Author: Vijay Datla

Data analytics has been playing a key role in the decision making process at various stages of the business in many industries. In this era of Big Data, the adoption level is going to ONLY increase day by day. It is really overwhelming to see all the Big Data technologies that are popping up every week to cater to various stages of the Big Data Solution implementations. With data being generated at a very fast pace at various sources (applications that automate the business processes), implementing solutions for use cases like “real time data ingestion from various sources”, “processing the data at different levels of the data ingestion” and “preparing the final data for analysis” become challenging. Especially, orchestrating, scheduling, managing and monitoring the pipelines is a very critical task for the Data platform to be stable and reliable. Also, due to the dynamic nature of the data sources, data inflow rate,…

Data analytics has been playing a key role in the decision making process at various stages of the business in many industries. In this era of Big Data, the adoption level is going to ONLY increase day by day. It is really overwhelming to see all the Big Data technologies that are popping up every week to cater to various stages of the Big Data Solution implementations. With data being generated at a very fast pace at various sources (applications that automate the business processes), implementing solutions for use cases like “real time data ingestion from various sources”, “processing the data at different levels of the data ingestion” and “preparing the final data for analysis” become challenging. Especially, orchestrating, scheduling, managing and monitoring the pipelines is a very critical task for the Data platform to be stable and reliable. Also, due to the dynamic nature of the data sources, data inflow rate,…