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

Amazon Kinesis: 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; Google Cloud Dataflow: A fully-managed cloud service and programming model for batch and streaming big data processing. 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.

Amazon Kinesis and Google Cloud Dataflow can be categorized as "Real-time Data Processing" tools.

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the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.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, Google Cloud Dataflow provides the following key features:

Fully managed
Combines batch and streaming with a single API
High performance with automatic workload rebalancing Open source SDK

Instacart, Lyft, and Zillow are some of the popular companies that use Amazon Kinesis, whereas Google Cloud Dataflow is used by Spotify, Resultados Digitais, and Kapten. Amazon Kinesis has a broader approval, being mentioned in 130 company stacks & 24 developers stacks; compared to Google Cloud Dataflow, which is listed in 32 company stacks and 8 developer stacks.

Decisions about Amazon Kinesis and Google Cloud Dataflow

Roel van den Brand

Lead Developer at Di-Vision Consultion · Dec 14, 2020 | 3 upvotes · 5.4K views

Chose

Amazon Kinesis Firehose

Amazon Kinesis

over

Amazon DynamoDB

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.

Pros of Amazon Kinesis

Pros of Google Cloud Dataflow

5
Scalable
2
Cons

1
Unified batch and stream processing
1
Autoscaling
1
Fully managed

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Cons of Amazon Kinesis

Cons of Google Cloud Dataflow

1
Cost

No cons available

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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.

What companies use Amazon Kinesis?

What companies use Google Cloud Dataflow?

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What tools integrate with Amazon Kinesis?

What tools integrate with Google Cloud Dataflow?

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

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

Interest over time

How much does Amazon Kinesis cost?

How much does Google Cloud Dataflow cost?

Pricing unavailable

News about Amazon Kinesis

Automatically Archive Items to S3 Using DynamoDB Time to Live (TTL) with AWS Lambda and Amazon Kinesis Firehose

(aws.amazon.com)

Jun 6, 2017

Serverless Scaling for Ingesting, Aggregating, and Visualizing Apache Logs with Amazon Kinesis Firehose, AWS Lambda, and Amazon Elasticsearch Service