Alternatives to Amazon Kinesis Firehose logo

Alternatives to Amazon Kinesis Firehose

Stream, Kafka, Amazon Kinesis, and Google Cloud Dataflow are the most popular alternatives and competitors to Amazon Kinesis Firehose.
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What is Amazon Kinesis Firehose and what are its top alternatives?

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.
Amazon Kinesis Firehose is a tool in the Real-time Data Processing category of a tech stack.

Top Alternatives to Amazon Kinesis Firehose

  • Stream

    Stream

    Stream allows you to build scalable feeds, activity streams, and chat. Stream’s simple, yet powerful API’s and SDKs are used by some of the largest and most popular applications for feeds and chat. SDKs available for most popular languages. ...

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • Amazon Kinesis

    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. ...

  • Google Cloud Dataflow

    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. ...

Amazon Kinesis Firehose alternatives & related posts

Stream logo

Stream

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Build scalable feeds, activity streams & chat in a few hours instead of months.
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PROS OF STREAM
  • 18
    Integrates via easy-to-use REST API
  • 18
    Up and running in few minutes
  • 18
    It's easy to setup with the minimum coding
CONS OF STREAM
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    related Stream posts

    Kafka logo

    Kafka

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    Distributed, fault tolerant, high throughput pub-sub messaging system
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    PROS OF KAFKA
    • 122
      High-throughput
    • 116
      Distributed
    • 87
      Scalable
    • 81
      High-Performance
    • 65
      Durable
    • 36
      Publish-Subscribe
    • 19
      Simple-to-use
    • 15
      Open source
    • 10
      Written in Scala and java. Runs on JVM
    • 6
      Message broker + Streaming system
    • 4
      Avro schema integration
    • 2
      Suport Multiple clients
    • 2
      Robust
    • 2
      KSQL
    • 2
      Partioned, replayable log
    • 1
      Fun
    • 1
      Extremely good parallelism constructs
    • 1
      Simple publisher / multi-subscriber model
    • 1
      Flexible
    CONS OF KAFKA
    • 27
      Non-Java clients are second-class citizens
    • 26
      Needs Zookeeper
    • 7
      Operational difficulties
    • 2
      Terrible Packaging

    related Kafka posts

    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.1M views

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

    See more
    John Kodumal

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

    See more
    Amazon Kinesis logo

    Amazon Kinesis

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    Store and process terabytes of data each hour from hundreds of thousands of sources
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    PROS OF AMAZON KINESIS
    • 7
      Scalable
    • 4
      Cons
    CONS OF AMAZON KINESIS
    • 2
      Cost

    related Amazon Kinesis posts

    John Kodumal

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

    See more
    Praveen Mooli
    Engineering Manager at Taylor and Francis · | 14 upvotes · 2M views

    We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

    To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

    To build #Webapps we decided to use Angular 2 with RxJS

    #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

    See more
    Google Cloud Dataflow logo

    Google Cloud Dataflow

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    A fully-managed cloud service and programming model for batch and streaming big data processing.
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    PROS OF GOOGLE CLOUD DATAFLOW
    • 2
      Unified batch and stream processing
    • 2
      Autoscaling
    • 2
      Fully managed
    • 1
      Throughput Transparency
    CONS OF GOOGLE CLOUD DATAFLOW
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      related Google Cloud Dataflow posts