Transdev, a globally recognized public transportation operator, has been diligently serving passengers worldwide since its inception in 1954. Their core mission is to offer safe, reliable, and efficient transportation solutions, ensuring seamless mobility for passengers. Its extensive global presence and commitment to public transportation cover a wide range of services, making them a versatile and essential provider in the industry. Whether it's buses, trains, trams, or other forms of transit, Transdev is a trusted partner dedicated to enabling convenient and confident public transportation experiences for people worldwide.
Our team took on the challenging task of migrating our database from Microsoft Azure to Amazon's AWS. This involved developing and executing an Extract, Transform, and Load (ETL) system to carefully extract, transform, and load data into AWS for a seamless transition. Additionally, we conducted data analytics tasks to analyze data in AWS and derive meaningful insights for informed decision-making. In summary, our multifaceted effort included database migration, ETL system implementation, and data analytics, ensuring a successful transition to AWS and leveraging data effectively.
Our team consisted of experienced engineers, including cloud experts and system analysts, ready to tackle complex challenges. We began by thoroughly studying Amazon's AWS and Microsoft's Azure cloud services, ensuring a deep understanding of these platforms. Due to our work with large volumes of big data, data security was a top priority. We implemented strong security measures and robust data backup and recovery processes to mitigate data loss risks. Overall, our skilled team combined expertise, continuous learning, and vigilant security to navigate cloud engineering complexities, ensuring secure data management.
In the process of transferring data between different cloud environments, we followed established methods to ensure secure and seamless data migration, minimizing data loss risks. We handled a significant data volume with caution, aiming for effective dataset management. This involved applying well-defined business rules to clean and organize raw data for storage, analytics, and machine learning. Centralizing data into a warehouse streamlined access, analysis, and utilization, enabling better insights and informed decision-making. In summary, our approach involved a structured methodology, thorough data cleaning, and centralization to effectively manage large datasets while ensuring data security and integrity during transfer.