Alternatives to Apache Beam logo

Alternatives to Apache Beam

Apache Spark, Kafka Streams, Kafka, Airflow, and Google Cloud Dataflow are the most popular alternatives and competitors to Apache Beam.
179
360
+ 1
14

What is Apache Beam and what are its top alternatives?

Apache Beam is an open-source unified programming model that allows you to define batch and streaming data processing jobs and run them seamlessly across different execution engines such as Apache Flink, Apache Spark, and Google Cloud Dataflow. It provides a flexible and portable way to create data processing pipelines that can scale from a single node to large clusters. However, Beam can be complex to learn and lacks out-of-the-box support for some advanced features found in other frameworks.

  1. Apache Flink: Apache Flink is a powerful open-source stream processing framework with support for both batch and stream processing. It offers low latency and high throughput, fault tolerance, and exactly-once processing semantics. Pros: Powerful stream processing capabilities, excellent performance. Cons: Steeper learning curve compared to Apache Beam.
  2. Apache Spark: Apache Spark is another popular open-source distributed processing framework that supports batch and stream processing. It provides rich APIs in multiple languages, built-in libraries for machine learning and graph processing, and can run on various cluster managers. Pros: Versatile, extensive ecosystem. Cons: Does not provide native support for some stream processing functionalities.
  3. Google Cloud Dataflow: Google Cloud Dataflow is a fully managed stream and batch processing service based on Apache Beam. It offers auto-scaling, built-in monitoring and debugging tools, and seamless integration with other Google Cloud services. Pros: Fully managed service, easy to deploy. Cons: Limited to Google Cloud Platform.
  4. Apache Kafka Streams: Apache Kafka Streams is a lightweight stream processing library that is tightly integrated with Apache Kafka, a distributed streaming platform. It allows for building real-time applications and microservices without the need for external processing tools. Pros: Seamless integration with Kafka, lightweight. Cons: Limited functionality compared to full-fledged processing frameworks.
  5. StreamSets Data Collector: StreamSets Data Collector is an open-source platform for designing, executing, and monitoring data pipelines. It offers a visual interface for building pipelines, support for various sources and destinations, and built-in validation and monitoring features. Pros: Intuitive visual interface, extensive connectivity. Cons: Less focus on advanced stream processing capabilities.
  6. Confluent Platform: Confluent Platform is a complete event streaming platform built on Apache Kafka that includes additional components for data integration, real-time analytics, and data governance. It provides enterprise-grade features and commercial support for running Kafka-based stream processing applications. Pros: Enterprise-grade features, commercial support. Cons: Cost associated with enterprise features.
  7. AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service that can also perform stream processing using Apache Spark. It offers data cataloging, job scheduling, and serverless execution of data transformation tasks. Pros: Serverless execution, seamless integration with other AWS services. Cons: Limited to the AWS ecosystem.
  8. Spring Cloud Data Flow: Spring Cloud Data Flow is a toolkit for building data integration and stream processing applications based on the popular Spring Boot framework. It offers a web-based dashboard for managing data pipelines, support for multiple runtime platforms, and integration with Spring Cloud Stream and Spring Cloud Task. Pros: Integration with Spring ecosystem, flexible deployment options. Cons: Relatively young project compared to established frameworks.
  9. Databricks: Databricks is a unified data analytics platform built on Apache Spark that provides collaborative workspace, interactive notebooks, and optimized performance for big data processing. It offers automated cluster management, built-in machine learning libraries, and integration with various data sources. Pros: Usability, built-in machine learning capabilities. Cons: Cost associated with the platform.
  10. Presto: Presto is a distributed SQL query engine optimized for interactive analytics on large datasets. It can connect to multiple data sources, including Hadoop, MySQL, and Kafka, and allows for querying data across different storage systems with high performance. Pros: High performance, SQL compatibility. Cons: Not a dedicated stream processing framework.

Top Alternatives to Apache Beam

  • 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. ...

  • 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. ...

  • Kafka
    Kafka

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

  • 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. ...

  • 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. ...

  • Apache Flink
    Apache Flink

    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. ...

  • AWS Glue
    AWS Glue

    A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. ...

  • StreamSets
    StreamSets

    An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps. ...

Apache Beam alternatives & related posts

Apache Spark logo

Apache Spark

2.9K
3.5K
140
Fast and general engine for large-scale data processing
2.9K
3.5K
+ 1
140
PROS OF APACHE SPARK
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
CONS OF APACHE SPARK
  • 4
    Speed

related Apache Spark posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M 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

See more
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

See more
Kafka Streams logo

Kafka Streams

394
473
0
A client library for building applications and microservices
394
473
+ 1
0
PROS OF KAFKA STREAMS
    Be the first to leave a pro
    CONS OF KAFKA STREAMS
      Be the first to leave a con

      related Kafka Streams posts

      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?

      See more
      Shared insights
      on
      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?

