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?

Talend is an open-source data integration platform that provides various tools for data integration, data profiling, and big data integration. Its key features include support for various data sources and formats, data quality checks, real-time data processing, and graphical design interface. However, some limitations of Talend include the complexity of learning curve for new users, high memory consumption for large datasets, and limited support for complex data transformations.

  1. Apache NiFi: Apache NiFi is a powerful data integration tool that provides an intuitive drag-and-drop interface for designing data flows. Key features include data ingestion from various sources, data routing, and real-time data processing. Pros include easy to use interface, extensive community support, and scalability. Cons include limited support for complex data transformations.
  2. Informatica PowerCenter: Informatica PowerCenter is an enterprise data integration tool that offers advanced features for data integration, data profiling, and data quality management. Pros include high performance, extensive connectivity options, and robust security features. Cons include high cost and steep learning curve.
  3. Pentaho Data Integration: Pentaho Data Integration provides a comprehensive set of tools for data integration, ETL, and big data processing. Key features include visual data pipeline design, support for various data sources, and data analytics capabilities. Pros include open-source community edition, scalability, and rich set of plugins. Cons include occasional performance issues with large datasets.
  4. Microsoft SSIS: Microsoft SQL Server Integration Services (SSIS) is a popular data integration tool that offers a wide range of features for ETL, data profiling, and data cleansing. Pros include seamless integration with other Microsoft products, robust data security, and high performance. Cons include limited cross-platform compatibility and high licensing costs.
  5. Talend Big Data Platform: Talend Big Data Platform extends the capabilities of Talend for big data integration, data quality, and data governance. Key features include support for Hadoop, Spark, and NoSQL databases, as well as real-time data processing. Pros include seamless integration with existing Talend solutions, advanced data profiling capabilities, and strong data security features. Cons include high cost of licensing and complex setup process.
  6. Matillion: Matillion is a cloud-native data integration platform that offers ETL capabilities for various cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake. Pros include user-friendly interface, easy scalability, and cost-effective pricing model. Cons include limited support for on-premises data sources and occasional performance issues with complex data transformations.
  7. IBM InfoSphere DataStage: IBM InfoSphere DataStage is an enterprise-level data integration tool that provides advanced features for ETL, data quality management, and data governance. Pros include high performance, extensive connectivity options, and robust metadata management capabilities. Cons include high cost of licensing and steep learning curve.
  8. StreamSets: StreamSets is a modern data integration platform that offers real-time data ingestion, big data processing, and data pipeline monitoring. Key features include support for various data sources, data drift detection, and data pipeline governance. Pros include open-source community edition, intuitive interface, and advanced data quality checks. Cons include limited support for complex data transformations and occasional bugs in the software.
  9. CloverDX: CloverDX is a versatile data integration platform that provides ETL, data transformation, and data quality capabilities. Pros include visual data transformation design, support for various data sources, and advanced debugging tools. Cons include limited support for big data processing and high cost for enterprise edition.
  10. Dell Boomi: Dell Boomi is a cloud-based integration platform that offers features for application integration, data synchronization, and API management. Pros include easy-to-use interface, no coding required, and seamless integration with various cloud applications. Cons include limited support for complex data transformations and potential scalability issues with large volumes of data.

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. ...

  • MySQL
    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • PostgreSQL
    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

Talend alternatives & related posts

Spring Batch logo

Spring Batch

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A lightweight, comprehensive batch framework
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0
PROS OF SPRING BATCH
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    CONS OF SPRING BATCH
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      Alooma logo

      Alooma

      24
      0
      Integrate any data source like databases, applications, and any API - with your own Amazon Redshift
      24
      0
      PROS OF ALOOMA
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        CONS OF ALOOMA
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          Airflow logo

          Airflow

          1.7K
          128
          A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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          128
          PROS OF AIRFLOW
          • 53
            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
          Matillion logo

          Matillion

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          An ETL Tool for BigData
          49
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          PROS OF MATILLION
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            CONS OF MATILLION
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              related Matillion posts

              Apache Spark logo

              Apache Spark

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              140
              Fast and general engine for large-scale data processing
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              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

              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
              Patrick Sun
              Software Engineer at Stitch Fix · | 10 upvotes · 61.3K views

              As a frontend engineer on the Algorithms & Analytics team at Stitch Fix, I work with data scientists to develop applications and visualizations to help our internal business partners make data-driven decisions. I envisioned a platform that would assist data scientists in the data exploration process, allowing them to visually explore and rapidly iterate through their assumptions, then share their insights with others. This would align with our team's philosophy of having engineers "deploy platforms, services, abstractions, and frameworks that allow the data scientists to conceive of, develop, and deploy their ideas with autonomy", and solve the pain of data exploration.

