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StreamSets vs Talend: What are the differences?


StreamSets and Talend are both popular data integration platforms used for building data pipelines and ETL processes. While both tools serve similar purposes, there are key differences that set them apart from each other. This Markdown code provides a concise comparison of StreamSets and Talend, highlighting the main differences between the two platforms.

  1. Data Flow Approach: StreamSets adopts a visual, dataflow-based approach to building and managing data pipelines. It allows users to design pipelines by connecting a set of pre-built stages or processors, enabling real-time, event-driven data integration. On the other hand, Talend follows a more traditional, code-based approach, where developers write code to implement the required transformations and manipulations on the data.

  2. Ease of Use: StreamSets offers an intuitive and user-friendly interface that simplifies the process of designing and managing data pipelines. Its drag-and-drop visual editor allows users to easily configure and connect different stages to define the desired transformations. Talend, while also providing a graphical interface, requires more technical expertise to operate efficiently, as it involves writing code for data manipulation and extraction tasks.

  3. Scalability: StreamSets is designed for big data workloads and supports scalability and data parallelism out of the box. It can handle large volumes of data and process data streams in a highly parallelized manner, making it well-suited for big data integration scenarios. Talend, although capable of working with large datasets, may face performance limitations when dealing with extremely high data volumes and complex processing requirements.

  4. Real-time Data Integration: StreamSets specializes in real-time data integration and offers built-in support for streaming data sources and events. It provides connectors to various streaming platforms such as Apache Kafka and Apache Nifi, allowing users to process data in real-time and react to events as they occur. Talend, while it does support real-time data integration to some degree, may require additional setup and configuration to achieve similar functionality.

  5. Connectivity and Ecosystem: StreamSets has a strong focus on connectivity and provides a wide range of connectors and integration capabilities, allowing users to easily connect to various data sources and destinations. It offers native support for a variety of databases, cloud platforms, and streaming frameworks. Talend, with its extensive ecosystem, also offers a wide range of connectors and integration options. It provides connectors to different systems and services, including databases, cloud platforms, and APIs, enabling seamless data integration across various environments.

  6. Enterprise Features: StreamSets offers robust enterprise features such as data lineage, data quality, and security capabilities. It provides comprehensive auditing and monitoring functionalities, allowing users to track and analyze data movements and make informed decisions. Talend, similarly, provides enterprise-grade capabilities, including data governance, data profiling, and security features, which are essential for complex data integration projects in large organizations.

In Summary, StreamSets and Talend differ in their data flow approach, ease of use, scalability, real-time data integration capabilities, connectivity and ecosystem, as well as enterprise features. Both tools have their strengths and can be effective solutions depending on the specific requirements and preferences of the users.

Advice on StreamSets and Talend
karunakaran karthikeyan
Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

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Replies (1)
Rod Beecham
Partnering Lead at Zetaris · | 3 upvotes · 65.2K views

Hi Karunakaran. I obviously have an interest here, as I work for the company, but the problem you are describing is one that Zetaris can solve. Talend is a good ETL product, and Dremio is a good data virtualization product, but the problem you are describing best fits a tool that can combine the five styles of data integration (bulk/batch data movement, data replication/data synchronization, message-oriented movement of data, data virtualization, and stream data integration). I may be wrong, but Zetaris is, to the best of my knowledge, the only product in the world that can do this. Zetaris is not a dashboarding tool - you would need to combine us with Tableau or Qlik or PowerBI (or whatever) - but Zetaris can consolidate data from any source and any location (structured, unstructured, on-prem or in the cloud) in real time to allow clients a consolidated view of whatever they want whenever they want it. Please take a look at for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.

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    What is StreamSets?

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

    What is 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.

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    What are some alternatives to StreamSets and Talend?
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    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.
    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
    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.
    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.
    See all alternatives