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  1. Stackups
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  5. Amazon Athena vs Apache Flink

Amazon Athena vs Apache Flink

OverviewDecisionsComparisonAlternatives

Overview

Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
Amazon Athena
Amazon Athena
Stacks521
Followers840
Votes49

Amazon Athena vs Apache Flink: What are the differences?

Introduction

This Markdown code provides a comparison between Amazon Athena and Apache Flink. Both platforms are used for data processing and analysis, but they have key differences in their capabilities and functionalities.

  1. Query Language: Amazon Athena utilizes SQL-like queries for data analysis and querying, making it easy for those familiar with SQL to use. On the other hand, Apache Flink provides a more flexible and expressive query language called Flink SQL, which supports complex event processing and supports both batch and stream processing.

  2. Processing Model: Amazon Athena is primarily designed for running ad-hoc queries on data stored in Amazon S3, making it a suitable choice for interactive analysis. Apache Flink, on the other hand, is a real-time stream processing framework that allows for continuous data processing and supports advanced operations like stateful processing and event time processing.

  3. Data Source: Amazon Athena is tightly integrated with Amazon S3 and supports querying data directly from S3 buckets. On the other hand, Apache Flink supports multiple data sources, including file systems, databases, messaging systems, and streaming platforms, providing a more versatile solution for data processing.

  4. Scalability: Amazon Athena is a managed service provided by Amazon Web Services, allowing users to scale their queries as needed by provisioning more computing resources. Apache Flink, on the other hand, can be deployed on clusters and is designed to scale horizontally, allowing for large-scale data processing and distributed computing.

  5. Connectivity: Amazon Athena provides built-in connectors to various AWS services, making it easy to integrate with other services in the AWS ecosystem. Apache Flink has a rich ecosystem of connectors and integrations with popular data storage and processing systems, providing seamless connectivity with external systems.

  6. User Interface: Amazon Athena offers a web-based query editor and console for managing and executing queries. Apache Flink provides a web-based dashboard and APIs for managing and monitoring jobs, allowing users to interact with the system programmatically.

In summary, Amazon Athena is a serverless query service optimized for ad-hoc analysis on data stored in Amazon S3, with a focus on simplicity and ease of use. Apache Flink, on the other hand, is a powerful stream processing framework that supports real-time data processing, complex event processing, and advanced analytics, with a strong focus on performance and scalability.

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Advice on Apache Flink, Amazon Athena

Aditya
Aditya

Mar 13, 2021

Review

you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster

220k views220k
Comments
Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments
Kevin
Kevin

Co-founder at Transloadit

Dec 18, 2020

Review

Hey there, the trick to keeping costs under control is to partition. This means you split up your source files by date, and also query within dates, so that Athena only scans the few files necessary for those dates. I hope that makes sense (and I also hope I understood your question right). This article explains better https://aws.amazon.com/blogs/big-data/analyze-your-amazon-cloudfront-access-logs-at-scale/.

5.08k views5.08k
Comments

Detailed Comparison

Apache Flink
Apache Flink
Amazon Athena
Amazon Athena

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.

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
25.4K
GitHub Stars
-
GitHub Forks
13.7K
GitHub Forks
-
Stacks
534
Stacks
521
Followers
879
Followers
840
Votes
38
Votes
49
Pros & Cons
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
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Apache Flink, Amazon Athena?

Apache Spark

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.

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.

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