Apache Hive vs Presto vs Apache Spark

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Apache Hive

479
475
+ 1
0
Presto

394
1K
+ 1
66
Apache Spark

3K
3.5K
+ 1
140
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Pros of Apache Hive
Pros of Presto
Pros of Apache Spark
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    • 18
      Works directly on files in s3 (no ETL)
    • 13
      Open-source
    • 12
      Join multiple databases
    • 10
      Scalable
    • 7
      Gets ready in minutes
    • 6
      MPP
    • 61
      Open-source
    • 48
      Fast and Flexible
    • 8
      One platform for every big data problem
    • 8
      Great for distributed SQL like applications
    • 6
      Easy to install and to use
    • 3
      Works well for most Datascience usecases
    • 2
      Interactive Query
    • 2
      Machine learning libratimery, Streaming in real
    • 2
      In memory Computation

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    Cons of Apache Hive
    Cons of Presto
    Cons of Apache Spark
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        • 4
          Speed

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        96
        19
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        3.2K
        982
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        What is 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.

        What is Presto?

        Distributed SQL Query Engine for Big Data

        What is 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.

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        What companies use Apache Hive?
        What companies use Presto?
        What companies use Apache Spark?

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        What tools integrate with Apache Hive?
        What tools integrate with Presto?
        What tools integrate with Apache Spark?

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        What are some alternatives to Apache Hive, Presto, and Apache Spark?
        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.
        Hadoop
        The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
        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.
        Pig
        Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce.
        Snowflake
        Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
        See all alternatives