Amazon Kinesis Firehose vs AWS Data Pipeline

Amazon Kinesis Firehose
Amazon Kinesis Firehose

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AWS Data Pipeline
AWS Data Pipeline

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Amazon Kinesis Firehose vs AWS Data Pipeline: What are the differences?

What is Amazon Kinesis Firehose? Simple and Scalable Data Ingestion. Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.

What is AWS Data Pipeline? Process and move data between different AWS compute and storage services. AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.

Amazon Kinesis Firehose can be classified as a tool in the "Real-time Data Processing" category, while AWS Data Pipeline is grouped under "Data Transfer".

Some of the features offered by Amazon Kinesis Firehose are:

  • Easy-to-Use
  • Integrated with AWS Data Stores
  • Automatic Elasticity

On the other hand, AWS Data Pipeline provides the following key features:

  • You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.
  • Hourly analysis of Amazon S3‐based log data
  • Daily replication of AmazonDynamoDB data to Amazon S3
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    What is Amazon Kinesis Firehose?

    Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.

    What is AWS Data Pipeline?

    AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.
    What companies use Amazon Kinesis Firehose?
    What companies use AWS Data Pipeline?

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    What tools integrate with Amazon Kinesis Firehose?
    What tools integrate with AWS Data Pipeline?
    What are some alternatives to Amazon Kinesis Firehose and AWS Data Pipeline?
    Stream
    Stream allows you to build scalable feeds, activity streams, and chat. Stream’s simple, yet powerful API’s and SDKs are used by some of the largest and most popular applications for feeds and chat. SDKs available for most popular languages.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Amazon Kinesis
    Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
    Google Cloud Dataflow
    Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
    See all alternatives
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