Our client's journey of transforming their data analytics platform, addressing key challenges, implementing advanced technologies, and achieving measurable business impacts. Migration of a large-scale Teradata data warehouse, with 5 petabytes (PB) of data and 200,000 database objects, from an on-premises environment to Google Cloud Platform (GCP).
Data processing time was reduced by 40%, enabling quicker decision-making. after conversion of datastage jobs to pyspark and Cloud Data Fusion.
Automation reduced manual efforts by 30%, freeing up resources for strategic initiatives. Our Code Converter tools convert 200K object to biqquery sql, pyspark and Airflow
Infrastructure costs were cut in half by leveraging cloud-based solutions.
Implemented real-time data analytics to improve decision-making speed.
Banking & Financial Services
UK/Europe
90000+ Employees
Our client faced significant challenges in handling large volumes of data, leading to delayed insights and inefficient processes. They also needed a scalable solution to accommodate future data growth and maintain compliance with strict regulatory requirements.
Migrating large Teradata objects required a meticulous approach to ensure data integrity and performance. We implemented a phased migration strategy to handle the complex data structures and minimize disruptions using our code converter tool.
Transitioning from DataStage jobs to a more modern ETL solution was essential. We re-engineered the ETL workflows to leverage Google Cloud Data Fusion, Dataflow, dataproc and airflow, ensuring efficient data processing and transformation.
To address discrepancies and ensure data accuracy, we performed comprehensive data reconciliation. This involved validating migrated data against source systems to confirm consistency and reliability. developed 20+ dashboard to monitor data reliability.
Our migration strategy was designed with a focus on the end user's needs. We implemented a data hub approach to streamline data consumption and provide users with timely and accurate insights.
By leveraging modern cloud infrastructure and advanced analytics platforms, the client was able to significantly improve operational efficiency, reduce costs, and enhance their data-driven decision-making capabilities.
Our client's journey of transforming their data analytics platform, addressing key challenges, implementing advanced technologies, and achieving measurable business impacts. Migration of a large-scale Teradata data warehouse, with 5 petabytes (PB) of data and 200,000 database objects, from an on-premises environment to Google Cloud Platform (GCP).
Data processing time was reduced by 40%, enabling quicker decision-making. after conversion of datastage jobs to pyspark and Cloud Data Fusion.
Automation reduced manual efforts by 30%, freeing up resources for strategic initiatives. Our Code Converter tools convert 200K object to biqquery sql, pyspark and Airflow
Infrastructure costs were cut in half by leveraging cloud-based solutions.
Implemented real-time data analytics to improve decision-making speed.
Banking & Financial Services
UK/Europe
90000+ Employees
Our client faced significant challenges in handling large volumes of data, leading to delayed insights and inefficient processes. They also needed a scalable solution to accommodate future data growth and maintain compliance with strict regulatory requirements.
Migrating large Teradata objects required a meticulous approach to ensure data integrity and performance. We implemented a phased migration strategy to handle the complex data structures and minimize disruptions using our code converter tool.
Transitioning from DataStage jobs to a more modern ETL solution was essential. We re-engineered the ETL workflows to leverage Google Cloud Data Fusion, Dataflow, dataproc and airflow, ensuring efficient data processing and transformation.
To address discrepancies and ensure data accuracy, we performed comprehensive data reconciliation. This involved validating migrated data against source systems to confirm consistency and reliability. developed 20+ dashboard to monitor data reliability.
Our migration strategy was designed with a focus on the end user's needs. We implemented a data hub approach to streamline data consumption and provide users with timely and accurate insights.
By leveraging modern cloud infrastructure and advanced analytics platforms, the client was able to significantly improve operational efficiency, reduce costs, and enhance their data-driven decision-making capabilities.