Apache Impala vs HBase

Overview

Apache Impala

Stacks145

Followers301

Votes18

GitHub Stars34

Forks33

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

Apache Impala vs HBase: What are the differences?

Introduction

When comparing Apache Impala and HBase, it is important to understand the key differences between these technologies to determine which one best suits your needs.

Data Model: Apache Impala is a SQL engine that supports querying data stored in various formats like Parquet and Avro, while HBase is a NoSQL database that stores data in key-value pairs. Impala can query existing data in HDFS or cloud storage directly, whereas HBase requires data to be ingested into its tables.
Querying Performance: Apache Impala is optimized for interactive SQL queries and offers real-time query responses due to its in-memory processing capabilities. On the other hand, HBase provides low-latency access to single rows of data making it suitable for random read/write operations but might not be as efficient for complex analytical queries.
Consistency: HBase provides strong consistency guarantees for data storage and retrieval operations, ensuring the latest data is always accessible. In contrast, Impala does not guarantee strong consistency across distributed queries, which means eventual consistency might be observed in certain scenarios.
Scalability: Apache Impala supports scaling out horizontally by adding more nodes to the cluster to handle increased workloads efficiently. HBase also offers horizontal scalability but requires careful consideration of schema design and partitioning to achieve optimal performance as the data grows.
Use Cases: Apache Impala is best suited for ad-hoc analytical queries and interactive SQL processing where real-time responses are required. In contrast, HBase is ideal for fast data retrieval applications, time-series data, and use cases that demand low-latency access but can compromise on complex analytics.

In Summary, Apache Impala focuses on interactive SQL querying with in-memory processing, while HBase is a NoSQL database optimized for low-latency key-value access and strong consistency guarantees.

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Detailed Comparison

Apache Impala	HBase
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.	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.
Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse	-
Statistics
GitHub Stars 34	GitHub Stars 5.5K
GitHub Forks 33	GitHub Forks 3.4K
Stacks 145	Stacks 511
Followers 301	Followers 498
Votes 18	Votes 15
Pros & Cons
Pros 11 Super fast 1 Scalability 1 Replication 1 Open Sourse 1 Load Balancing	Pros 9 Performance 5 OLTP 1 Fast Point Queries
Integrations
Hadoop Mode Redash Apache Kudu	No integrations available

What are some alternatives to Apache Impala, 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.

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Apache Impala vs HBase: What are the differences?

Introduction

When comparing Apache Impala and HBase, it is important to understand the key differences between these technologies to determine which one best suits your needs.

Data Model: Apache Impala is a SQL engine that supports querying data stored in various formats like Parquet and Avro, while HBase is a NoSQL database that stores data in key-value pairs. Impala can query existing data in HDFS or cloud storage directly, whereas HBase requires data to be ingested into its tables.
Querying Performance: Apache Impala is optimized for interactive SQL queries and offers real-time query responses due to its in-memory processing capabilities. On the other hand, HBase provides low-latency access to single rows of data making it suitable for random read/write operations but might not be as efficient for complex analytical queries.
Consistency: HBase provides strong consistency guarantees for data storage and retrieval operations, ensuring the latest data is always accessible. In contrast, Impala does not guarantee strong consistency across distributed queries, which means eventual consistency might be observed in certain scenarios.
Scalability: Apache Impala supports scaling out horizontally by adding more nodes to the cluster to handle increased workloads efficiently. HBase also offers horizontal scalability but requires careful consideration of schema design and partitioning to achieve optimal performance as the data grows.
Use Cases: Apache Impala is best suited for ad-hoc analytical queries and interactive SQL processing where real-time responses are required. In contrast, HBase is ideal for fast data retrieval applications, time-series data, and use cases that demand low-latency access but can compromise on complex analytics.

In Summary, Apache Impala focuses on interactive SQL querying with in-memory processing, while HBase is a NoSQL database optimized for low-latency key-value access and strong consistency guarantees.