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You’re trying to learn about data warehousing. What are the most important things you need to know?

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Data warehousing is a process of collecting, storing, and analyzing data from various sources to support business intelligence, reporting, and decision making. It can help you gain insights, improve performance, and optimize strategies. But how do you get started with data warehousing? What are the key concepts and skills you need to know? Here are some of the most important things you need to know.

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Divya Srivastava

Top Voice Data Warehousing | SnowPro Certified| Data Engineer | Datawarehousing | ETL/ ELT | Ab Initio | Linux

1 Data Warehouse Architecture

A data warehouse is not a single database, but rather a complex system of components that work together to provide data access and analysis. These components include data sources, such as operational databases, web logs, and CRM systems; an ETL process for extracting, transforming, and loading the data; the central repository of integrated and historical data organized into subject areas or dimensions; data marts that focus on specific business domains or user groups; and BI tools that allow users to query, visualize, and explore the data. All of these components are essential for a successful data warehouse system.

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Divya Srivastava

Top Voice Data Warehousing | SnowPro Certified| Data Engineer | Datawarehousing | ETL/ ELT | Ab Initio | Linux
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Datawarehousing is not to be considered as a single entity. We need to consider various components when delving into Data Warehousing: 1. ETL Tools 2. DWH concepts 3. BI / Visualization tools 4. Data Modelling 5. Data Architecture 6. Cloud Platforms like AWS, Azure, GCP 7. Data Warehouse maintenance

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Sushil Dhadse

Data Engineer at Sonra | Snowflake SnowPro Core Certified | ETL | Azure | Snowflake | Prefect Orchestration
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To understand data warehousing effectively, grasp its fundamental concepts, such as ETL and ELT techniques. Essential skills include SQL, data modeling, and familiarity with ETL tools. Proficiency in at least one cloud platform is increasingly valuable due to integration with cloud technologies. However, practical experience through hands-on projects is crucial for reinforcing learning and gaining real-world insights. Remember, continuous learning and adapting to new technologies are essential for proficiency in data warehousing. So, prioritize understanding core concepts, gaining hands-on experience, and staying updated on industry trends to excel in the field of data warehousing.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Identify and integrate data from various sources, including operational databases, external systems, web logs, spreadsheets, and other structured and unstructured data repositories. Establishing a clear understanding of the source systems is crucial for effective data integration.

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Sashanth Embakula

Microsoft Certified Azure Data Engineer | AWS Certified Solutions Architect | Data Engineer | Database Engineer | Data Analyst | ML Engineer | ETL Developer | Snowflake | PySpark | AWS | Azure | Airflow | Python | SQL
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A data warehouse architecture includes multiple components like data sources, ETL processes, the central data repository, data marts, and BI tools. This architecture is designed to facilitate the extraction of data from various sources, its transformation and loading into a centralized repository, and ultimately, its analysis and visualization for business insights.

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2 Data Warehouse Design

Designing a data warehouse is a complex and iterative process that requires careful planning and analysis. To ensure success, you must consider the business requirements of stakeholders and users, as well as the data sources and quality they need. Additionally, you must choose an appropriate data model, such as star schema, snowflake schema, or data vault, and define the dimensions, facts, attributes, and relationships of the data. Furthermore, it is essential to ensure that the data in the warehouse is accurate, complete, consistent, and reliable by implementing data quality checks and validations. Finally, you must optimize the performance of the data warehouse and BI tools with techniques such as indexing, partitioning, aggregating, caching, etc., as well as monitor and tune performance regularly based on usage patterns and feedback.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Perform data profiling and exploration on source data to gain insights into data quality, patterns, and anomalies. Identify potential challenges early in the design process and plan for data cleansing and transformation accordingly.

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Sashanth Embakula

Microsoft Certified Azure Data Engineer | AWS Certified Solutions Architect | Data Engineer | Database Engineer | Data Analyst | ML Engineer | ETL Developer | Snowflake | PySpark | AWS | Azure | Airflow | Python | SQL
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The design phase is crucial and involves understanding business requirements, choosing a data model (star schema, snowflake schema, data vault), and defining data elements. The design should ensure data accuracy, completeness, and reliability through quality checks and validations, and optimize performance through indexing, partitioning, and other techniques.

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3 Data Warehouse Development

Developing a data warehouse requires implementing the design using the appropriate technologies and tools. This involves having skills in ETL development, such as using ETL tools or programming languages to extract, transform, and load data from sources to the data warehouse. It also requires skills in data warehouse development, such as database management systems or platforms for creating and managing the data warehouse and data marts. Lastly, you need BI development skills, such as using BI tools or frameworks to create and deploy BI applications that provide data analysis and reporting capabilities to the users. These skills include handling data formats, types, transformations, quality, loading, queries, visualization, exploration, presentation, delivery, security, backup, recovery, etc.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Implement robust transformation logic during the ETL process to ensure data quality, consistency, and compatibility with the data warehouse schema. Handle data cleansing, enrichment, and validation based on predefined business rules.

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4 Data Warehouse Testing

Testing a data warehouse is an essential step to guarantee that the data warehouse fulfills the business requirements and produces the expected results. You should conduct unit testing, which involves verifying the correctness and output of individual components or modules, such as ETL workflows, data warehouse tables, data marts, and BI reports. Additionally, integration testing is necessary to check the interactions and dependencies between components of the data warehouse, like the flow of data from sources to the data warehouse, the consistency between the data warehouse and data marts, and accuracy of BI reports. Lastly, system testing is needed to assess if the entire system meets functional, non-functional, and user acceptance criteria.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Perform unit testing on data warehouse tables to validate their structure, integrity, and consistency. Ensure that tables accurately reflect the transformed and integrated data and that primary and foreign key relationships are maintained.

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5 Data Warehouse Maintenance

Maintaining a data warehouse is an ongoing process that requires updating, enhancing, and improving the data warehouse in order to keep it relevant, reliable, and responsive. This involves scheduling and executing ETL workflows regularly to load new or changed data from the sources. Additionally, you must add, modify, or remove data elements, structures, or models based on changing business requirements or data availability. Furthermore, you need to monitor and analyze the data quality, performance, and usability of the data warehouse to identify and resolve any issues. Finally, any changes, enhancements, or innovations that can improve the data warehouse should be implemented.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You’re trying to learn about data warehousing. What are the most important things you need to know?

1

2

3

4

5

6

1 Data Warehouse Architecture

2 Data Warehouse Design

3 Data Warehouse Development

4 Data Warehouse Testing

5 Data Warehouse Maintenance

6 Here’s what else to consider

Data Warehousing

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You’re trying to learn about data warehousing. What are the most important things you need to know?

1

2

3

4

5

6

1 Data Warehouse Architecture

2 Data Warehouse Design

3 Data Warehouse Development

4 Data Warehouse Testing

5 Data Warehouse Maintenance

6 Here’s what else to consider

Data Warehousing

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