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  5. StreamSets vs Talend

StreamSets vs Talend

OverviewDecisionsComparisonAlternatives

Overview

Talend
Talend
Stacks297
Followers249
Votes0
StreamSets
StreamSets
Stacks53
Followers133
Votes0

StreamSets vs Talend: What are the differences?

Introduction

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.

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Advice on Talend, StreamSets

karunakaran
karunakaran

Consultant

Jun 26, 2020

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.

80.4k views80.4k
Comments

Detailed Comparison

Talend
Talend
StreamSets
StreamSets

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.

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

-
Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
Statistics
Stacks
297
Stacks
53
Followers
249
Followers
133
Votes
0
Votes
0
Pros & Cons
No community feedback yet
Cons
  • 2
    No user community
  • 1
    Crashes
Integrations
No integrations available
HBase
HBase
Databricks
Databricks
Amazon Redshift
Amazon Redshift
MySQL
MySQL
gRPC
gRPC
Google BigQuery
Google BigQuery
Amazon Kinesis
Amazon Kinesis
Cassandra
Cassandra
Hadoop
Hadoop
Redis
Redis

What are some alternatives to Talend, StreamSets?

Kafka

Kafka

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

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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.

ActiveMQ

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Presto

Distributed SQL Query Engine for Big Data

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.

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