Reporting Solution with Azure Data Factory and Python
Industry: Manufacturing and Quality Control
Data analytics projects frequently face a variety of challenges, such as:
Consolidating quality data from multiple sources, including machining, assembly, test rig, and production order data, into a single reporting system presented difficulties for a manufacturing organization. Protight, MES Traceability, TRS, EIQMI, and other platforms provided the data, which was stored in several databases (SQL, Oracle, SharePoint, and cost center folders). It was challenging for the business to provide consolidated reports and digital quality certificates on time because of this dispersed data.
- Ensuring accurate, comprehensive, and trustworthy data for analysis is known as data quality.
- Data security: preserving compliance and safeguarding private data
- Data integration is the process of combining information from many forms and sources.
- Ensuring consistent and trustworthy analytical results is known as reproducibility.
- Ethical Considerations: Resolving issues with bias, privacy, and data use
- Interpreting Complex Results: Providing Actionable and Accessible Insights
Solution Overview:
The company achieved an automated, end-to-end reporting solution by using Azure Data Factory (ADF) as the primary data integration service and Python for data transformation, cleaning, and report generation. This solution allowed for real-time data processing, seamless data flow, and comprehensive reporting capabilities, which helped the company maintain high standards and streamline its reporting process.
Solution Architecture
Data Ingestion:
As seen in the provided process image, Azure Data Factory retrieves data from multiple data sources, such as SQL databases, Oracle databases, and SharePoint. Different data kinds, such as machining data, assembly data, and quality check results, are represented by each source.
Data Cleaning and Processing:
The solution cleans and processes data using Python scripts inside ADF pipelines. In this process, data from various forms are combined, inconsistencies are eliminated, and raw data is transformed into a single format for analysis.
Data Storage:
A centralized common database, which forms the basis of reporting and analytics, houses cleaned data. Easy access and query optimization for reporting tools are made possible by this uniform data store.
Data Visualization and Reporting:
To provide real-time insights into quality measurements, reports are created using Power BI, Tableau, or Python-based visualization tools. For every production batch, this procedure guarantees that digital quality certificates are current and easily accessible.
Digital Quality certifications:
To guarantee adherence to quality standards and give stakeholders convenient access to certification documentation, the final reports and certifications are produced and saved digitally.
Data Analysis
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Benefits
This approach ensures that organizations not only address data challenges but also deliver the highest quality solutions that meet their unique business needs.