Apache Impala vs MongoDB

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Apache Impala

110
230
+ 1
10
MongoDB

62.9K
51.9K
+ 1
4.1K
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Apache Impala vs MongoDB: What are the differences?

What is Apache Impala? Real-time Query for Hadoop. 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.

What is MongoDB? The database for giant ideas. 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.

Apache Impala can be classified as a tool in the "Big Data Tools" category, while MongoDB is grouped under "Databases".

"Super fast" is the primary reason why developers consider Apache Impala over the competitors, whereas "Document-oriented storage" was stated as the key factor in picking MongoDB.

Apache Impala and MongoDB are both open source tools. MongoDB with 19.7K GitHub stars and 4.79K forks on GitHub appears to be more popular than Apache Impala with 3 GitHub stars and 4 GitHub forks.

According to the StackShare community, MongoDB has a broader approval, being mentioned in 3943 company stacks & 50403 developers stacks; compared to Apache Impala, which is listed in 18 company stacks and 87 developer stacks.

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Pros of Apache Impala
Pros of MongoDB
  • 10
    Super fast
  • 823
    Document-oriented storage
  • 590
    No sql
  • 546
    Ease of use
  • 465
    Fast
  • 405
    High performance
  • 255
    Free
  • 215
    Open source
  • 179
    Flexible
  • 142
    Replication & high availability
  • 109
    Easy to maintain
  • 41
    Querying
  • 37
    Easy scalability
  • 36
    Auto-sharding
  • 35
    High availability
  • 31
    Map/reduce
  • 26
    Document database
  • 24
    Easy setup
  • 24
    Full index support
  • 15
    Reliable
  • 14
    Fast in-place updates
  • 13
    Agile programming, flexible, fast
  • 11
    No database migrations
  • 7
    Enterprise
  • 7
    Easy integration with Node.Js
  • 5
    Enterprise Support
  • 4
    Great NoSQL DB
  • 3
    Aggregation Framework
  • 3
    Support for many languages through different drivers
  • 3
    Drivers support is good
  • 2
    Schemaless
  • 2
    Fast
  • 2
    Awesome
  • 2
    Managed service
  • 2
    Easy to Scale
  • 1
    Consistent

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Cons of Apache Impala
Cons of MongoDB
    Be the first to leave a con
    • 5
      Very slowly for connected models that require joins
    • 3
      Not acid compliant
    • 1
      Proprietary query language

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    What is Apache Impala?

    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.

    What is 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.

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    What companies use Apache Impala?
    What companies use MongoDB?
    See which teams inside your own company are using Apache Impala or MongoDB.
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    What tools integrate with Apache Impala?
    What tools integrate with MongoDB?

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    Blog Posts

    Dec 8 2020 at 5:50PM

    DigitalOcean

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    What are some alternatives to Apache Impala and MongoDB?
    Presto
    Distributed SQL Query Engine for Big Data
    Apache Drill
    Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
    Apache Hive
    Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    HBase
    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.
    See all alternatives