Business requirements analysis - Conducting detailed analysis of business needs and data requirements. Identifying the type of data to be stored, its structure, and relationships.
Architecture design - Determining the appropriate architecture for the data warehouse/data lake, considering the type of data, scalability, and performance. Choosing suitable technologies and tools, such as databases, data processing tools, cloud computing, etc.
Data modelling - Developing data models, including schemas, tables, relationships, and keys.
Data integration - Implementing “extract, transform, load” (ETL) processes to move data from multiple, disparate sources to the data warehouse/data lake. Creating data pipelines for continuous data streaming and updates.
DWH/data lake testing - comprehensive testing set, which can include ETL/ELT testing, BI testing, DWH performance testing and security testing.
DWH/data lake implementation and support - building a DWH tailored to each customer’s unique data consolidation/storage needs and implementing it with the  target ecosystem. Providing DWH support to identify and solve performance issues, ensuring stability for timely and high-quality data flow to business users, and reducing storage and processing costs.