Talend Big Data : Advantages Of Using Open Source Technology

Talend Big Data : Advantages Of Using Open Source Technology


Today, we have many open source ETL/Big Data tools that can be directly download and used without any integration issues. In this story, we will try to understand about Talend Big Data offering, one of the leading open source Big Data Integration tools available in the market along with a quick comparison of its features with other open source tools.

Big Data is undergoing a major change. Have you, been seeing the adoption of Apache Spark as the new engine for Hadoop? Companies are very interested in its blazing fast speed and scalability. Many organizations are looking to use real-time big data to improve customer engagement and generate more business to increase their overall revenue.

A quiet revolution has been taking place in the technology world in recent years. The popularity of open source software has soared as more and more businesses have realized the value of moving away from walled-in, proprietary technologies of old.

And it’s no coincidence that this transformation has taken place in parallel with the explosion of interest in big data and analytics. The modular, fluid and constantly-evolving nature of open source is in synch with the needs of cutting edge analytics projects for faster, more flexible and, vitally, more secure systems and platforms with which to implement them.


Talend is a leading open source ETL, Big Data, MDM & ESB tool specifically for the highly data driven enterprises.  Talend offers innovative solutions to turn data into digital assets and helps them to gain the desired competitor advantage. From on premise to cloud, Talend offers solutions to all sizes of enterprises and helps them to gather real time insights about their businesses and customers.

Some of the key features of Talend are:

  • A graphical integrated development environment with an intuitive Eclipse-based interface
  • Drag-and-drop job design
  • A unified repository for storing and reusing metadata
  • The broadest data connectivity support of any data integration platform, with more than 900 components and built-in connectors that let you quickly bridge between databases, mainframes, file systems, web services, packaged enterprise applications, data warehouses, OLAP applications, Software-as-a-Service and Cloud-based applications, and more
  • Advanced ETL, Big Data, MDM & ESB functionality including string manipulations, automatic lookup handling, and management of slowly changing dimensions
  • Support heavy data load performance with Spark and Map Reduce integration and existing job conversion process with just a single click.


Excellent data integration, big data and data analysis capabilities, make Talend a market leader.  Here are a few advantages of using Talend:

Graphic User Interface

Talend has an excellent graphic user interface that is capable of converting simple ETL jobs to advance MapReduce & Spark jobs on Hadoop cluster to get the Big Data work done in minutes. The Eclipse based GUI of Talend lets developers and data scientists to leverage the Hadoop technologies with ease. 

Drag and Drop Job Design

The simple drag and drop job design feature of Talend makes it easy to create different Hadoop jobs. All you need to do is to just select the job, arrange and configure it.

Hadoop & Spark Integration

Talend offers seamless integration of Hadoop applications into your existing IT infrastructure, making it the best platform for Big data integration and analysis. With over 800 connectors, Talend makes it easy to read from or write to any file format or enterprise application.

Ease of Use

With more and more enterprises scaling up their Big Data technologies, it makes only sense for them to use their existing talent pool instead of spend on expensive talent. Talend makes it easy for professionals of all skill levels to quickly learn and adapt it in their industry.


So what exactly is open source, and what is it that makes it such a good fit for big data projects? Well, like big data, open source is really nothing new – it’s a concept which has existed since the early days of computing. However, it’s only more recently, with the huge growth in the number of people, and amount of data online, that its full potential is starting to be explored.

The lazy description of open source is often that it is “free” software. Certainly that’s how you will hear the more popular open source consumer and business products (such as the Microsoft Office alternative LibreOffice, or the web browser Firefox) described. But there’s much more to it than that. Generally, truly open source products are distributed under one of many different open source licenses, such as the GNU Public License or the Apache License. As well as granting the user the right to freely download and use the project, it can also be modified and redistributed. Software developers can even strip out useful parts from one open source project to use in their own products – which could either be open source themselves, or proprietary. In general, the only stipulation is that they must acknowledge where open source material has been used in their own products, and include the relevant licensing documentation in their distribution.



Open source development has many advantages over its alternative – proprietary development. Because anyone can contribute to the projects, the most popular have huge teams of enthusiastic volunteers constantly working to refine and improve the end product.

In fact, Justin Kestelyn, senior director of technical evangelism and developer relations at leading open source vendor Cloudera, tells me that proprietary solutions are no longer the default choice for data management platforms.

He says “Emerging data management platforms are just never proprietary any more. Most customers would simply see them as too risky for new applications.

“There are multiple – and at this point in history, thoroughly validated – business benefits to using open source software.”

Among those reasons, he says, are the lack of fees allowing customers to evaluate and test products and technologies at no expense, the enthusiasm of the global development community, the appeal of working in an open source environment to developers, and the freedom from “lock in”.

This last one has one caveat, though, Kestelyn explains – “Be careful, though, of open source software that leaves you on an architectural island, with commercial support only available from a single vendor. This can make the principle moot.”

The literal meaning of open source is that the raw source code behind the project is available for anyone to inspect, scrutinize and improve. This brings big security benefits – flaws which could lead to the loss of valuable or personal data are more likely to be spotted when hundreds or thousands of people are examining the code in its raw form. In contrast, in the world of proprietary development, only the handful of people whose job it is to write and then test the code will ever see the exact nature of the nuts and bolts holding it all together.


Don’t be mistaken by thinking that because it is free, open source software is amateur software. As well as the armies of volunteers which work on the projects in their spare time, large numbers of employed professionals are getting paid to do so, too. Tech giants such as IBM, Microsoft and Google are now some of the keenest contributors, in terms of man hours, to the biggest open source projects such as Apache Hadoop and Spark.

Of the involvement of these “internet scale” businesses in open source, Ciaran Dynes, vice president of products at vendor Talend, says “What’s interesting is that their business models are not dependent on ‘owning’ the software. The open sourcing of the software is a by-product of their need to innovate to address a market gap they’ve identified – for example Google Search.

“Open sourcing is a part of their branding and being recognized as a good company to join. This is quite different from vendors, such as Talend or Redhat, where the use of open source has been to seed the market with our technology to upset the status quo of proprietary vendors.”

Many popular big data related open source projects actually started out as in house initiatives at tech companies – for example, the Presto query engine which was developed at Facebook before being released into the wild and adopted by, among others, Netflix and AirBnB to handle back end analytics tasks.

Open source can often be more flexible than proprietary software, too. Because the code, poured over and optimized by thousands of contributors, is often highly efficient, it is often less demanding on computing resources and power than proprietary software which does the same job . This means there is less of a need to constantly be updating hardware and operating systems in order to make sure you can run your software.

The Internet is built on open source – and at the same, it enabled open source to begin to reach its potential by bringing together programmers from around the world and enabling them to collaborate with each other. An entire industry has sprung up around some of the most popular open source products – in the case of big data, that would include Hadoop and Spark – aimed at helping businesses get the most from them. These businesses typically produce enterprise distributions of open source products which, for a fee, come adapted for specific markets, or with packaged consulting services to help their customers get the most from them.