Alternatives to Apache Flink logo

Alternatives to Apache Flink

Apache Spark, Apache Storm, Akutan, Apache Flume, and Kafka are the most popular alternatives and competitors to Apache Flink.
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What is Apache Flink and what are its top alternatives?

Apache Flink is a powerful open-source stream processing framework that is capable of handling batch processing as well. It provides low-latency and high-throughput processing of big data sets in real-time. Flink offers fault tolerance, event time processing, support for complex event processing, and connectors to various data sources and sinks. However, configuring and managing Flink clusters can be complex, and it may not be suitable for small-scale projects.

  1. Apache Spark: Apache Spark is a popular open-source unified analytics engine for big data processing. It provides support for batch processing, real-time stream processing, machine learning, and graph processing. Key features include in-memory processing, fault tolerance, and a rich set of APIs. Pros of Apache Spark include ease of use and scalability, while cons include relatively higher memory consumption compared to Apache Flink.
  2. Kafka Streams: Kafka Streams is a client library for building real-time stream processing applications with Apache Kafka. It offers scalability, fault tolerance, stateful processing, and seamless integration with Kafka. Pros of Kafka Streams include tight integration with Kafka and simplicity, while cons include limited features compared to Flink.
  3. Apache Storm: Apache Storm is a distributed real-time computation system with a similar focus on stream processing as Flink. It provides fault tolerance, horizontal scalability, and support for complex event processing. Pros of Apache Storm include low latency and powerful processing capabilities, while cons include a steeper learning curve compared to Flink.
  4. Amazon Kinesis Data Analytics: Amazon Kinesis Data Analytics is a fully managed service for real-time processing of streaming data with Apache Flink. It offers easy deployment, scalability, and integration with other AWS services. Pros include seamless integration with AWS, while cons include potential vendor lock-in.
  5. Google Cloud Dataflow: Google Cloud Dataflow is a managed service for real-time stream and batch data processing. It provides autoscaling, fault tolerance, and integration with Google Cloud services. Pros include ease of use and integration with Google Cloud, while cons include limited flexibility compared to Apache Flink.
  6. Microsoft Azure Stream Analytics: Microsoft Azure Stream Analytics is a real-time data processing service that offers low-latency processing, scalability, and integration with Microsoft Azure services. Pros include tight integration with Azure, while cons include limited customization options compared to Flink.
  7. Apache NiFi: Apache NiFi is a robust data ingestion and distribution system that can be used for real-time data processing. It offers data routing, transformation, and system mediation capabilities. Pros of Apache NiFi include ease of use and flexibility in data flow management, while cons include limited complex event processing features compared to Flink.
  8. StreamSets: StreamSets is a data operations platform that enables real-time data movement and processing. It offers support for data drift handling, data pipeline monitoring, and integration with various data sources. Pros include ease of use and comprehensive monitoring capabilities, while cons include potential performance limitations compared to Flink.
  9. Heron: Heron is a real-time stream processing engine developed by Twitter as a successor to Apache Storm. It provides low latency, high throughput, and seamless integration with Apache Storm topologies. Pros of Heron include performance improvements over Storm, while cons include a smaller community compared to Flink.
  10. Hazelcast Jet: Hazelcast Jet is an open-source distributed stream processing engine that offers high performance, fault tolerance, and support for distributed computing primitives. Pros of Hazelcast Jet include fast processing speeds and scalability, while cons include a smaller ecosystem compared to Flink.

Top Alternatives to Apache Flink

  • Apache Spark
    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. ...

  • Apache Storm
    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. ...

  • Akutan
    Akutan

    A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world. ...

  • Apache Flume
    Apache Flume

    It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application. ...

  • Kafka
    Kafka

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

  • Kafka Streams
    Kafka Streams

    It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology. ...

  • Airflow
    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • Samza
    Samza

    It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka. ...

Apache Flink alternatives & related posts

Apache Spark logo

Apache Spark

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PROS OF APACHE SPARK
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    Fast and Flexible
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    One platform for every big data problem
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    Great for distributed SQL like applications
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    Easy to install and to use
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    Works well for most Datascience usecases
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    Interactive Query
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    Machine learning libratimery, Streaming in real
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CONS OF APACHE SPARK
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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M 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

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Apache Storm logo

Apache Storm

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Distributed and fault-tolerant realtime computation
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PROS OF APACHE STORM
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    Flexible
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    Easy setup
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    Event Processing
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CONS OF APACHE STORM
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    Marc Bollinger
    Infra & Data Eng Manager at Thumbtack · | 5 upvotes · 1.9M views

    Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.

    We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.

    We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.

    To find out more, read our 2017 engineering blog post about the migration!

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    Akutan logo

    Akutan

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    A Distributed Knowledge Graph Store
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        Apache Flume logo

        Apache Flume

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        A service for collecting, aggregating, and moving large amounts of log data
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            Kafka logo

            Kafka

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            Distributed, fault tolerant, high throughput pub-sub messaging system
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            PROS OF KAFKA
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              High-throughput
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              Distributed
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              Scalable
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              High-Performance
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              Durable
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              Publish-Subscribe
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              Simple-to-use
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              Open source
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              Written in Scala and java. Runs on JVM
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              Message broker + Streaming system
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              KSQL
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              Avro schema integration
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              Robust
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              Suport Multiple clients
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              Extremely good parallelism constructs
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              Partioned, replayable log
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              Simple publisher / multi-subscriber model
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            CONS OF KAFKA
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              Non-Java clients are second-class citizens
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              Needs Zookeeper
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              Operational difficulties
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              Terrible Packaging

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            Nick Rockwell
            SVP, Engineering at Fastly · | 46 upvotes · 3.6M views

            When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

            So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

            React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

            Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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            Ashish Singh
            Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

            To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

            Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

            We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

            Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

            Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

            #BigData #AWS #DataScience #DataEngineering

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            Kafka Streams logo

            Kafka Streams

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                I have recently started using Confluent/Kafka cloud. We want to do some stream processing. As I was going through Kafka I came across Kafka Streams and KSQL. Both seem to be A good fit for stream processing. But I could not understand which one should be used and one has any advantage over another. We will be using Confluent/Kafka Managed Cloud Instance. In near future, our Producers and Consumers are running on premise and we will be interacting with Confluent Cloud.

                Also, Confluent Cloud Kafka has a primitive interface; is there any better UI interface to manage Kafka Cloud Cluster?

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                Apache FlinkApache FlinkKafka StreamsKafka Streams

                We currently have 2 Kafka Streams topics that have records coming in continuously. We're looking into joining the 2 streams based on a key with a window of 5 minutes based on their timestamp.

                Should I consider kStream - kStream join or Apache Flink window joins? Or is there any other better way to achieve this?

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                Airflow logo

                Airflow

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                A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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                PROS OF AIRFLOW
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                  Features
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                  Task Dependency Management
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                  Beautiful UI
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                  Cluster of workers
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                  Extensibility
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                  Open source
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                  Complex workflows
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                  Python
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                  Good api
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                  Apache project
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                  Custom operators
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                  Dashboard
                CONS OF AIRFLOW
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                  Observability is not great when the DAGs exceed 250
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                  Running it on kubernetes cluster relatively complex
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                  Open source - provides minimum or no support
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                  Logical separation of DAGs is not straight forward

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                Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

                Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

                There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

                Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

                Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

                Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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                We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.

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                Samza

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                A distributed stream processing framework
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