Here's how you can develop the essential technical skills for a Data Warehousing career.

To develop essential technical skills for a Data Warehousing career, start by mastering SQL. SQL is the core language for interacting with relational databases and is fundamental to working with any data warehouse. Focus on learning how to create, read, update, and delete data using basic SQL queries, then gradually advance to more complex tasks like joins, subqueries, and optimization techniques. For example, in a data science project, SQL is used to retrieve large volumes of customer data, perform aggregations, and run complex analytical queries.

Before you start SQL the very first step is to lean fundamentals Start with basic data warehousing concepts, such as OLAP (Online Analytical Processing), OLTP (Online Transaction Processing), data lakes vs. data warehouses, and the importance of structured vs. unstructured data. Study concepts like star schema, snowflake schema, dimensional modeling, fact and dimension tables, and data marts.

To build essential technical skills for a successful career in Data Warehousing, focus on mastering SQL for querying and managing databases. Learn ETL tools like SSIS, Informatica, or Talend to efficiently integrate and transform data. Additionally, understanding Data Modeling and concepts like Star Schema will enhance your ability to optimize data structures. Stay up-to-date with cloud data solutions like AWS Redshift or Azure Synapse to manage scalability. Lastly, data visualization skills using Power BI or Tableau are critical for delivering insights. Keep honing these skills, and you'll thrive in this evolving field. #DataWarehousing #ETL #SQL #DataModeling #CloudData #AzureSynapse #AWSRedshift #PowerBI #CareerInTech #DataEngineering

What helped me a lot while reading books or watching tutorials was to write the same code a few minutes and a day later. As I increased the complexity and implemented my own ideas, I learned some of the most important basics, such as the order of execution: FROM, WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY, and LIMIT.

Once you've started using a tool, I recommend thinking about the next two transformation steps while checking and confirming the correctness of the previous steps. Keep an eye on the number of rows and the sums of your measures to get a feel for the data and the flow. What fascinates me is the magic you can do with data transformations.

Master data modeling. Start by learning how to design and structure databases efficiently to support the storage, retrieval, and analysis of large datasets. Familiarize yourself with key concepts like star schema, snowflake schema, and dimensional modeling. Practice using tools such as SQL, ER diagrams, and data modeling software. Understanding how to model data effectively ensures that your data warehouse is scalable, organized, and optimized for performance, giving you a strong foundation for success in the field.

Data modeling is another essential skill, as it defines how data is structured and stored in the warehouse. Learn about conceptual, logical, and physical data models, with a focus on dimensional modeling techniques like star and snowflake schemas. Proper data modeling ensures optimized query performance and accurate reporting. In a data science project, you could create a star schema to store and query sales data for efficient analysis, supporting faster retrieval of insights related to product demand or customer purchasing behavior.

Start with one tool and learn it's core concept. Basically all viz tools work the same, there are differences, but once you're familiar with the core concepts, you can learn to master them all. Looking back on my journey, I would now start earlier to interact with the community and share my learnings, gain from others, this is a booster for viz developers.

Different types of cloud deployment models which help deliver cloud computing services : Public cloud : Microsoft Azure Private cloud: Amazon Web Services Hybrid cloud: IBM Research shows that nearly 90% companies are regarded as multicloud Three main cloud service models: IaaS: Infrastructure as a Service PaaS: Platform as a Service SaaS: Software as a Service FaaS: Function as a Service,is a relatively new cloud service model. This is Serverless computing Cloud computing technology: Offers significant savings Provides deployment tools Advanced data analytics AI/ML capabilities Enhances team collaboration on projects Cloud data warehouses offer robust security features, ability to encrypt data at rest and in transit

Proficiency in scripting languages like Python, SQL, or Shell scripting is crucial for automating data extraction, transformation, and loading (ETL) processes. Scripting enables you to manipulate large datasets efficiently, optimize workflows, and troubleshoot issues quickly. Practice writing scripts to handle routine tasks such as data cleaning, integration, and backups. By honing these skills, you’ll be able to streamline data management processes and become a more effective and adaptable data warehousing professional.