Amazon Kinesis聽vs聽Google Cloud Dataflow

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

Amazon Kinesis and Google Cloud Dataflow are both popular data processing platforms that provide real-time and batch streaming capabilities. Let's explore the key differences between them:

  1. Data Processing Model: In Amazon Kinesis, data processing is event-driven and real-time, allowing users to process and analyze streaming data in real-time using various computational resources. On the other hand, Google Cloud Dataflow follows a batch-oriented data processing model, allowing users to process and analyze data in fixed intervals or batches.

  2. Latency: Amazon Kinesis is known for its low latency processing, which enables real-time data ingestion and analytics. In contrast, Google Cloud Dataflow has a slightly higher latency due to its batch processing nature, which processes data in fixed intervals.

  3. Ease of Use: Amazon Kinesis provides a simple and easy-to-use interface, making it user-friendly for developers and data engineers. Google Cloud Dataflow, on the other hand, offers a more advanced and feature-rich interface that might require a steeper learning curve for beginners.

  4. Integration with Ecosystem: Amazon Kinesis is tightly integrated with the Amazon Web Services (AWS) ecosystem, allowing users to easily connect and integrate their data pipelines with other AWS services like Amazon S3 and Amazon Redshift. In contrast, Google Cloud Dataflow is part of the larger Google Cloud Platform (GCP) ecosystem, providing seamless integration with other GCP services like BigQuery and Cloud Storage.

  5. Scalability and Elasticity: Both Amazon Kinesis and Google Cloud Dataflow offer scalability and elasticity to handle large volumes of data. However, Amazon Kinesis provides automatic scaling capabilities, allowing users to handle sudden spikes in data ingestion more efficiently. Google Cloud Dataflow, on the other hand, requires users to manage the scaling aspects manually.

  6. Pricing Model: Amazon Kinesis follows a pay-as-you-go pricing model, where users are charged based on the number of records ingested, data processed, and data transferred. In contrast, Google Cloud Dataflow utilizes a resource-based pricing model, where users are billed based on the resources consumed during the data processing.

In summary, Amazon Kinesis, offers services like Kinesis Data Streams and Kinesis Data Analytics, while Google Cloud Dataflow, part of Google Cloud Platform, provides a unified stream and batch processing model with Apache Beam.

Decisions about Amazon Kinesis 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 路 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
Pros of Google Cloud Dataflow
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    Unified batch and stream processing
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    Fully managed
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    Throughput Transparency

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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 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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    Blog Posts

    Jul 2 2019 at 9:34PM


    Google AnalyticsAmazon S3New Relic+25
    GitHubDockerAmazon EC2+23
    What are some alternatives to Amazon Kinesis and Google Cloud Dataflow?
    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鈥檙e already using today.
    Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
    See all alternatives