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Amazon Kinesis vs Amazon Kinesis Firehose: What are the differences?
Introduction
Amazon Kinesis and Amazon Kinesis Firehose are both services offered by Amazon Web Services (AWS) for real-time streaming of data.
1. Flexibility of Data Streaming vs End-to-End Processing:
- Amazon Kinesis: It offers both real-time streaming and storage capabilities, allowing users to ingest, process, and analyze streaming data from various sources.
- Amazon Kinesis Firehose: It is focused on data ingestion and is designed to load streaming data directly into data stores like Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service in near real-time without requiring any additional processing.
2. Ease of Use and Simplified Management:
- Amazon Kinesis: It provides more control over data processing and offers features like data retention, scaling, and detailed monitoring for managing streaming data effectively.
- Amazon Kinesis Firehose: It simplifies the data ingestion process by automatically handling data buffering, compressing, encrypting, and delivering the data to the desired storage service, reducing the need for managing infrastructure or data processing tasks.
3. Integration with Other AWS Services:
- Amazon Kinesis: It can integrate with various AWS services like AWS Lambda, Amazon Redshift, Amazon EMR, and Amazon Elasticsearch Service, allowing advanced data processing and analytics.
- Amazon Kinesis Firehose: It directly integrates with services like Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service, making it easier to load streaming data into these services without additional integration efforts.
4. Data Transformation and Enrichment:
- Amazon Kinesis: It provides the ability to write custom code using the Kinesis Client Library (KCL) for processing and transforming streaming data before storing or analyzing it.
- Amazon Kinesis Firehose: It has limited data transformation capabilities and usually requires external services like AWS Lambda for performing transformations on the data before delivering it to the storage service.
5. Processing Durability:
- Amazon Kinesis: It stores streaming data for a configurable retention period, providing durability and the ability to replay the data multiple times if needed.
- Amazon Kinesis Firehose: It doesn't retain the data for a longer duration as its main purpose is to efficiently deliver the data to the storage service, making it less suitable for scenarios requiring long-term data retention.
6. Pricing Structure:
- Amazon Kinesis: It has a pay-as-you-go pricing model based on the number of shards and data processing capacity required.
- Amazon Kinesis Firehose: It has a simple pricing structure based on the amount of data ingested and any additional optional data transformation operations.
In summary, Amazon Kinesis provides more flexibility for processing and analyzing streaming data, while Amazon Kinesis Firehose simplifies the data ingestion process and seamlessly integrates with storage services.
Decisions about Amazon Kinesis and Amazon Kinesis Firehose
Ryan Wans
at Project Aeolus · | 4 upvotes · 21.8K 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 · 20.2K 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
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- Scalable9
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Cons of Amazon Kinesis
Cons of Amazon Kinesis Firehose
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- 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.
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What companies use Amazon Kinesis?
What companies use Amazon Kinesis Firehose?
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What tools integrate with Amazon Kinesis?
What tools integrate with Amazon Kinesis Firehose?
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What are some alternatives to Amazon Kinesis and Amazon Kinesis Firehose?
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.