Amazon DynamoDB vs HBase

Overview

Amazon DynamoDB

Stacks4.0K

Followers3.2K

Votes195

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

Amazon DynamoDB vs HBase: What are the differences?

Introduction

Amazon DynamoDB and HBase are both NoSQL databases that offer scalable, high-performance storage options. However, there are key differences between the two that make them suitable for different use cases.

Data Model: Amazon DynamoDB is a key-value store with a flexible schema, allowing for dynamic attributes and sparse indexes. On the other hand, HBase is a wide-column store that organizes data in column families, allowing for fast access to large amounts of structured and semi-structured data.
Scalability: DynamoDB is fully managed by Amazon Web Services (AWS) and automatically scales to handle high traffic and storage needs. It provides horizontal scaling with automatic data partitioning and distribution. In contrast, HBase requires manual sharding and distribution of data across the cluster, making it less flexible for dynamic scaling.
Consistency Model: DynamoDB offers strong consistency for read and write operations, ensuring that all replicas are consistent before returning a response. HBase, on the other hand, offers tunable consistency, allowing users to choose between strong or eventual consistency based on their application requirements.
Querying: DynamoDB provides a rich set of querying options, including primary key lookups, efficient range queries, and secondary indexes. HBase supports key lookups and range queries but lacks built-in support for secondary indexes, requiring manual data modeling techniques for efficient querying.
Durability: DynamoDB provides synchronous replication across multiple Availability Zones (AZs) within a region, ensuring high durability and availability. HBase supports asynchronous replication but requires additional tooling and configuration for achieving high durability.
Integration with Ecosystem: DynamoDB seamlessly integrates with other AWS services and can easily be integrated with applications built on AWS infrastructure. HBase, being an Apache Hadoop project, is primarily used in big data ecosystems and integrates well with Hadoop and other Hadoop-compatible tools.

In summary, while both DynamoDB and HBase are NoSQL databases that offer scalability and high-performance, DynamoDB provides a flexible schema, automatic scaling, strong consistency, and tight integration with the AWS ecosystem. On the other hand, HBase offers a wide-column data model, manual scaling, tunable consistency, and is better suited for integration within big data ecosystems.

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Advice on Amazon DynamoDB, HBase

Doru

Solution Architect

Jun 9, 2019

Reviewon

Amazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

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Detailed Comparison

Amazon DynamoDB	HBase
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.	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.
Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture	-
Statistics
GitHub Stars -	GitHub Stars 5.5K
GitHub Forks -	GitHub Forks 3.4K
Stacks 4.0K	Stacks 511
Followers 3.2K	Followers 498
Votes 195	Votes 15
Pros & Cons
Pros 62 Predictable performance and cost 56 Scalable 35 Native JSON Support 21 AWS Free Tier 7 Fast Cons 4 Only sequential access for paginate data 1 Scaling 1 Document Limit Size	Pros 9 Performance 5 OLTP 1 Fast Point Queries
Integrations
Amazon RDS for PostgreSQL PostgreSQL MySQL SQLite Azure Database for MySQL	No integrations available

What are some alternatives to Amazon DynamoDB, HBase?

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

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

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

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 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

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 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.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Amazon DynamoDB vs HBase: What are the differences?

Introduction

Data Model: Amazon DynamoDB is a key-value store with a flexible schema, allowing for dynamic attributes and sparse indexes. On the other hand, HBase is a wide-column store that organizes data in column families, allowing for fast access to large amounts of structured and semi-structured data.
Scalability: DynamoDB is fully managed by Amazon Web Services (AWS) and automatically scales to handle high traffic and storage needs. It provides horizontal scaling with automatic data partitioning and distribution. In contrast, HBase requires manual sharding and distribution of data across the cluster, making it less flexible for dynamic scaling.
Consistency Model: DynamoDB offers strong consistency for read and write operations, ensuring that all replicas are consistent before returning a response. HBase, on the other hand, offers tunable consistency, allowing users to choose between strong or eventual consistency based on their application requirements.
Querying: DynamoDB provides a rich set of querying options, including primary key lookups, efficient range queries, and secondary indexes. HBase supports key lookups and range queries but lacks built-in support for secondary indexes, requiring manual data modeling techniques for efficient querying.
Durability: DynamoDB provides synchronous replication across multiple Availability Zones (AZs) within a region, ensuring high durability and availability. HBase supports asynchronous replication but requires additional tooling and configuration for achieving high durability.
Integration with Ecosystem: DynamoDB seamlessly integrates with other AWS services and can easily be integrated with applications built on AWS infrastructure. HBase, being an Apache Hadoop project, is primarily used in big data ecosystems and integrates well with Hadoop and other Hadoop-compatible tools.

Amazon DynamoDB vs HBase

Overview