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Machine Learning Based Chemical Composition Analysis

Chemical Composition Data Analytics

Overview

The client is a UK-based chemistry company that devises chemical solutions for a sustainable future. 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.

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20944236 [Converted] 1

Chemical Industry

Overview

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.

Features

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 utilising much computing power as well within a time constraint of 3 seconds.

Scope

New Product

Industries

Chemicals

Services

Ad-hoc

Technologies

Challenge

01

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.

02

AARCHIK 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.
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Challenge

01

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.

02

AARCHIK 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.
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Solution Features

01

Imported, cleaned, and transformed data from CSV files using Azure Databricks.

02

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.

03

Established interoperability with a graph-based query on Azure Cosmos DB.

04

Set up real-time streaming in Power BI for recording and scoring.

Solution Features

01

Imported, cleaned, and transformed data from CSV files using Azure Databricks.

02

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.

03

Established interoperability with a graph-based query on Azure Cosmos DB.

04

Set up real-time streaming in Power BI for recording and scoring.

Result

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.
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Result

Increased infrastructure efficiency by reducing the number of on-premise VMware servers