What is Amazon Kinesis Firehose and what are its top alternatives?
Amazon Kinesis Firehose is a scalable data streaming service that allows you to easily load streaming data into data lakes and analytics services. Key features include automatic scaling, data transformation capabilities, and integration with various AWS services. However, some limitations include the complexity of setting up and managing the service, and the cost associated with data transfer and storage.
- Apache Kafka: Apache Kafka is a distributed streaming platform known for its high throughput and fault tolerance. Key features include data replication, partitioning, and message durability. Pros: Scalability, fault tolerance, and high performance. Cons: Requires more configuration and setup compared to Amazon Kinesis Firehose.
- Google Cloud Pub/Sub: Google Cloud Pub/Sub is a messaging service that allows you to ingest, transform, and deliver event data. Key features include real-time messaging, event ordering, and reliable message delivery. Pros: Integration with Google Cloud Platform services, horizontal scalability. Cons: Limited support for data transformation.
- Azure Stream Analytics: Azure Stream Analytics is a real-time data streaming and analytics service provided by Microsoft Azure. Key features include complex event processing, real-time analytics, and integration with Azure services. Pros: Easy integration with Azure ecosystem, SQL-like querying. Cons: Limited scalability compared to Amazon Kinesis Firehose.
- Apache Flink: Apache Flink is a distributed stream processing framework known for its high performance and stateful processing capabilities. Key features include exactly-once processing semantics, event time processing, and support for batch processing. Pros: Advanced processing capabilities, low latency, and fault tolerance. Cons: Steeper learning curve compared to Amazon Kinesis Firehose.
- Confluent Platform: Confluent Platform is a distribution of Apache Kafka with additional tools and services for managing and monitoring Kafka clusters. Key features include schema registry, Kafka Connect, and KSQL for stream processing. Pros: Integrated platform for streaming data, rich ecosystem. Cons: Additional cost for enterprise features.
- IBM Streams: IBM Streams is a streaming analytics platform that enables real-time processing of data streams. Key features include analytics modeling, visual development tools, and integration with various data sources. Pros: Scalability, visual development interface. Cons: Complexity in deployment and management.
- Alibaba Cloud Log Service: Alibaba Cloud Log Service is a fully managed service for collecting, consuming, and analyzing log data in real time. Key features include log collection, indexing, and real-time analytics. Pros: Integration with Alibaba Cloud services, built-in log analysis tools. Cons: Limited scalability options compared to Amazon Kinesis Firehose.
- SignalFx: SignalFx is a monitoring and observability platform that provides real-time streaming analytics for metrics, traces, and logs. Key features include advanced analytics, anomaly detection, and data visualization. Pros: Real-time monitoring, integration with various data sources. Cons: Focuses more on monitoring and alerting, less on data transformation.
- StreamSets Data Collector: StreamSets Data Collector is an open-source data ingest platform that enables data movement between different sources and destinations. Key features include data drift handling, data quality monitoring, and support for various connectors. Pros: Flexibility, open-source community support. Cons: Requires more technical expertise and configuration.
- Elasticsearch Logstash: Elasticsearch Logstash is an open-source data processing pipeline that ingests data from multiple sources, transforms it, and sends it to various destinations. Key features include plugins for different data inputs and outputs, data enrichment capabilities, and scalability. Pros: Easy integration with Elasticsearch and Kibana, custom pipeline configurations. Cons: Requires more manual setup and configuration compared to Amazon Kinesis Firehose.
Top Alternatives to Amazon Kinesis Firehose
StreamStream 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. ...
KafkaKafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...
Amazon KinesisAmazon 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 DataflowGoogle 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
- Up and running in few minutes18
- Integrates via easy-to-use REST API18
- It's easy to setup with the minimum coding18
related Stream posts
- High-throughput126
- Distributed119
- Scalable92
- High-Performance86
- Durable66
- Publish-Subscribe38
- Simple-to-use19
- Open source18
- Written in Scala and java. Runs on JVM12
- Message broker + Streaming system9
- KSQL4
- Avro schema integration4
- Robust4
- Suport Multiple clients3
- Extremely good parallelism constructs2
- Partioned, replayable log2
- Simple publisher / multi-subscriber model1
- Fun1
- Flexible1
- Non-Java clients are second-class citizens32
- Needs Zookeeper29
- Operational difficulties9
- Terrible Packaging5
related Kafka posts
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:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
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.
Amazon Kinesis
- Scalable9
- Cost3
related Amazon Kinesis posts
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
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.
Google Cloud Dataflow
- Unified batch and stream processing7
- Autoscaling5
- Fully managed4
- Throughput Transparency3
related Google Cloud Dataflow posts
Will Dataflow be the right replacement for AWS Glue? Are there any unforeseen exceptions like certain proprietary transformations not supported in Google Cloud Dataflow, connectors ecosystem, Data Quality & Date cleansing not supported in DataFlow. etc?
Also, how about Google Cloud Data Fusion as a replacement? In terms of No Code/Low code .. (Since basic use cases in Glue support UI, in that case, CDF may be the right choice ).
What would be the best choice?
I am currently launching 50 pipelines in a Google Cloud Data Fusion version 6.4 instance. These pipelines are launched daily and transport data from a MySQLServer database to Google BigQuery. The cost is becoming very high and I was wondering if the costs with Google Cloud Dataflow decrease for the same rows transported.