The Data Warrior

Changing the world, one data model at a time. How can I help you?

Better Data Modeling: Agile Data Engineering

You asked for it, you got it!

Ever since I wrote my Kindle book on Agile Data Engineering and Data Vault 2.0, many, many people have asked me to provide it in a hardcopy format. Well, I finally managed to find time to convert that ebook into a paperback book (I even corrected a few errors in the process).

If you forgot what the book was about, here is the description:

This book will give you a short introduction to Agile Data Engineering for Data Warehousing and Data Vault 2.0. I will explain why you should be trying to become Agile, some of the history and rationale for Data Vault 2.0, and then show you the basics for how to build a data warehouse model using the Data Vault 2.0 standards.In addition, I will cover some details about the Business Data Vault (what it is) and then how to build a virtual Information Mart off your Data Vault and Business Vault using the Data Vault 2.0 architecture.So if you want to start learning about Agile Data Engineering with Data Vault 2.0, this book is for you.

So here it is – Introduction to Agile Data Engineering – now available to purchase on Amazon.

Get your copy now. Next time you see me at an event, I will be happy to sign it for you. 🙂

Enjoy!

Kent

The Data Warrior

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Adapting Agile Principles for Data Warehousing

Despite the clear benefits of data warehousing programs, business units often question what they perceive to be long delivery timelines and a lack of data accessibility. Nearly half (48%) of the respondents in a TDWI survey reported that personnel in their organizations spend at least 61% of their time finding and preparing data. Only 28% said that their business users and analysts can access and analyze new data, including external data, without close IT support.

Check out how to solve these type of issues, and more, by applying the principles of Agile to data warehousing in my newest post and ebook:

Adapting the Agile Manifesto Principles to Data Warehousing

Cheers.

Kent, The Data Warrior &

Chief Technical Evangelist, Snowflake

5 Business Needs That Fuel Enterprise Data Warehouse Development

The global market for data warehousing is expected to grow to $34.7 billion by 2025, according to a recent report from Allied Market Research. That’s nearly double the $18.6 billion it was worth in 2017.

What fuels investment in enterprise data warehouse development? Cloud data warehouse technology has given rise to innovative systems and practices that increase efficiency and reduce costs across company functions. Today, departments like marketing, finance, and supply chain operations benefit from a modern data warehouse as much as the organization’s engineering and data science teams.

In this blog post, I list five business priorities that fuel increased investment in modern enterprise data warehouse development. See them here on the Snowflake blog site:

5 Business Needs That Fuel Enterprise Data Warehouse Development

And don’t forget to download my newest ebook (free) listed at the end of the post!

Kent

The Data Warrior

How to manage GDPR compliance with Snowflake’s Time Travel and Disaster Recovery 

One year after implementation, the European Union’s General Data Protection Regulation (GDPR) continues to be a hot regulatory topic. As organizations work to bring their data practices into compliance with the new law, one question comes up repeatedly: How does Snowflake, the data warehouse built for the cloud, enable my organization to be GDPR compliant?

Check out my latest post to see my answer:

How to manage GDPR compliance with Snowflake’s Time Travel and Disaster Recovery | Snowflake Blog

Cheers!

Kent

The Data Warrior

The Elephant in the Data Lake and Snowflake

So is Hadoop finally dead? For many use cases, I think it really is. The cloud and the continued evolution of technology has created newer, better ways of working with data at scale. Check out what Jeff has to say about it!

Jeffrey Jacobs, Consulting Data Architect, Snowflake SnowPro Core Certified

Let’s talk about the elephant in the data lake, Hadoop, and the constant evolution of technology.

Hadoop, (symbolized by an elephant), was created to handle massive amounts of raw data that were beyond the capabilities of existing database technologies. At its core, Hadoop is simply a distributed file system. There are no restrictions on the types of data files that can be stored, but the primary file contents are structured and semi-structured text. “Data lake” and Hadoop have been largely synonymous, but, as we’ll discuss, it’s time to break that connection with Snowflake’s cloud data warehouse technology.

Hadoop’s infrastructure requires a great deal of system administration, even in cloud managed systems.   Administration tasks include: replication, adding nodes, creating directories and partitions, performance, workload management, data (re-)distribution, etc.  Core security tools are minimal, often requiring add-ons. Disaster recovery is another major headache.  Although Hadoop is considered a “shared nothing” architecture, all…

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