![]() In this data warehouse tutorial we’ll review the basic elements of a data warehouse, with a special emphasis on what’s new and different in cloud-based data warehouse architectures, and how they can move your data team light years forward. The data warehouse focuses on data relevant for business analysis, organizes and optimizes it to enable efficient analysis. What is a Data Warehouse?Ī data warehouse is a decision support system which stores historical data from across the organization, processes it, and makes it possible to use the data for critical business analysis, reports and dashboards.Ī data warehouse system stores data from numerous sources, typically structured, Online Transaction Processing (OLTP) data such as invoices and financial transactions, Enterprise Resource Planning (ERP) data, and Customer Relationship Management (CRM) data. ![]() To learn how to set up a data warehouse and go from data to insight in minutes, see Panoply’s getting started guide. For a detailed step-by-step guide, consult the user documentation of your data warehouse product of choice. Note: This conceptual tutorial provides overviews and best practices for major aspects of data warehouse infrastructure. The Future of the Data Warehouse-an analysis of cloud-based data infrastructure and next-gen data warehouses, capable of creating new value and possibilities for your data team.Data Warehouse Backup-models for backing up critical data warehouse data, and new backup strategies in the cloud.Data Warehouse Security-an understanding of user access, data movement, auditing and documentation.The ETL Process-a brief review of the traditional ETL process step-by-step, data warehouse testing, and a new, flexible data loading process based on Extract-Load-Transform (ELT).Ralph Kimball’s departmental Data Marts and partitioning strategies in the cloud. Data Warehouse Partitioning and Data Marts-a survey of Bill Inmon’s Enterprise Data Warehouse vs.unstructured data, basics of data modeling, multidimensional data modeling, and snowflake vs. Data Warehouse Modelling and Schemas-an overview of structured vs. ![]() Data Warehouse Design Process-the traditional waterfall design process, and how cloud technology is making data warehouses agile and flexible.Data Warehouse Infrastructure and Technology-a quick review of Simple Query Language (SQL) as a foundation for the data warehouse, Extract-Transform-Load (ETL) infrastructure and tools, and Online Analytical Processing (OLAP) servers.Data Warehouse Architecture-traditional three-tier architecture vs.Data Warehouse Concepts-traditional vs.Data Warehouse Solutions-popular data warehouse products, including on-premise and cloud.What is a Data Warehouse? A basic definition, and the difference between data warehouses, data lakes and relational databases.This conceptual guide introduces important aspects of data warehousing, with links to additional resources to help you go in-depth: Data Warehouse Guide Data Warehouse Tutorial
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