From digital transformation to infrastructure optimization, business analytics and data visualization allow IT stakeholders to make sense of complex situations. You can’t improve what you can’t measure, right? More and more, we see IT organizations combining different sources of information to uncover unique insight, allowing them to detect areas of improvement, find new opportunities, optimize processes and gain that competitive edge.

VMware Tanzu Greenplum is a massively parallel processing (MPP) data platform based on the open source Greenplum Database project. It’s designed to run the full gamut of analytical workloads, from BI to AI. Because enterprise data lives and grows throughout an organization, it is suboptimal to copy large data sets between different systems as they aren’t able to perform fast enough, scale high enough, or offer the right features.

Microsoft Power BI is a business analytics solution providing interactive visualizations and business intelligence capabilities from data and provides an interface that is simple enough for admins to create their own reports and dashboards. Data inputs to Power BI can come from multiple sources – Excel worksheets, CSV files, database tables, log files, the web, etc. It then employs smart visualizations and built-in AI technologies on that data to turn it into interactive insights.

Hadoop ecosystem now serves as a comfortable home to Big Data now, and the Hadoop data stores now have a greater acceptance across the world by programmers, developers, data scientists, and database management experts. These ecosystems are as convenient as the data storages; however, the inherent reporting system of Hadoop poses a few challenges for the users to overcome.

In this blog series, we will cover how Amazon Redshift and Sumo Logic deliver best-in-class data storage, processing, analytics, and monitoring. In this first post, we will discuss how Amazon Redshift works and why it is the fastest growing cloud data warehouse in the market, used by over 15,000 customers around the world. When an organization gains traction, the size of data that needs to be stored, monitored, and analyzed expands exponentially.

Hive and Spark are two very popular and successful products for processing large-scale data sets. In other words, they do big data analytics. This article focuses on describing the history and various features of both products. A comparison of their capabilities will illustrate the various complex data processing problems these two products can address.

Starting this week, we will do a series of four blogposts on the intersection of Spark with Kubernetes. The first blog post will delve into the reasons why both platforms should be integrated. The second will deep-dive into Spark/K8s integration. The third will discuss usecases for Serverless and Big Data Analytics. The last post will round off with insights on best practices.