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In the Strategy & Architecture phase, our data team collaborates with the business and various application owners to understand the business goals/issues which are driving the requirement for a redshift implementation. The purpose of this phase is to understand what data is available, where the data is sourced, the respective transformation processes, and the downstream use cases of the data. An understanding of this enables our team to design the best architecture to support the business needs. Analyzing the data sources and what is available gives our team an idea of the scope of transformation and aggregation that will be necessary to land the data into Redshift.
The data model is vital to the Redshift implementation strategy. After gaining an understanding of the data sources and the business needs of the organization, Effectual’s team interviews specific users and use cases to gain an understanding of the data model that Redshift will need to support. This includes identifying the data dictionary – attributes (dimensions) and facts (metrics) that are important KPIs and drivers to the respective users.
Phase two, the build and implementation stage, is where the team builds the necessary data ingestion and transformation pipelines which land the source data into Redshift. Once the data is landed into Redshift in the correct data model to support the business needs, aggregated views can be created and precalculated to support different queries. These views are extremely important when it comes to reporting structure and predictive analytics models.