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  1. Stackups
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  4. Big Data Tools
  5. Amazon Athena vs Apache Hive vs Apache Spark

Amazon Athena vs Apache Hive vs Apache Spark

OverviewComparisonAlternatives

Overview

Apache Hive
Apache Hive
Stacks488
Followers475
Votes0
GitHub Stars5.9K
Forks4.8K
Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49

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Detailed Comparison

Apache Hive
Apache Hive
Apache Spark
Apache Spark
Amazon Athena
Amazon Athena

Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.

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.

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Built on top of Apache Hadoop; Tools to enable easy access to data via SQL; Support for extract/transform/load (ETL), reporting, and data analysis; Access to files stored either directly in Apache HDFS and HBase; Query execution using Apache Hadoop MapReduce, Tez or Spark frameworks
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
-
Statistics
GitHub Stars
5.9K
GitHub Stars
42.2K
GitHub Stars
-
GitHub Forks
4.8K
GitHub Forks
28.9K
GitHub Forks
-
Stacks
488
Stacks
3.1K
Stacks
519
Followers
475
Followers
3.5K
Followers
840
Votes
0
Votes
140
Votes
49
Pros & Cons
No community feedback yet
Pros
  • 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
Cons
  • 4
    Speed
Pros
  • 16
    Use SQL to analyze CSV files
  • 8
    Glue crawlers gives easy Data catalogue
  • 7
    Cheap
  • 6
    Query all my data without running servers 24x7
  • 4
    No data base servers yay
Integrations
Hadoop
Hadoop
HBase
HBase
No integrations available
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Apache Hive, Apache Spark, Amazon Athena?

Presto

Presto

Distributed SQL Query Engine for Big Data

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

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.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

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.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Apache Kudu

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.

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