In this session, we'll unveil the potential of Data Vault 2.0, an often overlooked but powerful data warehousing method. Discover how it offers scalability, agility, and flexibility to your data solutions.

Key Highlights:
- Explore the origins and essence of Data Vault 2.0
- Learn how Data Vault 2.0 streamlines big data solutions for scalability.
- See how it integrates with dbt via AutomateDV for faster time to value.
- Understand how AutomateDV simplifies Data Vault 2.0 data warehouses, freeing data teams from intricate SQL. 

This session will not be live streamed. It will be available on-demand on Tuesday, October 17, 6:30 PM BST.

<span data-sheets-userformat='{"2":15235,"3":{"1":0},"4":{"1":2,"2":16776960},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":12}' data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;In this session, we'll unveil the potential of Data Vault 2.0, an often overlooked but powerful data warehousing method. Discover how it offers scalability, agility, and flexibility to your data solutions.\n\nKey Highlights:\n- Explore the origins and essence of Data Vault 2.0\n- Learn how Data Vault 2.0 streamlines big data solutions for scalability.\n- See how it integrates with dbt via AutomateDV for faster time to value.\n- Understand how AutomateDV simplifies Data Vault 2.0 data warehouses, freeing data teams from intricate SQL.&quot;}">In this session, we'll unveil the potential of Data Vault 2.0, an often overlooked but powerful data warehousing method. Discover how it offers scalability, agility, and flexibility to your data solutions. Key Highlights: - Explore the origins and essence of Data Vault 2.0 - Learn how Data Vault 2.0 streamlines big data solutions for scalability. - See how it integrates with dbt via AutomateDV for faster time to value. - Understand how AutomateDV simplifies Data Vault 2.0 data warehouses, freeing data teams from intricate SQL.<span data-sheets-formula="=Sessions!R[-53]C[5]" data-sheets-userformat='{"2":256,"11":4}' data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;The Data Vault 2.0 Methodology and Architecture for Data Warehousing is not new; however, it is uncommon, and often unheard of. I would like to introduce the audience to the method with a story and take them on a journey to learn how and why this approach could help them deliver business value, faster. \r\n\r\nAs organizations grow and mature, they accumulate more and more data, making it increasingly important to have a centralized and scalable data platform that can efficiently manage and process this information. Point-to-point data pipelines, which are often used in the early stages of an organization's data journey, can become inefficient, inflexible, and difficult to maintain as the organization grows. \r\n\r\nMoving to a well-architected and robust Data Warehouse on a suitable platform provides several benefits, including improved data quality, greater scalability and flexibility, better data governance, and increased efficiency. The modern Data Warehouse requires a few key features, outlined below. \r\n\r\nScalability: Data Warehouses become increasingly complex and difficult to manage as the organization grows and more data sources are added. A Data Vault 2.0 approach provides a scalable architecture that can handle large volumes of data from multiple sources. \r\n\r\nIntegration: Point-to-point data pipelines often require custom integrations for each data source, which can be time-consuming and error-prone. A Data Vault 2.0 approach can provide a standardized method of integrating data from multiple sources, reducing the risk of errors, improving efficiency, and ultimately providing a single source of truth for your data. \r\n\r\nGovernance: Legacy solutions can result in data silos and a lack of visibility into data lineage, making it difficult to ensure compliance with regulations such as GDPR and CCPA. A Data Warehouse following the Data Vault 2.0 approach can provide a centralized and fully audited repository of data, enabling the business to implement better governance and control over data access in less time and following industry standards. \r\n\r\nData Vault 2.0 is an agile data warehousing methodology, that can help organizations achieve these benefits by providing a highly scalable and adaptable data model that can accommodate changing business requirements and evolving data sources. \r\n\r\nTo be truly agile, organizations need a data platform that can support rapid development and iteration of data-driven applications and analytics. \r\n\r\nAutomateDV is a dbt package that provides a set of macros for implementing a Data Vault 2.0 compliant data warehouse. By automating Data Vault using dbt and AutomateDV (formerly known as dbtvault), data teams can take advantage of the scalability, flexibility, and agility that Data Vault offers while also ensuring that the implementation is maintainable and consistent.  \r\n\r\n \r\nBenefits include: \r\n\r\nModular and maintainable: dbt provides a modular approach to building data pipelines, allowing data teams to break down complex data transformations into smaller, more manageable components. With AutomateDV, teams can implement Data Vault using provided macros, making it easier to maintain and update the implementation over time, allowing engineers to focus on delivering business value. \r\n\r\nConsistency and scalability: With dbt and AutomateDV, data teams can ensure that the Data Vault implementation is consistent across different environments, such as development, staging, and production. This consistency helps to reduce errors and ensures that the implementation can scale as the data volume and complexity grows. \r\n\r\nBetter collaboration: By using dbt and AutomateDV, data teams can collaborate more effectively, as the implementation is defined in code and version controlled. This approach allows different team members to work on different parts of the implementation simultaneously, while also providing a clear audit trail of changes. \r\n\r\n \r\nOverall, automating Data Vault using dbt and AutomateDV can help data teams to build a scalable, flexible, and maintainable data platform that can meet the needs of a growing organization. &quot;}">&nbsp; <span data-sheets-formula="=Sessions!R[-53]C[5]" data-sheets-userformat='{"2":256,"11":4}' data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;The Data Vault 2.0 Methodology and Architecture for Data Warehousing is not new; however, it is uncommon, and often unheard of. I would like to introduce the audience to the method with a story and take them on a journey to learn how and why this approach could help them deliver business value, faster. \r\n\r\nAs organizations grow and mature, they accumulate more and more data, making it increasingly important to have a centralized and scalable data platform that can efficiently manage and process this information. Point-to-point data pipelines, which are often used in the early stages of an organization's data journey, can become inefficient, inflexible, and difficult to maintain as the organization grows. \r\n\r\nMoving to a well-architected and robust Data Warehouse on a suitable platform provides several benefits, including improved data quality, greater scalability and flexibility, better data governance, and increased efficiency. The modern Data Warehouse requires a few key features, outlined below. \r\n\r\nScalability: Data Warehouses become increasingly complex and difficult to manage as the organization grows and more data sources are added. A Data Vault 2.0 approach provides a scalable architecture that can handle large volumes of data from multiple sources. \r\n\r\nIntegration: Point-to-point data pipelines often require custom integrations for each data source, which can be time-consuming and error-prone. A Data Vault 2.0 approach can provide a standardized method of integrating data from multiple sources, reducing the risk of errors, improving efficiency, and ultimately providing a single source of truth for your data. \r\n\r\nGovernance: Legacy solutions can result in data silos and a lack of visibility into data lineage, making it difficult to ensure compliance with regulations such as GDPR and CCPA. A Data Warehouse following the Data Vault 2.0 approach can provide a centralized and fully audited repository of data, enabling the business to implement better governance and control over data access in less time and following industry standards. \r\n\r\nData Vault 2.0 is an agile data warehousing methodology, that can help organizations achieve these benefits by providing a highly scalable and adaptable data model that can accommodate changing business requirements and evolving data sources. \r\n\r\nTo be truly agile, organizations need a data platform that can support rapid development and iteration of data-driven applications and analytics. \r\n\r\nAutomateDV is a dbt package that provides a set of macros for implementing a Data Vault 2.0 compliant data warehouse. By automating Data Vault using dbt and AutomateDV (formerly known as dbtvault), data teams can take advantage of the scalability, flexibility, and agility that Data Vault offers while also ensuring that the implementation is maintainable and consistent.  \r\n\r\n \r\nBenefits include: \r\n\r\nModular and maintainable: dbt provides a modular approach to building data pipelines, allowing data teams to break down complex data transformations into smaller, more manageable components. With AutomateDV, teams can implement Data Vault using provided macros, making it easier to maintain and update the implementation over time, allowing engineers to focus on delivering business value. \r\n\r\nConsistency and scalability: With dbt and AutomateDV, data teams can ensure that the Data Vault implementation is consistent across different environments, such as development, staging, and production. This consistency helps to reduce errors and ensures that the implementation can scale as the data volume and complexity grows. \r\n\r\nBetter collaboration: By using dbt and AutomateDV, data teams can collaborate more effectively, as the implementation is defined in code and version controlled. This approach allows different team members to work on different parts of the implementation simultaneously, while also providing a clear audit trail of changes. \r\n\r\n \r\nOverall, automating Data Vault using dbt and AutomateDV can help data teams to build a scalable, flexible, and maintainable data platform that can meet the needs of a growing organization. &quot;}"><span data-sheets-formula="=Sessions!R[-43]C[5]" data-sheets-userformat='{"2":256,"11":4}' data-sheets-value='{"1":2,"2":"Over the past four years, the data team at EQT has leveraged dbt and Snowflake to create a myriad of data products across the company. With a rapidly growing organization and increased demands for timely and accurate data, our immense monolithic dbt repository has become challenging to maintain. Learn about the best practices we are adopting to keep the platform in shape and scale with the business."}'>This session will not be live streamed. It will be available on-demand on Tuesday, October 17, 6:30 PM BST.

London

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Senior Consultant Data Engineer

Demystifying Data Vault with dbt

On-demand

In this session, we'll unveil the potential of Data Vault 2.0, an often overlooked but powerful data warehousing method. Discover how it offers scalability, agility, and flexibility to your data...

Alex Higgs

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