Thanks for all those who enthusiastically responded to my first blog post on Qlik analytics with Peloton! Now, onward brave souls as we learn HOW I was able to create the analytics I wrote about earlier!
In the previous posts in this series, we have discussed Kerberos, LDAP and PAM authentication for Kafka. In this post we will look into how to configure a Kafka cluster and client to use a TLS client authentication. The examples shown here will highlight the authentication-related properties in bold font to differentiate them from other required security properties, as in the example below. TLS is assumed to be enabled for the Apache Kafka cluster, as it should be for every secure cluster.
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.
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.
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?
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.
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.
Today’s Data Privacy Day offers consumers an opportunity to learn about how companies use, collect, and share their personal information. At the same time, it gives companies a chance to focus on and highlight how they are protecting customer data. Although most businesses view data privacy practices as a way to mitigate their risk, good practices around data privacy can actually differentiate your organization from your competitors.
As your organization begins planning and budgeting for 2021 initiatives, it’s time to take a critical look at your cloud migration strategy. If you’re planning to move your on-premises big data workloads to the cloud this year, you’re undoubtedly faced with a number of questions and challenges.
Retail businesses understand the value of demand forecasting—using their intuition, product and market experience, and seasonal patterns and cycles to plan for future demand. Beyond the need for forecasts that are as accurate as possible, modern retailers also face the challenge of being able to perform demand planning at scale.
