Amazon Athena vs HBase

Overview

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs HBase: What are the differences?

Introduction

In this article, we will discuss the key differences between Amazon Athena and HBase.

Scalability: Amazon Athena is a serverless analytical query service that allows users to query data on Amazon S3 using standard SQL. It scales automatically to handle large datasets and can handle multiple concurrent queries efficiently. On the other hand, HBase is a distributed, scalable, and consistent NoSQL database built on top of Hadoop that can handle large amounts of structured and semi-structured data. It uses sharding and replication techniques to achieve scalability.
Data Storage and Retrieval: Amazon Athena stores data in Amazon S3, which provides high durability, scalability, and cost-effective storage. It allows users to perform ad-hoc queries on the data stored in S3 without the need for any infrastructure setup or management. HBase, on the other hand, stores data in a distributed manner across multiple Hadoop DataNodes. It provides fast random read/write access to the data using a key-value store mechanism.
Data Model: Amazon Athena supports a schema-on-read data model, which means that the data does not need to be structured or indexed before querying. It can query different file formats like CSV, JSON, Parquet, and Avro. HBase, on the other hand, uses a schema-on-write data model, which requires defining a data schema and creating tables before storing the data. It supports structured data with flexible column families.
Query Language: Amazon Athena uses standard SQL for querying data stored in S3. It provides a rich set of SQL functions and allows users to write complex queries with joins, aggregations, and window functions. HBase, on the other hand, provides a Java API for querying data using scan and get operations. It requires writing custom code to perform complex queries.
Consistency and Transactions: Amazon Athena provides read-after-write consistency for the data stored in S3. It supports ACID transactions for data modifications using AWS Glue DataBrew. HBase, on the other hand, provides eventual consistency for the data stored in HDFS. It does not natively support ACID transactions but can be achieved using the Apache HBase-WAL mechanism.
Cost: Amazon Athena follows a pay-per-query pricing model, where users only pay for the queries they execute. It does not require any upfront cost or infrastructure provisioning. In contrast, HBase requires setting up a Hadoop cluster and managing the infrastructure, which can incur additional costs for hardware, maintenance, and administration.

In summary, Amazon Athena is a serverless analytical query service that scales automatically, stores data in S3, supports a schema-on-read data model, uses SQL for querying, provides strong consistency, and follows a pay-per-query pricing model. On the other hand, HBase is a distributed NoSQL database that scales by sharding and replication, stores data in HDFS, uses a schema-on-write data model, provides a Java API for querying, provides eventual consistency, and requires infrastructure setup and management.

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Advice on HBase, Amazon Athena

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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Detailed Comparison

HBase	Amazon Athena
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.	Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
Statistics
GitHub Stars 5.5K	GitHub Stars -
GitHub Forks 3.4K	GitHub Forks -
Stacks 511	Stacks 521
Followers 498	Followers 840
Votes 15	Votes 49
Pros & Cons
Pros 9 Performance 5 OLTP 1 Fast Point Queries	Pros 16 Use SQL to analyze CSV files 8 Glue crawlers gives easy Data catalogue 7 Cheap 6 Query all my data without running servers 24x7 4 No data base servers yay
Integrations
No integrations available	Amazon S3 Presto

What are some alternatives to HBase, Amazon Athena?

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 Athena vs HBase: What are the differences?

Introduction

In this article, we will discuss the key differences between Amazon Athena and HBase.

Scalability: Amazon Athena is a serverless analytical query service that allows users to query data on Amazon S3 using standard SQL. It scales automatically to handle large datasets and can handle multiple concurrent queries efficiently. On the other hand, HBase is a distributed, scalable, and consistent NoSQL database built on top of Hadoop that can handle large amounts of structured and semi-structured data. It uses sharding and replication techniques to achieve scalability.
Data Storage and Retrieval: Amazon Athena stores data in Amazon S3, which provides high durability, scalability, and cost-effective storage. It allows users to perform ad-hoc queries on the data stored in S3 without the need for any infrastructure setup or management. HBase, on the other hand, stores data in a distributed manner across multiple Hadoop DataNodes. It provides fast random read/write access to the data using a key-value store mechanism.
Data Model: Amazon Athena supports a schema-on-read data model, which means that the data does not need to be structured or indexed before querying. It can query different file formats like CSV, JSON, Parquet, and Avro. HBase, on the other hand, uses a schema-on-write data model, which requires defining a data schema and creating tables before storing the data. It supports structured data with flexible column families.
Query Language: Amazon Athena uses standard SQL for querying data stored in S3. It provides a rich set of SQL functions and allows users to write complex queries with joins, aggregations, and window functions. HBase, on the other hand, provides a Java API for querying data using scan and get operations. It requires writing custom code to perform complex queries.
Consistency and Transactions: Amazon Athena provides read-after-write consistency for the data stored in S3. It supports ACID transactions for data modifications using AWS Glue DataBrew. HBase, on the other hand, provides eventual consistency for the data stored in HDFS. It does not natively support ACID transactions but can be achieved using the Apache HBase-WAL mechanism.
Cost: Amazon Athena follows a pay-per-query pricing model, where users only pay for the queries they execute. It does not require any upfront cost or infrastructure provisioning. In contrast, HBase requires setting up a Hadoop cluster and managing the infrastructure, which can incur additional costs for hardware, maintenance, and administration.

Amazon Athena vs HBase

Overview

Amazon Athena vs HBase: What are the differences?