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

Amazon Athena vs Apache Flink vs Apache Spark

OverviewComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49

Amazon Athena vs Apache Flink vs Apache Spark: What are the differences?

# Key Differences Between Amazon Athena and Apache Flink and Apache Spark

<Write Introduction here>

1. **Data Processing Model**: Amazon Athena is more suitable for ad-hoc querying of data stored in Amazon S3, while Apache Flink and Apache Spark are used for complex stream processing and batch processing tasks, respectively.
2. **Deployment**: Amazon Athena is a fully managed service, which means users don't have to provision or manage any infrastructure, while Apache Flink and Apache Spark require setting up and managing clusters for deployment.
3. **Real-time Processing**: Apache Flink is known for its low-latency and high-throughput real-time stream processing capabilities, making it a popular choice for real-time data processing tasks, while Apache Spark focuses more on batch processing, although it also supports real-time processing.
4. **Programming Language Support**: Apache Flink supports Java, Scala, and Python, while Apache Spark provides support for Java, Scala, Python, and R. In contrast, Amazon Athena primarily uses SQL for querying data.
5. **Data Storage Support**: Amazon Athena is tightly integrated with Amazon S3 for data storage, making it easy to query data directly from S3, whereas Apache Flink and Apache Spark have more flexibility in terms of data sources and can work with various storage systems.
6. **Cost**: Amazon Athena follows a pay-per-query pricing model, which can be cost-effective for ad-hoc querying, while Apache Flink and Apache Spark require upfront infrastructure investment for cluster setup, which may result in higher costs for long-running processing tasks.

In Summary, Amazon Athena is ideal for ad-hoc querying on Amazon S3, Apache Flink excels in real-time stream processing, and Apache Spark is well-suited for batch processing tasks.

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Detailed Comparison

Apache Spark
Apache Spark
Apache Flink
Apache Flink
Amazon Athena
Amazon Athena

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.

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.

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.

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
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
-
Statistics
GitHub Stars
42.2K
GitHub Stars
25.4K
GitHub Stars
-
GitHub Forks
28.9K
GitHub Forks
13.7K
GitHub Forks
-
Stacks
3.1K
Stacks
534
Stacks
519
Followers
3.5K
Followers
879
Followers
840
Votes
140
Votes
38
Votes
49
Pros & Cons
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
    Unified batch and stream processing
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 8
    Easy to use streaming apis
  • 4
    Open Source
  • 2
    Low latency
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
No integrations available
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka
Amazon S3
Amazon S3
Presto
Presto

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

Presto

Presto

Distributed SQL Query Engine for Big Data

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.

AWS Glue

AWS Glue

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

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