Databricks migration to streamline data processing for a Fortune 500 technology company

The client constantly collected lots of logistics and production data from their factories. Processing this data was challenging due to the sheer amount of it. Hence, the client aimed to consolidate all data in a single, highly scalable solution. This would allow them to optimize data processing, making it faster and more cost-efficient.

N-iX helped the client improve data integration and processing to ensure seamless management of vast amounts of information. First, we moved the client’s data from MS SQL Server repositories to Azure Synapse, which allowed us to integrate more data sources. We then migrated data from Azure Synapse to Databricks to ensure efficient resource utilization and reduce overall costs. We also utilized DBT and its capability to support cross-platform data transformations to complete the migration process quickly and efficiently.

Additionally, N-iX engineers implemented Azure Data Factory to enable seamless data migration and pipeline management for Databricks, enhancing the data handling capabilities. We used DBT as a development platform to save time, establish CI/CD, and automate documentation. DBT helped us automate the documentation of data lineage, which provided the client with a clear and accurate depiction of the logistics chain. It allows the client’s employees to track data from various sources and receive comprehensive insights into its journey and origin.

We also leveraged DBT’s flexibility to implement a separate dedicated computing environment that would not impact the overall performance and costs of the new system. It allows the client to evaluate the cost and resource usage of different data consumers over time. This, in turn, helps identify which segments are the most resource-intensive and potentially require further segmentation or optimization.

N-iX helped the client migrate data to a newly designed solution to improve data integration and processing. As a result, the client gained several substantial advantages:

• Optimized expenses associated with data processing, which resulted in only a 10% increase in operational costs over the year, while the amount of processed data increased by 5 times;
• Accelerated data processing with a solution that utilizes various advanced tools, including DBT, Azure Data Factory, and Databricks;
• Enabled effective handling of increasing data loads over time with a scalable solution that can match business requirements without compromising performance or efficiency.