Redefine your advanced analytics initiatives with data engineering strategies of 2021

As per a Gartner survey, over 30% of data and analytics leaders identified deploying data and analytics efforts in existing business processes and applications as their most significant challenge.

Enterprise analytics projects fall short of their expectations when they are not driven by an infrastructure and process that supports management of data at scale. 
Data Engineering empowers your analytics and business teams to leverage the full potential of data by generating real-time insights, improving data quality and reducing infrastructure costs. Additionally it accelerates machine learning projects, saving precious time of data scientists.

What's inside:

- Importance of data management for enterprise analytics 

- The data processing cycle

- Impact of open source technologies in analytics

- Differences between software engineering and data engineering processes

- Building an efficient data engineering team