You’re looking to learn data warehousing. How can you do it quickly and effectively?

Read the Kimball and Inmon books. Then read them again. Find a mentor who has worked on data warehouses and discuss both books and their personal experience with the technology. If you have access to a database - design, build and load a data mart. There is no substitute to actually working on one, but very little opportunity (you need to be trusted, and trust comes from experience).

Even at the current speed at which data warehousing solutions and technologies are evolving, understanding and knowing the foundation is core. Most often through quick starts offered through different data warehousing solutions, it is easy to get carried away with cool features, but at the end, when it comes to comprehending what happens underneath, knowing foundational data can help you really grasp complex solutions. So it is always good to learn the fundamentals of data warehousing like ETL, OLAP, OLTP, SQL, Data modelling techniques and best practices, data structures, dimensions and facts...etc. With that knowledge, then diving into practical through a popular data warehousing platform can give you a much needed practical kickstart.

DW can be learnt by attending some trainer classes and then working under a good DW leader to get the practical aspects and nitty gritty.

Learning data warehousing efficiently involves understanding fundamental concepts like ETL processes, dimensional modeling, and data integration. Utilize online courses, books, and hands-on practice with tools like SQL Server or Oracle. Master ETL tools like Informatica and dimensional modeling techniques such as star schema. Engage in online communities for discussions and continuous learning. Consider certifications like CDMP and build a portfolio showcasing your skills. Continuous practice and staying updated with industry trends are key to mastering data warehousing.

?? To learn data warehousing quickly and effectively, start with a holistic understanding of these aspects: 1) technical - slowly changing dimensions, data movement (ETL), data change capture, Kimball Methodology, and data modeling. The data warehouse: Redshift, Snowflake, Bigquery are some examples. 2) Data - the variety, veracity, format, velocity, volume of the data, and the value of the data. 3) Business / Viz - since you are developing the data warehouse to meet the business needs, you need to understand the business and how it uses the data. Will the data be used for analytics, reporting, dashboards, KPIs or potentially data scientists? These are all vital as you develop a data warehouse. Start with the end in mind.

Selecting the right tool is crucial, as it will be your primary interface for working with data. Comparative Study: For example, while Amazon Redshift is renowned for its scalability, Snowflake offers a unique architecture that separates compute from storage, allowing for more flexibility. Engaging with community forums or trial versions can offer hands-on comparison experiences. Learning Resources: Each tool has its ecosystem of learning resources. Oracle Data Warehouse, for example, provides extensive documentation and community forums. Snowflake's "Snowflake University" offers structured courses and certifications.

Choosing the right tool is an important step after optimizing data warehouse concepts. Many tools support the design, construction, and management of data warehouses and stores. Notable features include Microsoft SQL Server, which provides a comprehensive platform including SSIS, SSAS, and SSRS. Oracle Database offers ODI, OLAP, and BI. Amazon Redshift, a cloud-based service, integrates seamlessly with other AWS services, making it easy to store and query large amounts of data. Similarly, Google BigQuery, another cloud-based service, supports extensive data analytics, integration with Google Cloud services, capture, pull, and ETL services.

Select data warehousing tools that align with your learning objectives and the skills you wish to acquire. Whether it's a cloud-based platform like Amazon Redshift or Snowflake or an on-premises solution such as Microsoft SQL Server, each offers unique features and learning opportunities

Select a data warehousing tool that aligns with your goals and the requirements of your projects. Popular options include Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure SQL Data Warehouse.

I think fastest way to learn data warehouse and data engineering techniques by doing loads of enhancements while maintaining and extending an existing data warehouse . We can quickly learn below : 1 . Dimension modeling 2. Data warehouse modeling techniques 3. SCD1 , SCD2 mapping techniques and use cases to apply them. 4. Incremental load , CDC techniques 5.Metadata to be kept in DW 6. Data Quality 7. Source to Target mapping - how a transactional system DB and DW DB is different in table structures and how it helps in reporting from DW. While working on them , if we can also read books on Ralph Kimball,Inman and modern data warehousing techniques and understand theories and evolution of DW- I think we will get the best result .

Engaging in hands-on learning is an excellent method for understanding a concept. Take a real time example of a dataset and try to apply data warehousing concepts to it. For example, consider creating a data warehouse using your bank and credit card statements. The goal could be to organize data by spending category, segmented by timeframe (weekly, monthly, or quarterly). Develop dimension and fact tables tailored to this use case. To further enhance this, extend it to your savings goals to determine how much you need to spend each month to achieve your saving goals. Once you have the data warehouse tables and relationships ready, reach out to an industry expert to review your work.

