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Apache Flink VS Amazon Athena

Compare Apache Flink VS Amazon Athena and see what are their differences

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Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Amazon Athena logo Amazon Athena

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.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • Amazon Athena Landing page
    Landing page //
    2023-03-17

Apache Flink features and specs

  • Real-time Stream Processing
    Apache Flink is designed for real-time data streaming, offering low-latency processing capabilities that are essential for applications requiring immediate data insights.
  • Event Time Processing
    Flink supports event time processing, which allows it to handle out-of-order events effectively and provide accurate results based on the time events actually occurred rather than when they were processed.
  • State Management
    Flink provides robust state management features, making it easier to maintain and query state across distributed nodes, which is crucial for managing long-running applications.
  • Fault Tolerance
    The framework includes built-in mechanisms for fault tolerance, such as consistent checkpoints and savepoints, ensuring high reliability and data consistency even in the case of failures.
  • Scalability
    Apache Flink is highly scalable, capable of handling both batch and stream processing workloads across a distributed cluster, making it suitable for large-scale data processing tasks.
  • Rich Ecosystem
    Flink has a rich set of APIs and integrations with other big data tools, such as Apache Kafka, Apache Hadoop, and Apache Cassandra, enhancing its versatility and ease of integration into existing data pipelines.

Possible disadvantages of Apache Flink

  • Complexity
    Flink’s advanced features and capabilities come with a steep learning curve, making it more challenging to set up and use compared to simpler stream processing frameworks.
  • Resource Intensive
    The framework can be resource-intensive, requiring substantial memory and CPU resources for optimal performance, which might be a concern for smaller setups or cost-sensitive environments.
  • Community Support
    While growing, the community around Apache Flink is not as large or mature as some other big data frameworks like Apache Spark, potentially limiting the availability of community-contributed resources and support.
  • Ecosystem Maturity
    Despite its integrations, the Flink ecosystem is still maturing, and certain tools and plugins may not be as developed or stable as those available for more established frameworks.
  • Operational Overhead
    Running and maintaining a Flink cluster can involve significant operational overhead, including monitoring, scaling, and troubleshooting, which might require a dedicated team or additional expertise.

Amazon Athena features and specs

  • Serverless
    Athena is serverless, which means there's no need to set up or manage any infrastructure. You can start querying data immediately without worrying about managing underlying servers.
  • Pay-as-you-go
    You only pay for the queries you run, and the cost is based on the amount of data scanned by the queries. This is cost-effective, especially for infrequent querying.
  • Scalable
    Athena scales automatically, enabling it to handle large datasets and concurrent queries efficiently, without manual intervention.
  • Integration with AWS ecosystem
    Athena integrates seamlessly with other AWS services like S3, Glue, and QuickSight, making it easy to build comprehensive data pipelines and analytics solutions.
  • Supports standard SQL
    Athena uses standard SQL for querying, which makes it easy for users familiar with SQL to get started quickly.
  • Quick to deploy
    Since there is no infrastructure to manage, you can start querying your data within minutes of setting up Athena.
  • Supports a variety of data formats
    Athena supports multiple data formats including CSV, JSON, ORC, Avro, and Parquet, providing flexibility in data ingestion and storage.

Possible disadvantages of Amazon Athena

  • Cost of scanning large datasets
    While the pay-as-you-go model is beneficial, querying large datasets frequently can become expensive.
  • Performance
    For very complex queries or extremely large datasets, Athena's performance might not match that of a dedicated data warehouse solution.
  • Limited built-in visualization
    Athena does not provide built-in data visualization tools, so you'll need to integrate with other services like QuickSight or third-party tools for visual analytics.
  • Learning curve for optimal usage
    Even though Athena supports SQL, optimizing performance and cost efficiency might require a good understanding of how Athena processes data.
  • Data preparation
    Data might require preprocessing or organization in a specific way for optimal performance with Athena, which could add to the setup time and complexity.
  • Cold start latency
    Athena can experience latency during query initiation, known as cold start latency, which can be an issue for time-sensitive analytics.

Analysis of Apache Flink

Overall verdict

  • Yes, Apache Flink is considered a good distributed stream processing framework.

Why this product is good

  • Rich api
    Flink offers a rich set of APIs for various levels of abstraction, catering to different needs of developers.
  • Scalability
    Flink provides excellent horizontal scalability, making it suitable for handling large data streams and high-throughput applications.
  • Fault tolerance
    Flink's checkpointing mechanism ensures fault-tolerance, maintaining data state consistency even after failures.
  • Ease of integration
    Flink integrates well with other big data tools and ecosystems, facilitating broader data architecture designs.
  • Real-time processing
    It excels at processing data in real-time, allowing for immediate insights and action on streaming data.
  • Community and support
    Being a part of the Apache Software Foundation, Flink benefits from a large community and comprehensive documentation.
  • Complex event processing
    It supports complex event processing, which is essential for many real-time applications.

Recommended for

  • real-time analytics
  • stream data processing
  • complex event processing
  • machine learning in streaming applications
  • applications requiring high-throughput and low-latency processing
  • companies looking for robust fault-tolerance in distributed systems

Analysis of Amazon Athena

Overall verdict

  • Amazon Athena is a powerful and flexible tool for users who need a cost-effective, straightforward solution for querying and analyzing data stored in S3 without the overhead of managing servers. Its serverless architecture, scalability, and wide integration with other AWS services make it a reliable choice for quick data analytics tasks.

