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 an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Apache Flink is a tool in the Big Data Tools category of a tech stack.
Apache Flink is an open source tool with 17.5K GitHub stars and 9.8K GitHub forks. Here’s a link to Apache Flink's open source repository on GitHub

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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2.8K
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Fast and general engine for large-scale data processing
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PROS OF APACHE SPARK
  • 58
    Open-source
  • 48
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query
CONS OF APACHE SPARK
  • 3
    Speed

related Apache Spark posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2M 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
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1M views

Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

See more
Apache Storm logo

Apache Storm

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Distributed and fault-tolerant realtime computation
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PROS OF APACHE STORM
  • 10
    Flexible
  • 6
    Easy setup
  • 3
    Clojure
  • 3
    Event Processing
  • 2
    Real Time
CONS OF APACHE STORM
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    related Apache Storm posts

    Marc Bollinger
    Infra & Data Eng Manager at Thumbtack · | 5 upvotes · 462.2K 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!

    See more
    Akutan logo

    Akutan

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    A Distributed Knowledge Graph Store
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    + 1
    0
    PROS OF AKUTAN
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      CONS OF AKUTAN
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        related Akutan posts

        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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        PROS OF APACHE FLUME
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          CONS OF APACHE FLUME
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            related Apache Flume 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 · 2M 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
            Kafka Streams logo

            Kafka Streams

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            A client library for building applications and microservices
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            PROS OF KAFKA STREAMS
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              CONS OF KAFKA STREAMS
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                related Kafka Streams posts

                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
                • 44
                  Features
                • 13
                  Task Dependency Management
                • 12
                  Beautiful UI
                • 11
                  Cluster of workers
                • 10
                  Extensibility
                • 5
                  Open source
                • 4
                  Python
                • 4
                  Complex workflows
                • 3
                  K
                • 2
                  Custom operators
                • 2
                  Dashboard
                • 2
                  Good api
                • 1
                  Apache project
                CONS OF AIRFLOW
                • 1
                  Open source - provides minimum or no support
                • 1
                  Logical separation of DAGs is not straight forward
                • 1
                  Running it on kubernetes cluster relatively complex
                • 1
                  Observability is not great when the DAGs exceed 250

                related Airflow posts

                Shared insights
                on
                JenkinsJenkinsAirflowAirflow

                I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

                1. Trigger Matillion ETL loads
                2. Trigger Attunity Replication tasks that have downstream ETL loads
                3. Trigger Golden gate Replication Tasks
                4. Shell scripts, wrappers, file watchers
                5. Event-driven schedules

                I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

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                Shared insights
                on
                AWS Step FunctionsAWS Step FunctionsAirflowAirflow

                I am working on a project that grabs a set of input data from AWS S3, pre-processes and divvies it up, spins up 10K batch containers to process the divvied data in parallel on AWS Batch, post-aggregates the data, and pushes it to S3.

                I already have software patterns from other projects for Airflow + Batch but have not dealt with the scaling factors of 10k parallel tasks. Airflow is nice since I can look at which tasks failed and retry a task after debugging. But dealing with that many tasks on one Airflow EC2 instance seems like a barrier. Another option would be to have one task that kicks off the 10k containers and monitors it from there.

                I have no experience with AWS Step Functions but have heard it's AWS's Airflow. There looks to be plenty of patterns online for Step Functions + Batch. Do Step Functions seem like a good path to check out for my use case? Do you get the same insights on failing jobs / ability to retry tasks as you do with Airflow?

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                Samza logo

                Samza

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                A distributed stream processing framework
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                PROS OF SAMZA
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                  CONS OF SAMZA
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                    related Samza posts