Our approach combines various data architecture and engineering methodologies, design patterns and features such as federated query. The design and implementation of the lakehouse is aligned with business objectives such support for diverse data structures, BI tools and multiple workloads including SQL and ML.
ITTStar's data engineers have developed frameworks to accelerate design, and implementation of lakehouses with high performance, low latency and optimized cost structure to the level of data storage and computing power. The robust design allows data aggregation from multiple sources and formats without the tedious ETL processes.
Identifying & implementing best repository for each dataset
Designing tables in Redshift using best practices
Using federated queries to provide access to data from various sources without ETL
Optimizing S3 datalake for high performance and low cost
