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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Google Cloud Bigtable vs HBase

Google Cloud Bigtable vs HBase

OverviewComparisonAlternatives

Overview

HBase
HBase
Stacks511
Followers498
Votes15
GitHub Stars5.5K
Forks3.4K
Google Cloud Bigtable
Google Cloud Bigtable
Stacks173
Followers363
Votes25

Google Cloud Bigtable vs HBase: What are the differences?

Introduction

Google Cloud Bigtable and HBase are both distributed, scalable, and high-performance NoSQL database systems. While they have similar functionalities, there are key differences between Google Cloud Bigtable and HBase that set them apart in terms of features and performance.

  1. Data Processing Model: Google Cloud Bigtable uses the concept of column families to organize data, where each column family can have multiple columns. This allows for faster data retrieval and efficient storage. On the other hand, HBase stores data in tables consisting of rows and columns, where each row can have multiple columns. This allows for more flexible data modeling but can result in slower data retrieval compared to Bigtable.

  2. Automatic Scaling: Google Cloud Bigtable provides automatic scaling of resources based on the workload, allowing applications to handle fluctuations in data size and read/write requests seamlessly. HBase requires manual intervention for scaling, which may involve adding or removing nodes from the cluster to adjust the capacity.

  3. Managed Service: Google Cloud Bigtable is provided as a fully managed service on Google Cloud Platform (GCP), taking care of operations like data replication, software updates, and hardware provisioning. On the other hand, HBase requires manual configuration and management, often involving setting up and maintaining a cluster of machines.

  4. Integration with Google Cloud Platform: Google Cloud Bigtable is tightly integrated with other services in the Google Cloud ecosystem, such as BigQuery and Dataflow, allowing for seamless data processing and analytics workflows. HBase, being an open-source project, can be integrated with other tools and platforms, but may require additional setup and customization.

  5. Data Durability and Replication: Google Cloud Bigtable provides built-in data replication and durability, ensuring high availability and fault tolerance. It replicates data across multiple regions within GCP. HBase, on the other hand, relies on Apache Hadoop Distributed File System (HDFS) for replication, which may need additional configuration and management.

  6. Community and Support: HBase has a large and active open-source community, allowing for active development and support. It has been around for a longer time and has a mature ecosystem with a wide range of community-contributed tools and libraries. Google Cloud Bigtable, being a managed service, provides support through Google Cloud Platform, ensuring enterprise-level support and SLAs.

In summary, Google Cloud Bigtable and HBase differ in their data processing models, automatic scaling capabilities, managed service offerings, integration with other platforms, data durability and replication mechanisms, and community support. However, both provide scalable and distributed NoSQL database solutions suitable for various use cases.

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Detailed Comparison

HBase
HBase
Google Cloud Bigtable
Google Cloud Bigtable

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.

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.

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Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.;Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google’s big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.;Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable’s total cost of ownership is less than half the cost of its direct competition.;Security: Cloud Bigtable is built with a replicated storage strategy, and all data is encrypted both in-flight and at rest.;Simplicity: Creating or reconfiguring a Cloud Bigtable cluster is done through a simple user interface and can be completed in less than 10 seconds. As data is put into Cloud Bigtable the backing storage scales automatically, so there’s no need to do complicated estimates of capacity requirements.;Maturity: Over the past 10+ years, Bigtable has driven Google’s most critical applications. In addition, the HBase API is a industry-standard interface for combined operational and analytical workloads.
Statistics
GitHub Stars
5.5K
GitHub Stars
-
GitHub Forks
3.4K
GitHub Forks
-
Stacks
511
Stacks
173
Followers
498
Followers
363
Votes
15
Votes
25
Pros & Cons
Pros
  • 9
    Performance
  • 5
    OLTP
  • 1
    Fast Point Queries
Pros
  • 11
    High performance
  • 9
    Fully managed
  • 5
    High scalability
Integrations
No integrations available
Heroic
Heroic
Hadoop
Hadoop
Apache Spark
Apache Spark

What are some alternatives to HBase, Google Cloud Bigtable?

MongoDB

MongoDB

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.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

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.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

Amazon DynamoDB

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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