Our 1.2.0.0 release of Cloudera Streaming Analytics Powered by Apache Flink brings a wide range of new functionality, including support for lineage and metadata tracking via Apache Atlas, support for connecting to Apache Kudu and the first iteration of the much-awaited FlinkSQL API. Flink’s SQL interface democratizes stream processing, as it caters to a much larger community than the currently widely used Java and Scala APIs focusing on the Data Engineering crowd.

In these uncertain times of the COVID-19 crisis, one thing is certain – data is key to decision making, now more than ever. And, the need for speed in getting access to data as it changes has only accelerated. It’s no wonder, then, that organisations are looking to technologies that help solve the problem of streaming data continuously, so they can run their businesses in real-time.

Research on COVID-19 is being produced at an accelerating rate, and machine intelligence could be crucial in helping the medical community find key information and insights. When I came across the COVID-19 Open Research Dataset (CORD-19), it contained about 57,000 scholarly articles. Just one month later, it has over 158,000 articles. If the clues to fighting COVID-19 lie in this vast repository of knowledge, how can Qlik help?

At Google Cloud, we work with organizations performing large-scale research projects. There are a few solutions we recommend to do this type of work, so that researchers can focus on what they do best—power novel treatments, personalized medicine, and advancements in pharmaceuticals.

We hear from our users in the scientific community that having the right technology foundation is essential. The ability to very quickly create entire clusters of genomics processing, where billing can be stopped once you have the results you need, is a powerful tool. It empowers the scientific community to spend more time doing their research and less time fighting for on-prem cluster time and configuring software.

In this release, we’ve focussed on providing exceptional capability for our customers - both enterprise and software companies - to create and deploy embedded analytics experiences that drive user adoption with minimum amount of effort and coding.

As first-party customer data continues to explode, companies have struggled to make it actionable for personalization, advanced analytics, and other business purposes. As a result, customer data platforms that consolidate and activate known customer information have emerged to help companies generate ROI from their data. At present, nearly 80% of marketing organizations already have a customer data platform or are developing one.

When it comes to furnishing our living spaces, it seems we go through phases. When I was just setting out and leaving home, IKEA was my preferred furniture store. You make your choice, collect all the flat-pack boxes, lug them home, and after some hex key gymnastics: voilà. You’ve truly made it! Since then, I’ve drifted from the “some assembly required” phase to the “ready-made” one.

It’s time to get back to my favorite topic in visualization – how to best use color. I’ve written about it before in two of my previous posts, which you can read by clicking here and here. But, for this post, I’m going to go into a bit more detail on some tips and tricks that you can use.

As a BI Analyst, have you ever encountered a dashboard that wouldn’t refresh because other teams were using it? As a data scientist, have you ever had to wait 6 months before you could access the latest version of Spark? As an application architect, have you ever been asked to wait 12 weeks before you could get hardware to onboard a new application?