Cloud Native Data Platform Engineering.

Quick timeline summary of innovations happening in Data Engineering landscape. Relational Calculus -> RDBMS -> Data Warehouse -> Data Lake -> Hybrid Bubble Lake -> ? Data Engineering Trend Investigation for 2019 and beyond. Why Cloud platform is fast becoming choice #1 for Data platform owners, some data points. How to build first project on cloud (K8, Docker, Helm etc.)? Some relevant points. Interesting Projects to monitor in 2020 - Kubeflow, Distributed TensorFlow, Airflow, Pulser, Helm etc.
Who is this presentation for?
Data Engineers, Platform Engineers and Software Developers
Prerequisite knowledge:
Docker, Microservices, Kubernets, Containers, Data Warehouse, Data Structure
What you'll learn?
Cloud Native Data Platform Management Techniques

• Hands on software development experience in ‘end to end’ Data engineering. • Cloud Infrastructure engineering at Production Scale using - Kubernetes and Docker based applications for portable, extensible open-source platform for managing containerised workloads and services. • Full stack data processing, profiling, cleaning and Master Data Management (M.D.M) expertise on large scale data (truly large scale – Delta file size in Tera-Byte Range). • Next gen ETL Infrastructure engineering to increase capabilities and reduce latency for data acquisition and delivery. Designed and implemented sophisticated and efficient platform capabilities using Lambda and Kappa Architecture.