Apache Kudu vs Presto vs Apache Spark

Need advice about which tool to choose?Ask the StackShare community!

Apache Kudu

62
214
+ 1
9
Presto

345
870
+ 1
63
Apache Spark

2.5K
2.9K
+ 1
132
Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of Apache Kudu
Pros of Presto
Pros of Apache Spark
  • 9
    Realtime Analytics
  • 17
    Works directly on files in s3 (no ETL)
  • 13
    Open-source
  • 11
    Join multiple databases
  • 10
    Scalable
  • 7
    Gets ready in minutes
  • 5
    MPP
  • 58
    Open-source
  • 48
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query

Sign up to add or upvote prosMake informed product decisions

Cons of Apache Kudu
Cons of Presto
Cons of Apache Spark
  • 0
    Restart time
    Be the first to leave a con
    • 3
      Speed

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is Apache Kudu?

    A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

    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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Apache Kudu?
    What companies use Presto?
    What companies use Apache Spark?

    Sign up to get full access to all the companiesMake informed product decisions

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

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    Mar 24 2021 at 12:57PM

    Pinterest

    GitJenkinsKafka+7
    3
    1689
    MySQLKafkaApache Spark+6
    2
    1637
    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    5
    2179
    What are some alternatives to Apache Kudu, Presto, and Apache Spark?
    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.
    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.
    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.
    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.
    Druid
    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
    See all alternatives