Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Amazon Athena

Stacks519

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.

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.
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.
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.
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.
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.
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

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

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Comments

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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

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/.

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Comments

Detailed Comparison

Apache Flink	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 519
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 Hadoop HBase Kafka	Amazon S3 Presto

What are some alternatives to Apache Flink, Amazon Athena?

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

Distributed SQL Query Engine for Big Data

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 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™ 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

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

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

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

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.