Birlasoft helps a Fortune 500 Company in DWBI Platform Migration for Energy and Utility, empowered the organization to maximize data processing capabilities, improve operational efficiency, and make timely and informed decisions.
The Challenge
The existing data warehouse faced significant challenges in terms of data load duration, with the end-to-end process taking more than 12 hours to complete. This impacted the timeliness of analytics and decision-making. Another challenge was the extraction of data from multiple sources, which posed difficulties in consolidating and integrating data efficiently, resulting in delays and potential data inconsistencies. These challenges affected the organization’s operational efficiency and decision-making.
Executing jobs from scratch, in case of failure, was a challenge. This led to inefficient use of resources and increased processing time, impacting job performance and productivity. Additionally, the extensive use of Oracle PL/SQL code affected maintenance and scalability. Complexities increased as the codebase grew larger, affecting the efficiency and reliability of data operations. These challenges necessitated finding solutions to streamline job execution and optimize the usage of PL/SQL code.
The higher cost associated with using Oracle EXA data presented a challenge. Deploying and managing Oracle EXA hardware and software required substantial investment. This limited the scalability of utilizing Oracle EXA data and impeded the organization’s ability to leverage advanced features for data management and analytics. Finding cost-effective alternatives or optimizing resource utilization was crucial to mitigate the financial burden while still reaping benefits from the platform.
The Solution
As part of the solution, the ETL code was migrated from OWB/ODI to Matillion ETL, a modern and cloud-native data integration platform. This allowed for more streamlined and efficient ETL processes. Additionally, the Oracle PL/SQL code was migrated to Matillion Jobs, ensuring seamless integration and execution within the Matillion environment. This eliminated the dependency on Oracle-specific code and provided a unified platform for managing data pipelines. It also enabled enhanced scalability, flexibility, and performance, empowering the organizations to leverage cloud-based data integration and transformation capabilities for efficient data processing and analytics.
As part of the solution, Snowflake functions were utilized to create reusable components. These functions eased the implementation of standardized logic across multiple data pipelines, reducing redundancy and improving productivity. Additionally, orchestration and transformation jobs were created to streamline the end-to-end data processing flow. These jobs provided a structured framework for data transformation, data integration, and data movement tasks, ensuring consistent and reliable data processing. The scheduling of jobs was implemented to automate the execution of data workflows, minimizing manual intervention and ensuring timely processing. Overall, this solution enhanced productivity, reusability, and efficiency in data processing while reducing operational overheads.
The Impact
The solution optimized data processing workflows, leveraged advanced technologies, and streamlined data integration processes to reduce data-loading time. This allowed the organization to access timely and accurate insights, enabling faster decision-making and enhancing operational efficiency. The solution also enhanced overall productivity, minimized downtime, and improved the organization's ability to leverage data for critical business processes. Overall, the solution contributed to increased agility in the ever-evolving data-driven landscape.
The solution enhanced data processing by centralizing business logic and simplifying the debugging process. By consolidating the logic, the organization minimized errors. Additionally, structured job scheduling ensured smooth execution and timely completion of critical tasks. This improved coordination and productivity, enabling seamless data processing operations. With optimized debugging and streamlined scheduling, the solution empowered the organization to maximize data processing capabilities, improve operational efficiency, and make timely and informed decisions.
The solution tapped into Snowflake credits usage to prove itself cost-effective. This cost-effective approach allowed organizations to maximize their data processing capabilities while minimizing operational expenses. The solution's efficient resource allocation and utilization ensured that businesses could achieve their data processing goals without incurring excessive costs. With reduced Snowflake credit usage, the organization effectively managed its budgets and allocated resources to other critical areas, driving overall financial efficiency and performance.
