Ganesh Chand

Data Engineer, Databricks

Ganesh Chand is a data engineering consultant at Databricks with 10+ years of industry experience in building enterprise-scale Data solutions. He is particularly passionate about solving world’s toughest data engineering problems. At Databricks, he is busy tackling some of the toughest data engineering projects for Databricks customers. Outside of Databricks, he manages and runs Kathmandu Apache Spark meetup group and has given numerous presentations and workshops on Apache Spark and functional programming using Scala.



Building Data Intensive Analytic Application on Top of Delta LakesSummit Europe 2019

Why to build your own analytics application on top on Delta lake : - Every enterprise is building a data lake. However, these data lakes are plagued by low user adoption, poor data quality, and result in lower ROI. - BI tools may not be enough for your use case, especially, when you want to build a data driven analytical web application such as paysa. - Delta's ACID guarantees allows you to build a real-time reporting app that displays consistent and reliable data

In this talk we will learn :

  • how to build your own analytics app on top of delta lake.
  • how Delta Lake helps you build pristine data lake with several ways to expose data to end-users
  • how analytics web application can be backed by custom Query layer that executes Spark SQL in remote Databricks cluster.
  • We'll explore various options to build an analytics application using various backend technologies.
  • Various Architecture pattern/components/frameworks can be used to build custom analytics platform in no time.
  • How to leverage machine learning to build advanced analytics applications Demo: Analytics application built on Play Framework(for back-end), React(for front-end), Structured Streaming for ingesting data from Delta table. Live query analytics on real time data ML predictions based on analytics data