This is the fourth part of our “Diving deep into Gartner’s Top 10 Data and Analytics Technology Trends for 2019” blog series. In this series, we’ll explore the top 10 Data & Analytics 2019 trends identified by Gartner. If you haven’t yet seen the third post on Continuous Intelligence check it out here.

This is the third part of our “Diving deep into Gartner’s Top 10 Data and Analytics Technology Trends for 2019” blog series. In this series, we’ll explore the top 10 Data & Analytics 2019 trends identified by Gartner. If you haven’t yet seen the second post on Augmented Data Management check it out here.

This is the second part of our “Diving deep into Gartner’s Top 10 Data and Analytics Technology Trends for 2019” blog series. In this series, we’ll explore the top 10 Data & Analytics 2019 trends identified by Gartner. If you haven’t yet seen the first post on Augmented Analytics check it out here.

Welcome to the “Diving deep into Gartner’s Top 10 Data and Analytics Technology Trends for 2019” blog series. In this series, we’ll explore the following top 10 Data & Analytics 2019 trends identified by Gartner.

Thus far we’ve discussed how Cassandra, Spark, Kafka, Docker, and Kubernetes can be useful to build a global data platform. These components are powerful in their own right and managing them is a little simpler if we decide to use commercial components from DataStax and Confluent.

There are other tools and services we can use to further accelerate our timeline to deliver a world-class global data and analytics platform. Although bringing up a distributed data (Cassandra), distributed computing (Spark), and distributed communication (Kafka) is a great start for a framework, it still needs a few more components to make it a “Platform” which allows quick creation and delivery of services that an enterprise can use.