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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the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.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.
SwaggerHub
GraphQL
Amazon Kinesis Firehose
Amazon Kinesis
Amazon DynamoDB
Amazon RDSBecause 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.
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
- Scalable7
- Cons4
Pros of Google Cloud Dataflow
- Unified batch and stream processing1
- Autoscaling1
- Fully managed1
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Cons of Amazon Kinesis
- Cost2
Cons of Google Cloud Dataflow
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What is Amazon Kinesis?
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