Why this product is good

  • Amazon Athena is a serverless query service that makes it easy to analyze large-scale datasets directly in Amazon S3 using standard SQL. It is especially advantageous because it is fully managed, meaning there is no need to set up or manage infrastructure. It automatically scales, so users only pay for the queries they run, making it cost-effective for intermittent data analysis tasks. Visualizing data becomes straightforward with its integration with AWS QuickSight or other BI tools. Additionally, its support for a wide range of data formats and ease of use through the AWS Management Console further enhance its appeal for data analysts and developers.

Recommended for

  • Data analysts and data scientists needing fast, ad-hoc querying capabilities.
  • Organizations looking to reduce costs associated with traditional data warehousing.
  • Developers and teams who want to integrate SQL-based data querying into their applications without backend infrastructure management.
  • Businesses using or planning to use AWS S3 for data storage and requiring analysis tools that seamlessly integrate within the AWS ecosystem.

Apache Flink videos

+ Add

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Amazon Athena videos

+ Add

AWS Big Data: What is Amazon Athena?

More videos:

  • Review - Deep Dive on Amazon Athena - AWS Online Tech Talks
  • Review - Deep Dive on Amazon Athena - AWS Online Tech Talks

Category Popularity

0-100% (relative to Apache Flink and Amazon Athena)

Apache Flink

Amazon Athena

Big Data
100 100%
Big Data
0% 0
Databases
45 45%
Databases
55% 55
Stream Processing
100 100%
Stream Processing
0% 0
Database Management
0 0%
Database Management
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apache Flink should be more popular than Amazon Athena. It has been mentiond 45 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Flink mentions (45)

  • Gravitino - the unified metadata lake
    In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / about 2 months ago
  • Towards Sub-100ms Latency Stream Processing with an S3-Based Architecture
    Many stream processing systems today still rely on local disks and RocksDB to manage state. This model has been around for a while and works fine in simple, single-tenant setups. Apache Flink, for example, uses RocksDB as its default state backend - state is kept on local disks, and periodic checkpoints are written to external storage for recovery. - Source: dev.to / 3 months ago
  • Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed
    Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / 3 months ago
  • When plans change at 500 feet: Complex event processing of ADS-B aviation data with Apache Flink
    I wrote a python based aircraft monitor which polls the adsb.fi feed for aircraft transponder messages, and publishes each location update as a new event into an Apache Kafka topic. I used Apache Flink — and more specially Flink SQL, to transform and analyse my flight data. The TL;DR summary is I can write SQL for my real-time data processing queries — and get the scalability, fault tolerance, and low latency... - Source: dev.to / 4 months ago
  • What is Apache Flink? Exploring Its Open Source Business Model, Funding, and Community
    Continuous Learning: Leverage online tutorials from the official Flink website and attend webinars for deeper insights. - Source: dev.to / 5 months ago
View more

Amazon Athena mentions (24)

  • How LayerX Achieves “Painless” Governance and Security in the Cloud
    Logs from AWS CloudTrail, Entra ID, Datadog, and Amazon Athena are aggregated and searchable via APIs and CLI commands. LayerX stores logs in Snowflake, making it easy to visualize and retrieve audit evidence. Log extraction is automated—no more ad hoc queries or manual exports. - Source: dev.to / 3 months ago
  • Vector: A lightweight tool for collecting EKS application logs with long-term storage capabilities
    In this article, we present an architecture that demonstrates how to collect application logs from Amazon Elastic Kubernetes Service (Amazon EKS) via Vector, store them in Amazon Simple Storage Service (Amazon S3) for long-term retention, and finally query these logs using AWS Glue and Amazon Athena. - Source: dev.to / 5 months ago
  • Introducing Iceberg Table Engine in RisingWave: Manage Streaming Data in Iceberg with SQL
    However, Iceberg defines the storage format, leaving the complexities of data ingestion and processing, especially for real-time streams, to separate systems. While query engines like Trino or Athena excel with static datasets, they aren't designed for continuous, low-latency ingestion and transformation of streaming data into Iceberg. This often forces engineers to integrate multiple complex tools, increasing... - Source: dev.to / 6 months ago
  • Deploying a Complete Machine Learning Fraud Detection Solution Using Amazon SageMaker : AWS Project
    SageMaker Feature Store keeps track of the metadata of stored features (e.g. Feature name or version number) so that you can query the features for the right attributes in batches or in real time using Amazon Athena , an interactive query service. - Source: dev.to / 11 months ago
  • Spatial Search of Amazon S3 Express One Zone Data with Amazon Athena and Visualized It in QGIS
    Prepare GIS data for use with Amazon Athena. This time, we created four types of sample data in QGIS in advance. - Source: dev.to / almost 2 years ago
View more

What are some alternatives?

When comparing Apache Flink and Amazon Athena, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

phpMyAdmin - phpMyAdmin is a tool written in PHP intended to handle the administration of MySQL over the Web.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

SQLyog - Webyog develops MySQL database client tools. Monyog MySQL monitor and SQLyog MySQL GUI & admin are trusted by 2.5 million users across the globe.

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Sequel Pro - MySQL database management for Mac OS X

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