Alternatives to StreamSets logo

Alternatives to StreamSets

Talend, Kafka, Apache NiFi, Airflow, and Apache Spark are the most popular alternatives and competitors to StreamSets.
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What is StreamSets and what are its top alternatives?

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
StreamSets is a tool in the Message Queue category of a tech stack.

Top Alternatives to StreamSets

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

  • Kafka
    Kafka

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

  • Apache NiFi
    Apache NiFi

    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. ...

  • 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. ...

  • 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. ...

  • Matillion
    Matillion

    It is a modern, browser-based UI, with powerful, push-down ETL/ELT functionality. With a fast setup, you are up and running in minutes. ...

  • 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. ...

  • Confluent
    Confluent

    It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream ...

StreamSets alternatives & related posts

Talend logo

Talend

148
209
0
A single, unified suite for all integration needs
148
209
+ 1
0
PROS OF TALEND
    Be the first to leave a pro
    CONS OF TALEND
      Be the first to leave a con

      related Talend posts

      Kafka logo

      Kafka

      18.3K
      17.4K
      593
      Distributed, fault tolerant, high throughput pub-sub messaging system
      18.3K
      17.4K
      + 1
      593
      PROS OF KAFKA
      • 125
        High-throughput
      • 119
        Distributed
      • 89
        Scalable
      • 83
        High-Performance
      • 65
        Durable
      • 37
        Publish-Subscribe
      • 19
        Simple-to-use
      • 17
        Open source
      • 11
        Written in Scala and java. Runs on JVM
      • 8
        Message broker + Streaming system
      • 4
        Avro schema integration
      • 4
        Robust
      • 4
        KSQL
      • 2
        Suport Multiple clients
      • 2
        Partioned, replayable log
      • 1
        Flexible
      • 1
        Extremely good parallelism constructs
      • 1
        Simple publisher / multi-subscriber model
      • 1
        Fun
      CONS OF KAFKA
      • 29
        Non-Java clients are second-class citizens
      • 27
        Needs Zookeeper
      • 7
        Operational difficulties
      • 2
        Terrible Packaging

      related Kafka posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M 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
      Apache NiFi logo

      Apache NiFi

      292
      590
      62
      A reliable system to process and distribute data
      292
      590
      + 1
      62
      PROS OF APACHE NIFI
      • 15
        Visual Data Flows using Directed Acyclic Graphs (DAGs)
      • 8
        Free (Open Source)
      • 7
        Simple-to-use
      • 5
        Reactive with back-pressure
      • 5
        Scalable horizontally as well as vertically
      • 4
        Fast prototyping
      • 3
        Bi-directional channels
      • 2
        Data provenance
      • 2
        Built-in graphical user interface
      • 2
        End-to-end security between all nodes
      • 2
        Can handle messages up to gigabytes in size
      • 1
        Hbase support
      • 1
        Kudu support
      • 1
        Hive support
      • 1
        Slack integration
      • 1
        Support for custom Processor in Java
      • 1
        Lot of articles
      • 1
        Lots of documentation
      CONS OF APACHE NIFI
      • 2
        HA support is not full fledge
      • 2
        Memory-intensive

      related Apache NiFi posts

      I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

      For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

      See more
      Airflow logo

      Airflow

      1.4K
      2.3K
      121
      A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
      1.4K
      2.3K
      + 1
      121
      PROS OF AIRFLOW
      • 49
        Features
      • 14
        Task Dependency Management
      • 12
        Cluster of workers
      • 12
        Beautiful UI
      • 10
        Extensibility
      • 5
        Open source
      • 5
        Python
      • 4
        Complex workflows
      • 3
        Good api
      • 3
        Custom operators
      • 2
        Dashboard
      • 2
        Apache project
      CONS OF AIRFLOW
      • 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
      • 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

      See more
      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?

      See more
      Apache Spark logo

      Apache Spark

      2.7K
      3.1K
      137
      Fast and general engine for large-scale data processing
      2.7K
      3.1K
      + 1
      137
      PROS OF APACHE SPARK
      • 59
        Open-source
      • 48
        Fast and Flexible
      • 8
        One platform for every big data problem
      • 7
        Great for distributed SQL like applications
      • 6
        Easy to install and to use
      • 3
        Works well for most Datascience usecases
      • 2
        Interactive Query
      • 2
        In memory Computation
      • 2
        Machine learning libratimery, Streaming in real
      CONS OF APACHE SPARK
      • 3
        Speed

      related Apache Spark posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M 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 · 1.1M 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
      Matillion logo

      Matillion

      37
      55
      0
      An ETL Tool for BigData
      37
      55
      + 1
      0
      PROS OF MATILLION
        Be the first to leave a pro
        CONS OF MATILLION
          Be the first to leave a con

          related Matillion posts

          AWS Glue logo

          AWS Glue

          374
          655
          8
          Fully managed extract, transform, and load (ETL) service
          374
          655
          + 1
          8
          PROS OF AWS GLUE
          • 8
            Managed Hive Metastore
          CONS OF AWS GLUE
            Be the first to leave a con

            related AWS Glue posts

            Pardha Saradhi
            Technical Lead at Incred Financial Solutions · | 6 upvotes · 47.1K 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
            Punith Ganadinni
            Senior Product Engineer · | 2 upvotes · 38.7K views

            Hey all, I need some suggestions in creating a replica of our RDS DB for reporting and analytical purposes. Cost is a major factor. I was thinking of using AWS Glue to move data from Amazon RDS to Amazon S3 and use Amazon Athena to run queries on it. Any other suggestions would be appreciable.

            See more
            Confluent logo

            Confluent

            200
            179
            13
            A stream data platform to help companies harness their high volume real-time data streams
            200
            179
            + 1
            13
            PROS OF CONFLUENT
            • 3
              No hypercloud lock-in
            • 3
              Dashboard for kafka insight
            • 3
              Free for casual use
            • 2
              Easily scalable
            • 2
              Zero devops
            CONS OF CONFLUENT
            • 1
              Proprietary

            related Confluent 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