              The final product, code-named Dora, is built with React, Redux.js and Victory, backed by Elasticsearch to enable fast and iterative data exploration, and uses Apache Spark to move data from our Amazon S3 data warehouse into the Elasticsearch cluster.

              See more
              AWS Glue logo

              AWS Glue

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              Fully managed extract, transform, and load (ETL) service
              459
              9
              PROS OF AWS GLUE
              • 9
                Managed Hive Metastore
              CONS OF AWS GLUE
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                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 · 107.7K 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
                MySQL logo

                MySQL

                125.5K
                3.8K
                The world's most popular open source database
                125.5K
                3.8K
                PROS OF MYSQL
                • 800
                  Sql
                • 679
                  Free
                • 562
                  Easy
                • 528
                  Widely used
                • 490
                  Open source
                • 180
                  High availability
                • 160
                  Cross-platform support
                • 104
                  Great community
                • 79
                  Secure
                • 75
                  Full-text indexing and searching
                • 26
                  Fast, open, available
                • 16
                  Reliable
                • 16
                  SSL support
                • 15
                  Robust
                • 9
                  Enterprise Version
                • 7
                  Easy to set up on all platforms
                • 3
                  NoSQL access to JSON data type
                • 1
                  Relational database
                • 1
                  Easy, light, scalable
                • 1
                  Sequel Pro (best SQL GUI)
                • 1
                  Replica Support
                CONS OF MYSQL
                • 16
                  Owned by a company with their own agenda
                • 3
                  Can't roll back schema changes

                related MySQL posts

                Nick Rockwell
                SVP, Engineering at Fastly · | 46 upvotes · 4.1M 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
                Tim Abbott

                We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

                We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

                And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

                I can't recommend it highly enough.

                See more
                PostgreSQL logo

                PostgreSQL

                98.3K
                3.5K
                A powerful, open source object-relational database system
                98.3K
                3.5K
                PROS OF POSTGRESQL
                • 764
                  Relational database
                • 510
                  High availability
                • 439
                  Enterprise class database
                • 383
                  Sql
                • 304
                  Sql + nosql
                • 173
                  Great community
                • 147
                  Easy to setup
                • 131
                  Heroku
                • 130
                  Secure by default
                • 113
                  Postgis
                • 50
                  Supports Key-Value
                • 48
                  Great JSON support
                • 34
                  Cross platform
                • 33
                  Extensible
                • 28
                  Replication
                • 26
                  Triggers
                • 23
                  Multiversion concurrency control
                • 23
                  Rollback
                • 21
                  Open source
                • 18
                  Heroku Add-on
                • 17
                  Stable, Simple and Good Performance
                • 15
                  Powerful
                • 13
                  Lets be serious, what other SQL DB would you go for?
                • 11
                  Good documentation
                • 9
                  Scalable
                • 8
                  Free
                • 8
                  Reliable
                • 8
                  Intelligent optimizer
                • 7
                  Transactional DDL
                • 7
                  Modern
                • 6
                  One stop solution for all things sql no matter the os
                • 5
                  Relational database with MVCC
                • 5
                  Faster Development
                • 4
                  Full-Text Search
                • 4
                  Developer friendly
                • 3
                  Excellent source code
                • 3
                  Free version
                • 3
                  Great DB for Transactional system or Application
                • 3
                  Relational datanbase
                • 3
                  search
                • 3
                  Open-source
                • 2
                  Text
                • 2
                  Full-text
                • 1
                  Can handle up to petabytes worth of size
                • 1
                  Composability
                • 1
                  Multiple procedural languages supported
                • 0
                  Native
                CONS OF POSTGRESQL
                • 10
                  Table/index bloatings

                related PostgreSQL posts

                Simon Reymann
                Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.6M views

                Our whole DevOps stack consists of the following tools:

                • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
                • Respectively Git as revision control system
                • SourceTree as Git GUI
                • Visual Studio Code as IDE
                • CircleCI for continuous integration (automatize development process)
                • Prettier / TSLint / ESLint as code linter
                • SonarQube as quality gate
                • Docker as container management (incl. Docker Compose for multi-container application management)
                • VirtualBox for operating system simulation tests
                • Kubernetes as cluster management for docker containers
                • Heroku for deploying in test environments
                • nginx as web server (preferably used as facade server in production environment)
                • SSLMate (using OpenSSL) for certificate management
                • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
                • PostgreSQL as preferred database system
                • Redis as preferred in-memory database/store (great for caching)

                The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

                • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
                • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
                • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
                • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
                • Scalability: All-in-one framework for distributed systems.
                • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
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                Jeyabalaji Subramanian

                Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

                We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

                Based on the above criteria, we selected the following tools to perform the end to end data replication:

                We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

                We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

                In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

                Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

                In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

                See more