Handling Semi Structured Data using Databricks

WeDistro, a leader in logistics and inventory management, is facing challenges with managing complex, semi-structured data from multiple sources. With varying schemas and the need for idempotent data processing, how can they efficiently streamline operations and unlock actionable insights?

WeDistro, a prominent player in the distribution and logistics industry, is currently facing significant challenges with its semi-structured data management. The company operates across multiple distribution centers and relies heavily on data to manage inventory, restocking, and vendor performance. However, the use of semi-structured JSON data has introduced issues that are slowing down operations and hindering decision-making.

As a subsidiary of a larger logistics conglomerate, WeDistro has adopted data-driven strategies to optimize its supply chain and inventory management processes. Unfortunately, the growing complexity of their data has created friction between the company’s operational goals and its ability to provide stakeholders with timely and accurate insights.

The issues affecting WeDistro include:

• Inconsistent schema across various records, making it difficult to maintain uniformity in data.
• Nested fields and arrays in the JSON data, making it difficult to extract actionable insights.
• Challenges in converting this semi-structured data into a format that can be easily analyzed for key insights on inventory management and vendor performance.

This lack of data clarity is eroding trust in WeDistro’s data systems, much like the challenges experienced by other companies struggling with growing data complexity. As a result, the company risks operational delays, stockouts, and inefficiencies in restocking and inventory management.

As a data engineer, your mission is to overcome these data challenges, ensuring that WeDistro can continue to deliver optimized and efficient logistics solutions. Your task is to streamline the semi-structured data, enabling stakeholders to regain trust in the data and improve decision-making based on actionable insights.