Agile Data Warehousing in the Cloud .

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Agile Data Warehousing in the Cloud .

Agile Data Warehousing in the Cloud
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 6.5 Hours | 2.94 GB
Genre: eLearning | Language: English

This all day workshop will provide you the background and principles to use agile in your data warehouse and business intelligence projects. It will introduce you to a modern method for agile data modeling, Data Vault 2.0, and provide you with a detailed, real world case study. At the end we will talk about how the cloud has changed everything and how you can enable your agile data warehouse by using a modern data warehouse as a service (DWaaS) built in the cloud, for the cloud. We will cover these four topics:
Agile Methods and Data Warehousing: How to Deliver Faster. Most people will agree that data warehousing and business intelligence projects take too long to deliver tangible results. Often by the time a solution is in place, the business needs have changed. With all the talk about Agile development methods like SCRUM and Extreme Programming, the question arises as to how these approaches can be used to deliver data warehouse and business intelligence projects faster. This presentation will look at the 12 principles behind the Agile Manifesto and see how they might be applied in the context of a data warehouse project. The goal is to determine a method or methods to get a more rapid (2-4 weeks) delivery of portions of an enterprise data warehouse architecture. Real world examples with metrics will be discussed.
Agile Data Engineering: Introduction to Data Vault 2.0. As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them.
Case Study: Hybrid Data Vault - Data Warehousing in the Real World. At a former health-care client, we were not able to implement a pure Data Vault 2.0 architecture. Instead, we had to evolve a hybrid solution that uses Type 2 persistent stage tables that we tie together using standard Hub and Link structures along with a Key Map table. While we were able to utilize DV 2.0 concepts such as MD5 hash keys, in order to avoid joins in loading the stage tables we had to come up with a way to resolve the Business Keys further into the process. In addition, we were able to build a BI reporting layer using virtual dimensions that were hybrid type 1 and 2 combined. I will show you our solution with examples of real working code.
Agile Data Warehouse in the Cloud (DWaaS). We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in the cloud, in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that). But what is a DWaaS really? How is it different from traditional on-premises data warehousing? How can a cloud-native data warehouse help enable your agile data warehouse practice?

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