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Time To Put on Our DataOps Lab Coat

Over the past several weeks, the term DataOps has reverberated throughout the walls of Hitachi Vantara offices around the world and soon it will take the spotlight at NEXT 2019, the world’s first smart conference. We’ve defined DataOps and how its changing the game by putting the right data into the hands of teams, when and where they need it most. We’ve also talked about Hitachi Vantara’s DataOps journey, Project Champagne.

Anscombe's Quartet

In this blog post I’m going to write about a famous piece in visualization history. How can we prove that a visualization is more worth than just looking at the data? That’s a question Francis Anscombe probably asked himself when he back in 1973 constructed the dataset that became known as Anscombe's quartet. A dataset he could use to show statisticians how wrong they were thinking that “numerical calculations are exact, but graphs are rough."

What is Anomaly detection and how to use it for Marketing

Businesses are collecting massive amounts of data as a part of their analytics pipeline. Most of the time, this data is filtered by a computer and presented in a way that a human interprets, through the analytics dashboard. That's a fantastic resource, and has no doubt been of great value to you in business decisions. However, restricting the interpretation of all that data that you've mined to humans leaves a lot of potential insights on the table.

9 top trends that are driving AI and software investments

IT and data leaders are constantly challenged to keep up with new trends in emerging and disruptive technologies, and to determine how each can best aid the organization. In the midst of all the changes going on in 2019, it gets increasingly hard to know where to invest in all this new technology. To help add clarity, here are my thoughts on some of the most important trends that will shape data management and software development for the next couple of years.

How next-gen DI works

Data integration in the ‘Age of Digital’ brings in need for ETL development to happen at the ‘Speed of Business’ rather than at ‘IT Speed’. Data integration layer is the important ‘glue’ between the user engagement apps in the EDGE and the systems of record at the CORE of IT landscape. Application development for the Experience Layer happens at the ‘Speed of Business’ while changes in Integration Layer move at ‘IT Speed’.

Experian: From credit bureau to technology company with APIs

Editor's note: Today we hear from Dang Nguyen, API Platform Product Owner at Experian, on how the company uses the Apigee API management platform to digitally transform from a traditional credit bureau to a true technology and software provider. Read on to learn how Experian uses APIs to help businesses make smarter decisions and individuals take financial control.

Best 14 CI/CD Tools You Must Know | Updated for 2019

“Quality at Speed” is the new norm in software development. Enterprises are making their moves toward DevOps methodologies and Agile culture to accelerate the delivery speed and ensure product quality. In DevOps, a continuous and automated delivery cycle is the backbone that makes fast and reliable delivery possible. This results in the need for proper continuous integration and continuous delivery (CI/CD) tools.

Have you checked out Talend's 2019 summer release yet?

Have you had a chance to take a look at Talend’s summer 2019 product release? Our 2019 release has some exciting features that not only will help improve your productivity but will help you scale data projects across your organization. We are all about helping you do your work faster, and we think you’ll find the new features in this latest product release pretty great.

5 Best Practices for Securing Microservices at Scale

As outlined in a previous article on security challenges for microservices, DevOps are getting more widely distributed, spread thin, and forced to plan for higher levels of interactivity as well as evolving national security “backdoor” measures. Microservices, born from a still-emerging DevOps laboratory environment, can be deployed anywhere: on-prem, in the public cloud, or a hybrid implementation.