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


AWS Data Pipeline and Amazon Kinesis are two widely used services provided by Amazon Web Services (AWS) for processing and managing data in various scenarios. While both services are designed for data processing, they differ in their functionalities and use cases. In this article, we will explore the key differences between AWS Data Pipeline and Amazon Kinesis.

  1. Data Processing Paradigm: The main difference between AWS Data Pipeline and Amazon Kinesis lies in their data processing paradigms. AWS Data Pipeline is a batch-oriented data processing service that enables you to orchestrate and automate data workflows. It is suitable for scenarios where data processing can be performed in a batch mode, such as daily data processing tasks or data warehousing. On the other hand, Amazon Kinesis is a real-time streaming data platform that allows you to ingest, process, and analyze data in real-time. It is ideal for scenarios where you need to process and react to data in real-time, such as real-time analytics or event-driven architectures.

  2. Data Source and Destination: Another key difference between AWS Data Pipeline and Amazon Kinesis is their data source and destination capabilities. AWS Data Pipeline can consume data from various sources, including AWS S3, RDS, DynamoDB, and others. It provides built-in connectors to extract data from these sources and load it into destinations like Redshift, S3, or even custom storage solutions. On the other hand, Amazon Kinesis primarily ingests data from streaming sources like IoT devices, social media platforms, or clickstream events. It allows you to process and analyze the data in real-time using services like Kinesis Data Streams, Kinesis Data Firehose, or Kinesis Data Analytics.

  3. Data Processing Latency: When it comes to data processing latency, AWS Data Pipeline and Amazon Kinesis exhibit different behaviors. AWS Data Pipeline operates in a batch mode, which means it is optimized for processing large volumes of data over a longer time span. It provides capabilities for data validation, transformation, and complex workflows but may introduce latency if real-time processing is required. On the other hand, Amazon Kinesis is designed for real-time data processing and analysis. It aims to minimize latency and provides near real-time processing capabilities, enabling you to react to data in near real-time.

  4. Scaling and Elasticity: AWS Data Pipeline and Amazon Kinesis also differ in terms of scaling and elasticity. AWS Data Pipeline supports automatic scaling of resources based on the demand of your data processing workflows. However, the scalability is more focused on the parallel execution of tasks rather than handling high throughput or real-time scenarios. Amazon Kinesis, on the other hand, is built for elastic and scalable data processing. It can handle high throughput scenarios where millions of events can be ingested, processed, and analyzed in real-time.

  5. Data Retention and Durability: When it comes to data retention and durability, AWS Data Pipeline and Amazon Kinesis exhibit different characteristics. AWS Data Pipeline does not provide built-in data retention or durability features, as it mainly orchestrates data workflows between different services. The durability and retention of data depend on the underlying storage services used within the pipeline. In contrast, Amazon Kinesis provides built-in data retention capabilities that allow you to automatically store data streams for a specified retention period. It also offers data replication across multiple availability zones to ensure durability and high availability.

  6. Use Cases and Scenarios: AWS Data Pipeline and Amazon Kinesis have different use cases and scenarios where they excel. AWS Data Pipeline is well-suited for scenarios that involve complex data processing workflows and batch-oriented data processing, such as data transformation, data aggregation, or ETL (Extract, Transform, Load) processes. It is commonly used for data warehousing, backup and restore procedures, or managing data-driven pipelines. On the other hand, Amazon Kinesis is designed for real-time streaming use cases, including real-time analytics, monitoring and alerting, IoT data ingestion and processing, or building event-driven architectures.

In Summary, AWS Data Pipeline is a batch-oriented data processing service suitable for complex data workflows, while Amazon Kinesis is a real-time streaming data platform designed for ingesting, processing, and analyzing data in real-time.

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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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    Blog Posts

    Jul 2 2019 at 9:34PM


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    What are some alternatives to Amazon Kinesis and AWS Data Pipeline?
    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 is both a Rack application and JavaScript library that makes building real-time web applications possible.
    See all alternatives