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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Impala vs MongoDB

Apache Impala vs MongoDB

OverviewComparisonAlternatives

Overview

Apache Impala
Apache Impala
Stacks145
Followers301
Votes18
GitHub Stars34
Forks33
MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K

Apache Impala vs MongoDB: What are the differences?

# Introduction

Apache Impala and MongoDB are both popular data processing technologies used in big data analytics and management. However, they differ in various aspects that make them suitable for different use cases.

1. **Data Storage Model**: Apache Impala is a SQL query engine for Apache Hadoop, which allows for real-time querying of data stored in Hadoop. On the other hand, MongoDB is a NoSQL database that stores data in JSON-like documents, providing high flexibility for unstructured data.

2. **Data Schema**: Apache Impala requires a predefined schema for data stored in Hadoop, making it suitable for structured data analysis. MongoDB, being a NoSQL database, allows for schema-less data storage, making it more adaptable for evolving data structures.

3. **Query Language**: Apache Impala uses SQL for querying data, making it easier for users familiar with SQL syntax to perform data analysis tasks. MongoDB, on the other hand, uses a rich query language that supports document-based queries, ideal for working with JSON-like data.

4. **Scalability**: Apache Impala is designed for real-time querying of data stored in Hadoop, providing horizontal scalability by adding more nodes to the cluster. MongoDB also offers horizontal scalability by sharding data across multiple nodes, making it suitable for large-scale applications.

5. **Consistency**: Apache Impala provides strong consistency guarantees for querying data in Hadoop, ensuring that users get accurate results. MongoDB, being a NoSQL database, offers eventual consistency, which may lead to discrepancies in data retrieval under certain conditions.

6. **Use Case**: Apache Impala is well-suited for interactive data analysis tasks that require real-time querying of large datasets stored in Hadoop. MongoDB, on the other hand, is often used for applications that demand flexible data structures and high availability for web and mobile applications.

In Summary, Apache Impala and MongoDB differ in their data storage model, schema requirements, query language, scalability options, consistency guarantees, and ideal use cases.

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Detailed Comparison

Apache Impala
Apache Impala
MongoDB
MongoDB

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.

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.

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
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars
34
GitHub Stars
27.7K
GitHub Forks
33
GitHub Forks
5.7K
Stacks
145
Stacks
96.6K
Followers
301
Followers
82.0K
Votes
18
Votes
4.1K
Pros & Cons
Pros
  • 11
    Super fast
  • 1
    Scalability
  • 1
    Distributed
  • 1
    High Performance
  • 1
    Open Sourse
Pros
  • 829
    Document-oriented storage
  • 594
    No sql
  • 554
    Ease of use
  • 465
    Fast
  • 410
    High performance
Cons
  • 6
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 2
    Proprietary query language
Integrations
Hadoop
Hadoop
Mode
Mode
Redash
Redash
Apache Kudu
Apache Kudu
No integrations available

What are some alternatives to Apache Impala, MongoDB?

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.

ArangoDB

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.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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