Getting Started with Sisense
This course will teach you how analyzing large, complex datasets is greatly simplified by Sisense, and introduce you to the architecture and features of this platform which enables fast and intuitive analysis of big data.
What you'll learn
Sisense has been able to carve out a niche for itself in the highly competitive cloud analytics space by providing a highly optimized and proprietary data layer known as Elasticube, in addition, to support for SQL, powerful built-in functions, and a great set of elegant visualizations.
In this course, Getting Started with Sisense, you’ll see how Sisense is a great technology choice for analyzing data accurately and at scale, and learn how this technology is set up to simplify the analysis of business data.
First, you’ll explore the architecture and features available in Sisense, and how these help users carry out their business intelligence tasks. You'll see how Sisense uses several optimizations to achieve great performance, including its own algorithms to boost CPU cache performance.
Next, you’ll discover how the ElastiCube technology implemented in Sisense enables high-performing visualizations and dashboards. It is also possible to use the live data model to connect directly to an underlying data source as well.
Finally, you’ll learn how to use Sisense to build a simple dashboard composed of widgets which visualize a dataset, and how to leverage the Sisense designer mode to quickly put together sophisticated dashboards.
When you’re finished with this course, you’ll have the skills and knowledge of the Sisense platform needed to explore the various features of this technology which make it easy for you to extract meaningful and actionable insights from data.
Table of contents
About the author
Vitthal has spent a lot of his life studying - he holds Masters Degrees in Math and Electrical Engineering from Stanford, an MBA from INSEAD, and a Bachelors Degree in Computer Engineering from Mumbai. He has also spent a lot of his life working - as a derivatives quant at Credit Suisse in New York, then as a quant trader, first with a hedge fund in Greenwich and then on his own, and finally at Google in Singapore and Flipkart in Bangalore. In all these roles, he has written a lot of code, and b... more