In my previous article, I provided a quick introduction to Apache Beam, a new distributed processing tool that's currently being incubated at the ASF. Apache Beam provides an abstraction layer allowing developers to focus on Beam code, using the Beam programming model. Thanks to Apache Beam, an implementation is agnostic to the runtime technologies being used, meaning you can switch to technologies quickly and easily.
If you’ve ever started working with new software, you know it can be challenging to start taking full advantage of features and functionality right away. At Talend, we want you to get up and running quickly with our data integration software. Therefore, we’ve introduced a new series of videos to get you familiar with the Talend Studio, building your first Talend Job and moving into some advanced features.
Talend Integration Cloud Summer ’16 went live last month and we couldn’t be more proud of our R&D team for pushing through some new and compelling AWS features for the enterprise.
This week, Talend was named by leading analyst firm, Gartner, Inc., as a Leader in its 2016 Magic Quadrant for Data Integration Tools. For those of you who do not know what that is, the Gartner Magic Quadrant (MQ) is one of the most respected and relied upon research report that gauges for the success, capability, and overall strength of both an entire market segment and the players by which it’s comprised.
It’s Not About the Dot: A Journey to Becoming a Leader in the Gartner Magic Quadrant for Data Integration Tools
When we received notice from Gartner that Talend had been named a Leader in its Magic Quadrant for Data Integration Tools, I took a little time to reflect on the process that has taken place over the past couple of years and how we arrived at this milestone.
Today’s CIOs are no strangers to the concept of the Enterprise Data Lake. Often times, an enterprise data lake is viewed as a panacea for all a CIO’s data ills, including being viewed as the ‘holy grail’ for those trying to spur digital transformation. Yet many CIOs are still struggling to see the payoffs from such data lake investments.
Beyond the quality and performance of its solutions, Talend has been able to innovate and distinguish itself from other product offerings in the market. On both a technological as well as a business level, Talend has democratized access to data integration solutions using an open source model to make them accessible to ever greater numbers of people.
Continuous Technological Innovation
Today we’re thrilled to announce that Talend has gone public on the NASDAQ exchange, ticker symbol TLND. We’re excited to work with all of you as we transition to being a public company. As we look forward to this next phase of our company’s journey, I’d like to spend a few minutes outlining the trends that are driving our business today and how we see the world evolving in the coming years.
The previous post [on Colm's blog] covered how to install the Apache Ranger Admin service. The Apache Ranger Admin UI supports creating authorization policies for various Big Data components, by giving users and/or groups permissions on resources. This means that we need to import users/groups into the Apache Ranger Admin service from some backend service in order to create meaningful authorization policies.
As I have been building analytics competencies and platforms with customers, I am continuously running into the same type of questions and desires and they center around a critical, but poorly understood topic; Master Data Management or MDM. I thought that it was time I got a couple of thoughts on this topic out so others might benefit from what I have seen so far.
As Talend has released its newest version of Talend Data Fabric 6.2.1 for GA, I thought this would be a good time to talk about software upgrades within the Enterprise.
Data is a critical competitive factor and is becoming more and more crucial for achieving success. In theory, hardly anyone disputes this to any extent. But in practice there are some very different levels of maturity when it comes to handling data. Based on practical experience gained in medium-sized companies in particular, I would like to try to describe five typical levels of maturity. This is intended to provide a helping hand on the way to becoming a data-driven organization.
With the continued growth in Cloud computing, more and more organizations are moving their data to Software as a Service (SaaS) providers such as Salesforce.
If you're about to embark on a Data Migration or Data Integration project, and you're used to working with a traditional relational database that sits in your own data center, you may be in for a few surprises.