The Attunity Blog

Enter a comma separated list of user names.
Melissa Kolodziej, May 23, 2017

According to a recent survey by Eckerson Group and the Business Application Research Center (BARC), the number of companies using the cloud for data warehousing or BI projects has increased nearly 50% since 2013.

John Evans, May 17, 2017

Most organizations have some sort of experience with building and maintaining a data warehouse or a data mart. And most of these warehouses and marts took a lot of people a long time to build, and cost the organization quite a bit of money. As the primary data source behind BI and analytics programs, the data warehouse is considered a critical component for storing and managing historical data. Over time, enterprises have learned the tools, trained the staff, and have their procedures and processes in place to build and maintain the data warehouse. When the enterprise has done it like the...

Gil Press, May 10, 2017

To help business and IT executives evaluate emerging technologies and their potential impact on the digital transformation of their organizations, Forrester recently published “Top Technologies for Digital Predators, 2017,” a detailed analysis of 15 emerging technologies with a wide range of disruptive potential and time-to-impact. This article is thought leader Gil Press’ summary of the 5 technologies with the highest potential to create competitive advantage, change markets, or alter the business landscape altogether.

Carole Gunst, April 27, 2017

Recently, we ran a webinar featuring Noel Yuhanna, Principal Analyst at Forrester, presenting the latest trends, challenges – including data latency, heterogeneity, and data security – and best practices for building a Hadoop data lake in the cloud.

Carole Gunst, April 20, 2017

The University of North Texas (UNT) has chosen Attunity Replicate to enable a Hadoop data lake as part of the University’s strategic, real-time analytics initiative designed to improve enrollment, retention and overall student experience.

Carole Gunst, April 10, 2017

Today’s enterprise data warehouse is rapidly filling with rising volumes of data from increasingly varied sources. Some analysts estimate that companies use a third of their structured data for analytics. While some IT departments suspect that this is the case, they don’t know how to identify the two thirds of structured data that’s not getting used for analytics.

Vijay Raman, April 2, 2017

When an international leader in the food industry needed to merge data from multiple sources, including SAP, into a centralized data lake for analytics, they chose Attunity Replicate for the job.

Carole Gunst, March 16, 2017

Broadcasting live from theCUBE, in conjunction with Strata + Hadoop World in San Jose, California, George Gilbert, Big Data & Analytics Analyst at Wikibon, interviewed Attunity CMO, Itamar Ankorion, Attunity customer Chris Murphy of a large global insurance company, and Martin Lidl from Deloitte, the firm that helped build the data lake. They discussed their experience using Attunity Replicate to enable a Hadoop data lake for a major insurance firm and some of the goals, challenges and solutions of the implementation.

Carole Gunst, March 8, 2017

What if you could derive real-time insights using ALL of your data? Making ALL of your data available for analytics helps to support business decisions that improve operations, optimize customer service and enable your company to compete more effectively. To do so, companies like yours often look for ways to bring live data into your analytics platform where it can be merged with other to serve a growing number of users.

Gil Press, March 1, 2017

The creation and consumption of data continues to grow by leaps and bounds and with it the investment in big data analytics hardware, software, and services and in data scientists and their continuing education. The availability of very large data sets is one of the reasons Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend, with Google, Facebook, Baidu, Amazon, IBM, Intel, and Microsoft, all with very deep pockets, investing in acquiring talent and releasing open AI hardware and software.