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Decisions about Amazon Kinesis, Amazon Kinesis Firehose, and Google Cloud Dataflow
Ryan Wans
at Project Aeolus · | 4 upvotes · 20K views
Chose
Amazon DynamoDB
Amazon Kinesis
Amazon Kinesis Firehose
GraphQL
SwaggerHubover
Amazon RDSat
Project Aeolus
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.
Roel van den Brand
Lead Developer at Di-Vision Consultion · | 3 upvotes · 19.2K 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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Learn MorePros of Amazon Kinesis
Pros of Amazon Kinesis Firehose
Pros of Google Cloud Dataflow
Pros of Amazon Kinesis
- Scalable9
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Pros of Google Cloud Dataflow
- Unified batch and stream processing7
- Autoscaling5
- Fully managed4
- Throughput Transparency3
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Cons of Amazon Kinesis
Cons of Amazon Kinesis Firehose
Cons of Google Cloud Dataflow
Cons of Amazon Kinesis
- Cost3
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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 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?
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What are some alternatives to Amazon Kinesis, Amazon Kinesis Firehose, 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.
Firehose.io
Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
Apache Storm
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.