      See more
      Kafka logo

      Kafka

      23.1K
      21.7K
      607
      Distributed, fault tolerant, high throughput pub-sub messaging system
      23.1K
      21.7K
      + 1
      607
      PROS OF KAFKA
      • 126
        High-throughput
      • 119
        Distributed
      • 92
        Scalable
      • 86
        High-Performance
      • 66
        Durable
      • 38
        Publish-Subscribe
      • 19
        Simple-to-use
      • 18
        Open source
      • 12
        Written in Scala and java. Runs on JVM
      • 9
        Message broker + Streaming system
      • 4
        KSQL
      • 4
        Avro schema integration
      • 4
        Robust
      • 3
        Suport Multiple clients
      • 2
        Extremely good parallelism constructs
      • 2
        Partioned, replayable log
      • 1
        Simple publisher / multi-subscriber model
      • 1
        Fun
      • 1
        Flexible
      CONS OF KAFKA
      • 32
        Non-Java clients are second-class citizens
      • 29
        Needs Zookeeper
      • 9
        Operational difficulties
      • 5
        Terrible Packaging

      related Kafka posts

      Nick Rockwell
      SVP, Engineering at Fastly · | 46 upvotes · 3.4M 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.

      See more
      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

      See more
      Airflow logo

      Airflow

      1.7K
      2.7K
      126
      A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
      1.7K
      2.7K
      + 1
      126
      PROS OF AIRFLOW
      • 51
        Features
      • 14
        Task Dependency Management
      • 12
        Beautiful UI
      • 12
        Cluster of workers
      • 10
        Extensibility
      • 6
        Open source
      • 5
        Complex workflows
      • 5
        Python
      • 3
        Good api
      • 3
        Apache project
      • 3
        Custom operators
      • 2
        Dashboard
      CONS OF AIRFLOW
      • 2
        Observability is not great when the DAGs exceed 250
      • 2
        Running it on kubernetes cluster relatively complex
      • 2
        Open source - provides minimum or no support
      • 1
        Logical separation of DAGs is not straight forward

      related Airflow posts

      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.

      See more

      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.

      See more
      Google Cloud Dataflow logo

      Google Cloud Dataflow

      217
      487
      19
      A fully-managed cloud service and programming model for batch and streaming big data processing.
      217
      487
      + 1
      19
      PROS OF GOOGLE CLOUD DATAFLOW
      • 7
        Unified batch and stream processing
      • 5
        Autoscaling
      • 4
        Fully managed
      • 3
        Throughput Transparency
      CONS OF GOOGLE CLOUD DATAFLOW
        Be the first to leave a con

        related Google Cloud Dataflow posts

        Nick Rockwell
        SVP, Engineering at Fastly · | 8 upvotes · 110.9K views

        We really drank the Google Kool-Aid on analytics. So, everything's going into Google BigQuery and almost everything is going straight into Google Cloud Pub/Sub and then doing some processing in Google Cloud Dataflow before ending up in BigQuery. We still do too much processing and augmentation on the front end before it goes into Pub/Sub. And that's using some kind of stuff we pulled together using Amazon DynamoDB and so on. And it's very brittle, actually. Actually, Dynamo throttling is one of our biggest headaches. So, I want all of that to go away and do all our augmentation in BigQuery after the data's been collected. And having it just go straight into Pub/Sub. So, we're working on that. And it'll happen, some time. #Analytics #AnalyticsPipeline

        See more

        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?

        See more
        Apache Flink logo

        Apache Flink

        517
        862
        38
        Fast and reliable large-scale data processing engine
        517
        862
        + 1
        38
        PROS OF APACHE FLINK
        • 16
          Unified batch and stream processing
        • 8
          Easy to use streaming apis
        • 8
          Out-of-the box connector to kinesis,s3,hdfs
        • 4
          Open Source
        • 2
          Low latency
        CONS OF APACHE FLINK
          Be the first to leave a con

          related Apache Flink posts

          Surabhi Bhawsar
          Technical Architect at Pepcus · | 7 upvotes · 718.6K views
          Shared insights
          on
          KafkaKafkaApache FlinkApache Flink

          I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.

          See more

          I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?

          See more
          AWS Glue logo

          AWS Glue

          449
          806
          9
          Fully managed extract, transform, and load (ETL) service
          449
          806
          + 1
          9
          PROS OF AWS GLUE
          • 9
            Managed Hive Metastore
          CONS OF AWS GLUE
            Be the first to leave a con

            related AWS Glue 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?

            See more
            Pardha Saradhi
            Technical Lead at Incred Financial Solutions · | 6 upvotes · 101.9K views

            Hi,

            We are currently storing the data in Amazon S3 using Apache Parquet format. We are using Presto to query the data from S3 and catalog it using AWS Glue catalog. We have Metabase sitting on top of Presto, where our reports are present. Currently, Presto is becoming too costly for us, and we are looking for alternatives for it but want to use the remaining setup (S3, Metabase) as much as possible. Please suggest alternative approaches.

            See more
            StreamSets logo

            StreamSets

            50
            132
            0
            An end-to-end platform for smart data pipelines
            50
            132
            + 1
            0
            PROS OF STREAMSETS
              Be the first to leave a pro
              CONS OF STREAMSETS
              • 2
                No user community
              • 1
                Crashes

              related StreamSets posts