Data Engineering | Head of Azure Big data Engineering Practice at Tiger Analytics
Manish comes with close to 19 years of experience spanning large-scale data lake implementations, DWH modernization on the cloud, Cognitive & AI solutions, and BI & DWH, across multiple cloud platforms. In his current role, Manish heads the Azure - Big Data Engineering Practice in Tiger Analytics, helping architect engineering solutions and providing thought leadership to our customers and, grooming our engineering teams.
"A data lake is a very fundamental solution component in the digital transformation roadmap for most companies. But, ability to get data ready and available for generating insights in a governed manner is still one of the most complex, costly and time-consuming process. While data lakes have been in practice for many years now, newer tools and technologies are evolving and a new set of capabilities are added to data lakes to make them more cost effective and improve their adoption. This presentation is to cover some of the interesting trends that we see in the industry- * Self-service based Data Ingestion/Processing & Delivery, Analytics Workbench for business teams that are fully configurable * Automated Data Classification & PII Discovery * Low/No Code approach for data modernization & migration programs * Reduce time to market with virtual Lakehouse technologies * Next generation Data Observability solution * Platform Observability to monitor data platforms in centralized manner & respond."