Amazon Kinesis Firehose vs Google Cloud Dataflow

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Amazon Kinesis Firehose

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Amazon Kinesis Firehose vs Google Cloud Dataflow: What are the differences?


This Markdown code provides a comparison between Amazon Kinesis Firehose and Google Cloud Dataflow in terms of key differences.

  1. Pricing Model: Amazon Kinesis Firehose has a pricing model based on data volume ingested and time spent. In contrast, Google Cloud Dataflow offers a pricing model based on the number of CPU hours utilized for data processing.
  2. Real-time vs Batch Processing: Amazon Kinesis Firehose is primarily designed for real-time data streaming and loading into data lakes, data stores, or analytics services. On the other hand, Google Cloud Dataflow focuses on batch and stream processing capabilities, enabling the handling of both real-time and batch workloads.
  3. Managed Service vs Data Processing Framework: Amazon Kinesis Firehose is a fully managed service, handling scalability, durability, and reliability aspects. In comparison, Google Cloud Dataflow is a data processing framework that provides flexibility in building and customizing data processing pipelines.
  4. Integration with AWS vs Google Ecosystem: Amazon Kinesis Firehose seamlessly integrates with various AWS services, allowing easy integration into the broader AWS ecosystem. In contrast, Google Cloud Dataflow integrates well with the Google Cloud Platform, leveraging its services and features.
  5. Latency and Throughput: Amazon Kinesis Firehose provides low-latency data delivery with near real-time processing capabilities, suitable for scenarios requiring quick insights. Google Cloud Dataflow focuses on high-throughput processing, making it suitable for handling large-scale data processing tasks efficiently.
  6. Development Complexity and Flexibility: Amazon Kinesis Firehose offers a simpler setup and configuration process with limited customization options for data processing logic. Google Cloud Dataflow provides more development flexibility, allowing users to write custom code in different programming languages, providing greater control over the processing logic.

In Summary, Amazon Kinesis Firehose focuses on real-time streaming, simpler managed service, and seamless integration with AWS ecosystem, while Google Cloud Dataflow offers flexibility in building custom data processing pipelines, supports both batch and stream processing, and integrates well with Google Cloud Platform.

Decisions about Amazon Kinesis Firehose and Google Cloud Dataflow
Ryan Wans

Because we're getting continuous data from a variety of mediums and sources, we need a way to ingest data, process it, analyze it, and store it in a robust manner. AWS' tools provide just that. They make it easy to set up a data ingestion pipeline for handling gigabytes of data per second. GraphQL makes it easy for the front end to just query an API and get results in an efficient fashion, getting only the data we need. SwaggerHub makes it easy to make standardized OpenAPI's with consistent and predictable behavior.

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Roel van den Brand
Lead Developer at Di-Vision Consultion · | 3 upvotes · 19.5K views

Use case for ingressing a lot of data and post-process the data and forward it to multiple endpoints.

Kinesis can ingress a lot of data easier without have to manage scaling in DynamoDB (ondemand would be too expensive) We looked at DynamoDB Streams to hook up with Lambda, but Kinesis provides the same, and a backup incoming data to S3 with Firehose instead of using the TTL in DynamoDB.

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Pros of Amazon Kinesis Firehose
Pros of Google Cloud Dataflow
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      Unified batch and stream processing
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      Fully managed
    • 3
      Throughput Transparency

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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 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.

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    What tools integrate with Amazon Kinesis Firehose?
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    What are some alternatives to Amazon Kinesis Firehose and Google Cloud Dataflow?
    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 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.
    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
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