As a result of overwhelming excitement (and pressure) from my fellow Qlikkies, I’m going to share with you the recent demo I did at our all-company annual kick-off which shows Active Intelligence in action. It was intended to be an “internal-only” demo because it mixes existing capabilities with near-term future ones, but, on reflection, I think you, too, will be just as excited.
If you're a developer trying to ETL data into and out of Heroku, the seemingly shortlist of options may disappoint you. Heroku itself promotes Heroku Connect, but this expensive solution might not even integrate with all the systems you use (like AdWords and Facebook), making it difficult to get a holistic view of your data. Fortunately, Heroku Connect isn't the only solution. In fact, there are several third-party ETL tools that can help you get your data in and out of Heroku with ease.
Combining the best of data lakes and data warehouses.
One of the key pieces of work that we've done this past year is to actually build a completely bespoke application, so that we could properly look at the different ways that we could embed Yellowfin. This has helped us create a really unique customer experience within a third-party application. Like all great stories, our vision fundamentally changed on that journey, and we learned three valuable lessons as we built this application we want to share with you.
Learn more about the joint AWS & Iguazio solution: https://www.iguazio.com/partners/aws/
Start working with MLRun, the open-source MLOps orchestration framework: https://github.com/mlrun/mlrun
Snowflake recently launched dynamic data masking, an incredibly useful feature for companies and data-centric organizations that have strict security data governance requirements. This article demonstrates how we implemented data masking at Snowflake by introducing a data masking policy on a VARIANT data type field that holds data in JSON format. We implemented the policy on top of tables and views.
Manufacturing’s digital transformation growth is truly impressive considering it’s delivering value with explosive growth rates. Consider that Manufacturing’s Industry Internet of Things (IIOT) was valued at $161b with an impressive 25% growth rate, or that the Connected Car market will be valued at $225b by 2027 with a 17% growth rate. But then conflicting information arrives as VentureBeat reports that around 90 percent of machine learning models never make it into production?
There is a scene in a certain TV show in which a middle-aged man tells a younger one that he missed picking someone up from the train station because his device did not remind him about it. The younger man points out that, since he bought a new device, he must transfer the appointments.
Having introduced the flow delivery challenges and corresponding resolutions in the first article ‘Cloudera Flow Management Continuous Delivery while Minimizing Downtime’, we will combine all the preceding solutions into an example of flow management continuous delivery architecture. DataFlow Continuous Delivery Architecture In the whole process, we can see the following steps.
