CASE STUDY
Industry | Retail
Technology | Databricks
Location | United States
The client is a large omnichannel retailer, which has an effective structure of brick-and-mortar stores and an excellent e-commerce platform. A legacy Talend ETL environment was used to conduct their critical business operations and process terabytes of data per day across key areas such as merchandising, supply chain, and customer analytics. Their system had developed into a complicated network comprising of about 150 interdependent jobs. This left a huge bottleneck in the operations, the scaling was restricted, at the busiest times, and the introduction of new insights was sluggish, which also created governance issues. The business need was obvious: modernize this expensive and inflexible data infrastructure and open up flexibility, guarantee reliability and future expansion.
Royal Cyber collaborated with the retailer and created an overall platform analysis, evaluating all the aspects, such as data ingestion and pipeline orchestration. The complete data process had to be updated to be scalable, efficient and governed. We started by mapping the intricate meshwork of the old Talend dependencies and then we concentrated our attention on the modernization core transformation logic and developing a sound framework of validations to have a smooth migration.
Scalability Constraints: The exorbitant High Talend licensing fees and rigid on-premise infrastructure were not able to accommodate peaked retail loads and was delayed.
Slow Productivity: Due to Talend complex and manual jobs, delivery of new data products was a very slow process leading to business inertia.
Poor Governance: Disjointed information, irregular quality controls, and information siloing resulted in headaches in compliance and auditing.
Complex Migration Sequencing: It was essential to the safe and rational migration plan to unweave a web of 150+ Talend jobs that were dependent on each other.
Data Parity Requirement: Business users needed to have the new Databricks outputs to be identical to the old Talend results, which demanded perfect validation.
Varied Workload Patterns: The combination of batch files, database extracts, and real time feeds necessitated a specific modernization strategy with each category.
40%
Reduction in ETL Job Failures during peak seasonal processing, ensuring business continuity.
50%
Faster Development Cycle for new data products and pipeline modifications.
70%
Reduction in Infrastructure & Licensing Costs by migrating off the legacy Talend stack.
60%
Improvement in Data Pipeline Performance for critical inventory and reporting jobs.
Structured Migration Methodology: A four step process (Discover, Assess, Convert, Validate) was used to provide a controlled and risk managed transition.
Modernization with Lakehouse Tools: To modernize pipelines, we re-engineered pipelines with native Databricks services, such as Delta Live Tables to orchestrate and Auto Loader to ingest data efficiently.
Unified Governance and Ops: Unity Catalog offered centralized access control and lineage, and Databricks Workflows as well as Git-based CI/CD transformed operations.
Automated Validation Framework: Before cutover, an embedded system was used to carry out parallel checks to ensure accuracy of data between the old and the new pipeline.
Workload-Specific Strategies Jobs were categorized based on criticality (Critical, Standard, Archive) to implement the appropriate migration tactic, parallel runs to retirement.
Team Enablement and Process Shift: We retrained the employees about the new platform, transformed the working model into collaborative and Git-integrated workflow.
The team excelled in implementing a customised compliance and customer privacy solution with the invaluable assistance of Databricks. Our customers now have enhanced trust in our services, enabling us to operate securely and confidently.
Director- AI/ML Solutions
70%
Reduction in Infrastructure cost
Audience
- Executives, CTOs, Director
- IT Consultants
- Data Engineering Managers
- Chief Data Officers
- IT Project Coordinators
- ETL/Data Pipeline Architects and Engineers
