Just like building a suspension bridge or a subway tunnel, a well-detailed blueprint goes a long way in easing the implementation of the project. Data architecture depends on components such as data sources and integration, Extract, Transform Load (ETL) processes, Data modelling, Data Storage, Data Access and Security and Data Governance. The components serve as pillars towards building and maintaining data warehouses across business intelligence environments.
Data sources pinpoint a digital location where numerous valid databases can be outsourced. Depending on the data formats, extracting quality and ensuring the system maintains consistent standards can be an issue. Reinforced data integration ensures that data warehouses accommodate different data types from diverse data sources. Standardisation and improved data accessibility generated from ETL processes promote consistency, ensuring a business’s objectives are met.
Optimising storage efficiency ensures a data warehouse is functional. Analytical queries support the extraction and analysis of large volumes of historical data in a warehouse. Under Modern Data Architecture, supported by tools such as Snowflake, dimension and fact tables offer a common structure for data stored. Data engineers use dimensional queries to filter and slice dimensional tables housing information on classes such as location and product name. Fact tables use relational measurement metrics, storing facts and foreign keys used to join tables during querying. Both dimensional query and fact tables are created in a snowflake schema, providing a gateway to efficient analysis and reporting.
Data warehouse is profoundly relied upon in fields such as retail-inventory management and customer segmentation, Manufacturing- quality control, healthcare- reducing operational risk, telecommunications- customer behavioural analysis, commerce – forecasting and customer segmentation.
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