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Amazon Kinesis

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

Developers describe Amazon Kinesis as "Store and process terabytes of data each hour from hundreds of thousands of sources". 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. On the other hand, AWS Data Pipeline is detailed as "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 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 are:

  • Real-time Processing- Amazon Kinesis enables you to collect and analyze information in real-time, allowing you to answer questions about the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.
  • Easy to use- You can create a new stream, set the throughput requirements, and start streaming data quickly and easily. Amazon Kinesis automatically provisions and manages the storage required to reliably and durably collect your data stream.
  • High throughput. Elastic.- Amazon Kinesis seamlessly scales to match the data throughput rate and volume of your data, from megabytes to terabytes per hour. Amazon Kinesis will scale up or down based on your needs.

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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Pros of Amazon Kinesis
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Cons of Amazon Kinesis
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    What is 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.

    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.

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    What companies use Amazon Kinesis?
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    See which teams inside your own company are using Amazon Kinesis or AWS Data Pipeline.
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    What tools integrate with Amazon Kinesis?
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    Blog Posts

    Jul 2 2019 at 9:34PM

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    What are some alternatives to Amazon Kinesis and AWS Data Pipeline?
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    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.
    Amazon SQS
    Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.
    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.
    Firehose.io
    Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
    See all alternatives