Learning by doing is great but it’s important to make it fascinating. Want to start from creating fact and dimensions tables? It can be boring. Start with a visualization. Let’s implement something complex. Is it slow? Accelerate it! See what query your dashboard sends to the DB. Try query optimization. Try saving transformed data in a warehouse. Compare results. Try full and incremental refresh options. Find what else to improve. Do somethind and see results as in a well organized workshop.

I think the most effective way is to learn by doing. Using the ideas and techniques gained to build a personalized data warehouse or data mart using data types of interest is invaluable. Online platforms such as Kaggle, Coursera, or Udemy, which offer a variety of data, services, and courses, enhance this learning experience. Learning supervision provides a practical understanding of concepts and principles through hands-on, interactive activities that allow testing and validation of knowledge, performing various projects to demonstrate skills, and receiving feedback and support from the data warehouse community .

Salesforce , MS , Google , PMI , Axelos , the US / EU / Educational institutions and so many more platforms offer datasets and even Non-Profit clients which you may work on ( do not expect remuneration at this stage) with more experienced DW professionals. The application of your test environment work ,theory and courteous request for coaching from your new peer network will exponentially increase your learning curve when applied in the "Real world" while you are also paying it forward / giving back to your community.

Given the evolution of the industry, it’s important to be aware of current trends in data storage. To stay informed, one can follow data warehouse-related blogs, podcasts, newsletters and social media accounts. For example, the Oracle Data Warehouse Insider blog offers insights and best practices. The Data Engineering Podcast covers trends in data engineering, warehousing, and science. Subscribing to the Data Warehouse Newsletter provides access to articles, videos and podcasts on relevant topics. The Data Warehousing subreddit on Reddit is a place for discussion, questions, and valuable resources in the data warehousing and analytics community. Using such features keeps them up to date with the latest developments in the industry.

Data warehousing is a dynamic field with continuous innovations and best practices. Stay updated by following industry blogs, podcasts, newsletters, and joining relevant online communities. This will keep you informed about the latest technologies and methodologies.

Stay updated with the latest trends, technologies, and best practices in data warehousing. Follow industry blogs, attend webinars, and participate in online forums to stay informed about advancements in the field.

Staying current is non-negotiable in the rapidly evolving data warehousing landscape. Emerging Technologies: Familiarize yourself with the latest in cloud services, such as Google BigQuery's machine learning capabilities, which allow users to run ML models directly on stored data without needing specialized infrastructure. Real-World Applications: Study how modern businesses are leveraging data warehousing solutions. Netflix, for instance, utilizes data warehousing for content recommendation algorithms, analyzing viewer habits to suggest personalized content.

Engage with data warehousing communities through forums, LinkedIn groups, meetups, and other networking platforms. Sharing experiences, challenges, and solutions with peers can provide insights and opportunities that accelerate your learning process.

There is too much out there , too many official databases , too many ETL's , Datawarehouse's , Cube designs , Data marts , Data lakes and integration methodologies to be an expert on all. Your peers however will have worked on something you have not and if you are reading an article such as this, it is evident that we / you are willing to help when asked politely as well as give evidence that you have tried to get it right yourself and not simply "outsourced" , your learning and your network will not benefit from the latter scenario.

Join data warehousing communities and engage with peers who are also learning or working in the field. Networking provides opportunities for knowledge sharing, collaboration, and gaining insights from others' experiences.

The collective wisdom of a community can significantly accelerate learning. Success Stories: Engage with case studies or testimonials within your network to understand different approaches to problem-solving. For example, how did a peer overcome data integration challenges from disparate sources? Professional Groups: Platforms like Meetup or LinkedIn groups host regular discussions and webinars. Join groups like "Data Warehouse, BI and Analytics Professionals" to connect with like-minded individuals.

As you embark on this learning journey, keep these additional pointers in mind: Certifications: Earning a certification, such as the AWS Certified Data Analytics - Specialty, not only bolsters your resume but also provides a structured learning path. Real-World Challenges: Apply your skills to real-world scenarios. Participate in Kaggle competitions that involve data warehousing concepts to test and showcase your abilities. Ethical Considerations: Delve into case studies, such as how companies navigate data privacy while leveraging big data analytics. This will prepare you for the ethical considerations of handling sensitive information.