Important notice: Beware of scammers pretending to represent InData Labs
Back to all case studies

Delivering Data Intelligence for Chemical Industry

Graph database solution for real-time sales and chemical composition analytics at scale.

bi chemical industry
Key Details

Graph database solution for real-time sales and chemical composition analytics at scale.

  • Challenge
    Unstructured production data with complex parent-child database relations making data querying resource-intensive
  • Solution
    Analytical solution for real-time sales, product analytics with Graph database to maintain complex relation, built on Azure
  • Technologies and tools
    Azure Databricks, CosmosDB, ADLS/Data lake service

Client

The client is a UK-based chemical manufacturer. The company was looking for a more efficient way to analyze the chemical composition of mixtures and products as well as their test results. The sales data of a particular chemical product also remained untapped requiring a robust analytical solution.

The client wanted to focus on a graph-based querying data solution that retrieves streaming data on sales and product chemical composition. The insights should then be visualized for recording and scoring information as well as becoming a reliable source of data-driven critical business decisions.

InData Labs was chosen by a client as a tech partner with seasoned Big data analytics solutions and Machine Learning Consulting services as well as experience in deploying AI in chemical industry.

Challenge: unstructured production data with complex parent-child database relations making data querying resource-intensive

The main issue was that the client’s existing data storage approach revolved around unstructured production data that came in various stages and forms. Besides, the data had complex parent-child relations that required to be maintained for querying and follow up analysis.

A new compiling process was required that would include newly created metadata and database relations to request information using a graph database without utilizing much computing power as well within a time constraint of 5 seconds.

Solution: analytical solution for real-time sales, product analytics with Graph database to maintain complex relation, built on Azure

Our Big data engineering team performed a thorough analysis of the client’s requirements and bottlenecks. The following scope of work was performed to transform the data:

  • Imported, cleaned, and transformed data from CSV files using Azure Databricks.
  • Defined relations and transformed data in the form of graphs in Azure Databricks Pushed it in Cosmos DB and queried data and graph using Azure Cosmos graph.
  • Established interoperability with a graph-based query on Azure Cosmos DB.
  • Set up real-time streaming in Power BI for recording and scoring.

The whole transformation process was carried out by three Big data specialists, including a data architect, data engineer, and BI expert. The business intelligence project was completed in 6 months.

Here is the solution architecture that demonstrates this case study of the chemical industry and data lifecycle:

Delivering Data Intelligence for Chemical Industry

Result: harnessing chemical and sales data with BI & reporting solution

As a result of our collaboration, we have transformed the client’s data stack and delivered a tailored advanced data analytics solution that drastically improved decision-making, real-time analytics, and insight extraction. The company now has a comprehensive data-driven overview of chemical composition analysis as well as historical customer data that is visualized within a scalable platform for self-service and enterprise business intelligence.

Tags:
  • Manufacturing
  • BI Implementation
  • BI
  • Big Data

Contact InData Labs

Want to start getting value from your data? Fill the form. Click send. Let's talk.

    By clicking Send Message, you agree to our Terms of Use and Privacy Policy.