push down on Parquet

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What is HBase?

Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.

What is Vertica?

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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What companies use HBase?
What companies use Vertica?
See which teams inside your own company are using HBase or Vertica.
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Blog Posts

Jun 24 2020 at 4:42PM


Amazon S3KafkaHBase+4
MySQLKafkaApache Spark+6
What are some alternatives to HBase and Vertica?
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Google Cloud Bigtable
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
See all alternatives