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  5. Amazon Kinesis vs Google Cloud Dataflow

Amazon Kinesis vs Google Cloud Dataflow

OverviewDecisionsComparisonAlternatives

Overview

Amazon Kinesis
Amazon Kinesis
Stacks794
Followers604
Votes9
Google Cloud Dataflow
Google Cloud Dataflow
Stacks219
Followers497
Votes19

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.

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Advice on Amazon Kinesis, Google Cloud Dataflow

Ryan
Ryan

Mar 11, 2021

Decided

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.

23k views23k
Comments
Roel
Roel

Lead Developer at Di-Vision Consultion

Dec 14, 2020

Decided

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.

21k views21k
Comments

Detailed Comparison

Amazon Kinesis
Amazon Kinesis
Google Cloud Dataflow
Google Cloud Dataflow

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 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.

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;Integrate with Amazon S3, Amazon Redshift, and Amazon DynamoDB- With Amazon Kinesis, you can reliably collect, process, and transform all of your data in real-time before delivering it to data stores of your choice, where it can be used by existing or new applications. Connectors enable integration with Amazon S3, Amazon Redshift, and Amazon DynamoDB;Build Kinesis Applications- Amazon Kinesis provides developers with client libraries that enable the design and operation of real-time data processing applications. Just add the Amazon Kinesis Client Library to your Java application and it will be notified when new data is available for processing;Low Cost- Amazon Kinesis is cost-efficient for workloads of any scale. You can pay as you go, and you’ll only pay for the resources you use. You can get started by provisioning low throughput streams, and only pay a low hourly rate for the throughput you need
Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
Statistics
Stacks
794
Stacks
219
Followers
604
Followers
497
Votes
9
Votes
19
Pros & Cons
Pros
  • 9
    Scalable
Cons
  • 3
    Cost
Pros
  • 7
    Unified batch and stream processing
  • 5
    Autoscaling
  • 4
    Fully managed
  • 3
    Throughput Transparency

What are some alternatives to Amazon Kinesis, Google Cloud Dataflow?

Amazon Kinesis Firehose

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.

Twister2

Twister2

It is a high-performance data processing framework with capabilities to handle streaming and batch data. It can leverage high-performance clusters as well we cloud services to efficiently process data.

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