Talend was founded in 2005 to modernize data integration. Since then, Talend went public on Nasdaq (TLND) in 2016, and Talend Open Studio, its first open source product, has been downloaded more than 3 million times.
Today, Talend, a leader in cloud integration solutions, liberates data from legacy infrastructure and puts more of the right data to work. Talend Cloud delivers a single platform for data integration across public and private cloud, as well as on-premises environments, and enables greater collaboration between IT and business teams. Combined with an open, native, and extensible architecture for rapidly embracing market innovations, Talend allows to cost-effectively meet the demands of ever-increasing data volumes, users, and use cases.
This is the second part of my blog series on CI/CD best practices. For those of you who are new to this blog, please refer to Part 1 of the same series and for those who want to see the first 10 best practices. Also, I want to give a big thank you for all the support and feedback! In my last blog, we saw the first ten best practices when working with Continuous integration. In this blog, I want to touch on some more best practices. So, with that, let’s jump right in!
With millions of downloads, Talend Open Studio is the leading open source data integration solution. Talend makes it easy to design and deploy integration jobs quickly with graphical tools, native code generation, and hundreds of pre-built components and connectors.
In this blog, I want to highlight some of the best practices that I’ve come across as I've implemented continuous integration with Talend. For those of you who are new to CI/CD please go through the part 1 and part 2 of my previous blogs on ‘Continuous Integration and workflows with Talend and Jenkins’. This blog would also introduce you to some basic guidance on how to implement and maintain a CI/CD system. These recommendations will help in improving the effectiveness of CI/CD.
Let’s be honest, the ‘Data Lake’ is one of the latest buzz-words everyone is talking about. Like many buzzwords, few really know how to explain what it is, what it is supposed to do, and/or how to design and build one. As pervasive as they appear to be, you may be surprised to learn that Gartner predicts that only 15% of Data Lake projects make it into production. Forrester predicts that 33% of Enterprises will take their attempted Data Lake projects off life-support. That’s scary!
Redwood City, CA and London - October 16, 2018 - Talend (NASDAQ: TLND), a global leader in cloud data integration solutions, today announced a major update to Talend Data Fabric, the company’s unified data platform for data integration across complex, multi-cloud and on-premises environments.
The Cloud model lowers the barriers to entry—especially cost, complexity, and time-to-value—that have traditionally limited the adoption and successful use of data warehousing technology. Download this report to learn the advantages of data warehouses in the Cloud and where a Cloud data warehouse fits within the enterprise analytics strategy.
This eBook helps you understand the full capacity of what a true data lake can deliver and the four pillars required to support it. The right data lake platform goes deeper and broader than ever before to reach unfathomable levels of new insight.
With the most comprehensive security measures in place, Talend Integration Cloud makes cloud data secure in transit and at rest, giving enterprises full confidence in cloud data privacy.
Using Talend and Amazon Web Services (AWS), financial institutions are building cloud data lakes to consolidate customer data across hundreds of sources. By validating the quality of that data and correlating data sets with automated processes, you can deliver trusted reporting that meets regulatory requirements and uncover insights for new business.
To find out the state of the IoT and Big Data markets, the O’Reilly team analyzed 300 TB of text and billions of digital documents—search queries, meetups, hiring patterns, SEC filings, websites, and more. The results—based on a big-data analysis of true market activity, not just speculation—are surprising and exciting.
Data plays an important part in nearly every business operation; for it to be valuable, it must be moved and prepared for use, which means you need ETL processes.
Talend’s Big Data and Machine Learning Sandbox is a virtual environment that utilizes Docker containers to combine the Talend Real-time Big Data Platform with some sample scenarios that are pre-built and ready-to-run. This example uses Talend's machine learning capabilities to implement a personalized recommendation model based on user input.
Talend’s Big Data and Machine Learning Sandbox is a virtual environment that utilizes Docker containers to combine the Talend Real-time Big Data Platform with some sample scenarios that are pre-built and ready-to-run. This example demonstrates a Data Warehouse Optimization approach that utilizes the power of Spark to perform analytics of a large dataset before loading it to the Data Warehouse.
Talend seven’s release is our biggest yet, introducing improvements to Cloud, big data, governance and developer productivity. Here are some of Talend 7 “hidden gems” that did not make the headlines but will support you in managing your company’s data lifecycle.
Talend’s Big Data and Machine Learning Sandbox is a virtual environment that utilizes Docker containers to combine the Talend Real-time Big Data Platform with some sample scenarios that are pre-built and ready-to-run. This video shows you what to expect when you start the sandbox for the first time and how to select a Big Data Platform for evaluation.