Data warehousing is a skill that can help you store, manage, and analyze large volumes of data from different sources. It can also enable you to create reports and dashboards that provide insights and support decision-making. But how can you master data warehousing skills? Here are some tips to help you learn and improve your data warehousing knowledge and abilities.
Before you dive into the details of data warehousing, you need to have a solid grasp of the fundamental concepts and principles. You should know what a data warehouse is, how it differs from a database, what are the benefits and challenges of data warehousing, and what are the main components and architectures of a data warehouse. You should also be familiar with the key terms and processes involved in data warehousing, such as data modeling, data integration, data quality, data mart, dimensional modeling, star schema, snowflake schema, ETL, OLAP, and BI.
Many a times, technological people think that DWH is a collection of Facts & Dimensions tables and ETL tool to ingest data into them, for the sole purpose of reporting. But DWH is much more than that. It has to be seen from the usability perspective or more importantly it should be built for Analytical Decision making and envisioning data insights. Understanding Data models and their normalized relationships is another key in mastering DWH along with the very principles of DWH: SUBJECT ORIENTED, TIME VARIANT, INTEGRATED and NON-VOLATILE. Notwithstanding the evolution of modern day tools and solutions such as Data Lakes, Cloud Storages, Ingestion mechanisms, Curation techniques, the concept of DWH is still intact.
Data warehousing can be done using various tools and platforms, depending on your needs, preferences, and budget. You can use traditional on-premise solutions, such as Oracle, Microsoft SQL Server, IBM DB2, or Teradata, or you can opt for cloud-based services, such as Amazon Redshift, Google BigQuery, Microsoft Azure SQL Data Warehouse, or Snowflake. You can also use open-source options, such as PostgreSQL, MySQL, or Apache Hive. Each tool or platform has its own features, advantages, and limitations, so you should do some research and comparison before you decide which one to use.
The best way to master data warehousing skills is to practice them in real or simulated scenarios. You can find online courses, tutorials, books, or blogs that offer hands-on exercises and projects that will help you apply what you learn and test your understanding. You can also use online platforms, such as Kaggle, DataCamp, or Coursera, that provide data sets, challenges, and feedback for data warehousing learners. You can also create your own data warehouse using your own data or public data sources, and experiment with different tools, techniques, and queries.
Once you have identified your area of expertise, be it data engineering, data analysis etc advance your knowledge consistently by 1. Revamp on your skills by picking mini projects to do - there are plenty of blogs/you tube videos/udemy courses 2. Document your findings in github/medium and share your learnings with people on linked in. 3. Update your knowledge and the trends with blogs. Join Discussion forums and help people with queries or understand other ways things can be done. 4.Follow people who are experts in the area to know what "else" is new
Data warehousing is not only a technical skill, but also a strategic and organizational one. You need to follow some best practices to ensure that your data warehouse is reliable, efficient, secure, and scalable. Some of these best practices include: defining clear business requirements and objectives, designing a logical and consistent data model, ensuring data quality and accuracy, optimizing data integration and transformation processes, implementing proper data governance and security policies, and monitoring and evaluating the performance and usage of your data warehouse.
Data warehousing is a dynamic and evolving field, with new trends, technologies, and challenges emerging constantly. You need to keep learning and updating your skills to stay relevant and competitive in the market. You should follow the latest developments and innovations in data warehousing, such as data lakes, data virtualization, data streaming, data warehousing as a service, or artificial intelligence. You should also seek feedback and guidance from experts, mentors, or peers in the data warehousing community, and participate in forums, webinars, podcasts, or events that can help you learn from others and share your knowledge and experience.
Some parts of this paragraph are a bit misleading. Things like Data lakes or AI whilst being very important in the global context of data analytics, are concepts completly different from what a DataWarehouse should be.
Consider looking at the online documentation around the Kimball methodology. Immon might have coined the term DataWarehouse back in the 70s, but it was Kimball that brought an actual implementation that is widly used to these days. And all that knowledge is online free for access to anyone who wants to learn.