Alternatives to Talend logo

Alternatives to Talend

Spring Batch, Alooma, Airflow, Matillion, and Apache Spark are the most popular alternatives and competitors to Talend.
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What is Talend and what are its top alternatives?

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.
Talend is a tool in the Big Data Tools category of a tech stack.

Top Alternatives to Talend

  • Spring Batch

    Spring Batch

    It is designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. It also provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management. ...

  • Alooma

    Alooma

    Get the power of big data in minutes with Alooma and Amazon Redshift. Simply build your pipelines and map your events using Alooma鈥檚 friendly mapping interface. Query, analyze, visualize, and predict now. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Splunk

    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • 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. ...

Talend alternatives & related posts

Spring Batch logo

Spring Batch

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A lightweight, comprehensive batch framework
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PROS OF SPRING BATCH
    No pros available
    CONS OF SPRING BATCH
      No cons available

      related Spring Batch posts

      Alooma logo

      Alooma

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      Integrate any data source like databases, applications, and any API - with your own Amazon Redshift
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      PROS OF ALOOMA
        No pros available
        CONS OF ALOOMA
          No cons available

          related Alooma posts

          Airflow logo

          Airflow

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          A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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          related Airflow posts

          Shared insights
          on
          Jenkins
          Airflow

          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

          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?

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          Matillion logo

          Matillion

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          An ETL Tool for BigData
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          PROS OF MATILLION
            No pros available
            CONS OF MATILLION
              No cons available

              related Matillion posts

              Apache Spark logo

              Apache Spark

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              Fast and general engine for large-scale data processing
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              related Apache Spark posts

              Eric Colson
              Chief Algorithms Officer at Stitch Fix | 20 upvotes 路 1.6M 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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              Conor Myhrvold
              Tech Brand Mgr, Office of CTO at Uber | 7 upvotes 路 817.6K 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 )

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              AWS Glue logo

              AWS Glue

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              Fully managed extract, transform, and load (ETL) service
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              PROS OF AWS GLUE
              CONS OF AWS GLUE
                No cons available

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                Punith Ganadinni
                Senior Product Engineer | 2 upvotes 路 1.7K 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.

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                Splunk logo

                Splunk

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                Search, monitor, analyze and visualize machine data
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                PROS OF SPLUNK
                  No pros available
                  CONS OF SPLUNK
                    No cons available

                    related Splunk posts

                    Shared insights
                    on
                    Kibana
                    Splunk
                    Grafana

                    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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                    Apache Flink logo

                    Apache Flink

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                    Fast and reliable large-scale data processing engine
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                    related Apache Flink posts

                    Surabhi Bhawsar
                    Technical Architect at Pepcus | 7 upvotes 路 454.3K views
                    Shared insights
                    on
                    Kafka
                    Apache 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