
Energy & Resources
An energy company faced significant data management issues, making business intelligence difficult. synvert built a Cloudera-based architecture, including a data lake and forecasting tools, enabling efficient energy production predictions and improved energy trading.
Initial situation
The customer had numerous problems with data management.
Data was spread across multiple systems and it was proving incredibly difficult to figure out exactly where certain data was located.
This made effective business intelligence virtually impossible.
Architecture
The architecture is built on Cloudera using the full Cloudera ecosystem and technologies such as Jupyter Hub, Spark, R, Oozie, and Hive.
Generated benefits
An example of how this all works in practice is the concept of using energy as a commodity on the trading market.
If you know in advance how much energy to expect, you can make better offers and better deals.
With a proper data lake and the necessary tools for rapid forecasting, data could be quickly analyzed to predict energy production.
Energy trading can then be conducted much more efficiently.
Services accomplished by synvert
synvert created a data lake where data could be consolidated and quickly accessed for business intelligence.
CDH tools were implemented to manage the data.
And proper additional health check systems, cluster maintenance logs, workshop checks, and housekeeping systems were added to ensure everything was running smoothly.