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Amazon Kinesis Firehose vs AWS Data Pipeline: What are the differences?
What is Amazon Kinesis Firehose? Simple and Scalable Data Ingestion. 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 AWS Data Pipeline? Process and move data between different AWS compute and storage services. AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.
Amazon Kinesis Firehose can be classified as a tool in the "Real-time Data Processing" category, while AWS Data Pipeline is grouped under "Data Transfer".
Some of the features offered by Amazon Kinesis Firehose are:
- Easy-to-Use
- Integrated with AWS Data Stores
- Automatic Elasticity
On the other hand, AWS Data Pipeline provides the following key features:
- You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.
- Hourly analysis of Amazon S3‐based log data
- Daily replication of AmazonDynamoDB data to Amazon S3
Amazon DynamoDB
Amazon Kinesis
Amazon Kinesis Firehose
GraphQL
SwaggerHub
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 Firehose
Pros of AWS Data Pipeline
- Easy to create DAG and execute it1
