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’s 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. ...

  • Amazon Athena
    Amazon Athena

    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. ...

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
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      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
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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
          • 50
            Features
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            Task Dependency Management
          • 12
            Cluster of workers
          • 12
            Beautiful UI
          • 10
            Extensibility
          • 5
            Complex workflows
          • 5
            Python
          • 5
            Open source
          • 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

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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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          Matillion logo

          Matillion

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          An ETL Tool for BigData
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          PROS OF MATILLION
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              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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              PROS OF APACHE SPARK
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                Open-source
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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
              • 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 · 2.5M 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 · 1.2M 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
              • 9
                Managed Hive Metastore
              CONS OF AWS GLUE
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                related AWS Glue posts

                Pardha Saradhi
                Technical Lead at Incred Financial Solutions · | 6 upvotes · 60.3K 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.

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                Punith Ganadinni
                Senior Product Engineer · | 2 upvotes · 49K 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
                • 2
                  Alert system based on custom query results
                • 2
                  API for searching logs, running reports
                • 2
                  Query engine supports joining, aggregation, stats, etc
                • 1
                  Ability to style search results into reports
                • 1
                  Query any log as key-value pairs
                • 1
                  Splunk language supports string, date manip, math, etc
                • 1
                  Granular scheduling and time window support
                • 1
                  Custom log parsing as well as automatic parsing
                • 1
                  Dashboarding on any log contents
                • 1
                  Rich GUI for searching live logs
                CONS OF SPLUNK
                • 1
                  Splunk query language rich so lots to learn

                related Splunk posts

                Shared insights
                on
                KibanaKibanaSplunkSplunkGrafanaGrafana

                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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                Amazon Athena logo

                Amazon Athena

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                Query S3 Using SQL
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                PROS OF AMAZON ATHENA
                • 15
                  Use SQL to analyze CSV files
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                  Glue crawlers gives easy Data catalogue
                • 7
                  Cheap
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                  Query all my data without running servers 24x7
                • 4
                  No data base servers yay
                • 3
                  Easy integration with QuickSight
                • 2
                  Query and analyse CSV,parquet,json files in sql
                • 2
                  Also glue and athena use same data catalog
                • 1
                  No configuration required
                • 0
                  Ad hoc checks on data made easy
                CONS OF AMAZON ATHENA
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                  related Amazon Athena posts

                  I use Amazon Athena because similar to Google BigQuery , you can store and query data easily. Especially since you can define data schema in the Glue data catalog, there's a central way to define data models.

                  However, I would not recommend for batch jobs. I typically use this to check intermediary datasets in data engineering workloads. It's good for getting a look and feel of the data along its ETL journey.

                  See more

                  Hi all,

                  Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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