Innovative organizations need DataOps and new technologies because old-school data integration is no longer sufficient. The traditional approach creates monolithic, set-in-concrete data pipelines that can’t convert data into insights quickly enough to keep pace with business. The following trends are driving the adoption of Hitachi’s Lumada DataOps Suite.
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.
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?
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.
