Today’s modern enterprises are collecting data at exponential rates, and it’s no mystery that effectively making use of that data has become a top priority for many. According to a recent survey of 2000 global enterprises by McKinsey & Company, 47% of organizations have embedded at least one AI capability in their standard business processes.

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."

One of the most popular business buzz words du jour is ‘frictionless’. The term has been co-opted in recent years to describe the phenomenon of commercial processes which appear to work smoothly and effortlessly – on the customer side at least.

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.

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’.

Last month, you heard us get all excited about Cloudera Stream Processing – our solution to enabling all of Cloudera customers get the benefits of having the latest, secure version of Apache Kafka along with Schema Registry and Kafka Streams. We are proud to have been supporting hundreds of active customers with their Kafka implementations and now, even more eager to serve several hundred more customers.

In our previous blog on A Case for Mirromaker 2, we had discussed how enterprises rely on Apache Kafka as an essential component of their data pipelines and require that the data availability and durability guarantees cover for entire cluster or datacenter failures. As we had discussed in the blog, the current Apache Kafka solution with Mirrormaker 1 has known limitations in providing an enterprise managed disaster recovery solution.

A Convolutional Neural Network (CNN) is a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. The architecture of these networks was loosely inspired by biological neurons that communicate with each other and generate outputs dependent on the inputs.

We are excited to announce that Raytheon has recognized Cloudera Government Solutions Inc. (CGSI) with an EPIC Supplier Excellence Award for its outstanding quality of work, performance and innovation. EPIC awards are awarded to suppliers who have demonstrated superior achievement for Raytheon and demonstrated overall excellence in performance, innovation, and collaboration. Only the highest-achieving suppliers are awarded this distinction.

Kudu is typically deployed as part of an Operations Data Warehouse (DWH) solution (also commonly referred to as an Active DWH and Live DWH). As such, it is typically deployed as the storage engine of choice for streaming ingest using Apache Nifi or Apache Spark and query using Apache Impala and sometimes Spark SQL.