Using data and analytics to create actionable insights at scale


Datacast: Data & Analytics at Scale

You write a query and get data back. In between, magic happens.



In this episode, we talk with <a href="https://www.linkedin.com/in/ron-luebke-b9078a177/">Ron Luebke</a> about the “query optimizer”—a critical component of that magic.



We talk about query optimizers in general, what makes Teradata’s query optimizer a bit more magic than the rest, and how that magic gets extended beyond the relational database to other parts of a complete analytics ecosystem.

What is a Query Optimizer?

In this episode, <a href="https://www.linkedin.com/in/carrie-ballinger-79851050/">Carrie Ballinger</a> introduces us to a critical requirement of managing data and analytics at scale—workload management.



<ul><li>What is &#8220;workload management&#8221;?</li><li>Why and when workload management is necessary</li><li>The most important thing workload management enables</li><li>How is workload management managed?</li><li>Different types of workload management</li><li>How workload management improves overall throughput</li><li>Interesting workload management stories</li></ul>

Workload Management: A Critical Aspect of Analytics at Scale

&#8220;There are many types of data that have many different types of relationships, and you need to manage that data based on value.&#8221; — Rob Armstrong, Teradata



This week, Rob Armstrong shares all you need to know about data management by value—in just two shapes.







<h3 class="wp-block-heading">Links</h3>



<ul><li><a href="https://www.teradata.com/Blogs/The-Circle-and-Square,-all-you-need-to-know-about">The Circle and Square, All You Need to Know About Data and Analytics</a> [blog post]</li></ul>

Circle & Square

Our guest this week is Tim Miller, from Teradata&#8217;s Technology & Innovation Office.&nbsp;He was a&nbsp;principal&nbsp;in the development of the first commercial in-database data mining system, which makes him a perfect choice to&nbsp;provide an <g class="gr_ gr_994 gr-alert gr_gramm gr_inline_cards gr_run_anim Grammar only-ins replaceWithoutSep" id="994" data-gr-id="994">overview</g>&nbsp;of the data science process.



New to data science? There may be occasional bouts of jargon you don&#8217;t understand. If so, don&#8217;t worry! Tim (naturally) uses terminology from the data science domain, but we always bring it back to plain-English explanations.



<ul><li>What is data science?</li><li>CRISP-DM:&nbsp;<g class="gr_ gr_6 gr-alert gr_gramm gr_inline_cards gr_run_anim Grammar only-ins doubleReplace replaceWithoutSep" id="6" data-gr-id="6">Cross-industry</g> standard process for data mining [<a href="https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining">Wikipedia</a>]</li><li>Fundamental: What is the business problem we&#8217;re trying to address?</li><li>Data science is a team sport</li><li>Supervised and unsupervised learning</li><li>How do you decide which techniques and algorithms to use?</li><li>What is analytics ops?</li><li>Happiness is &#8220;in-platform analytics&#8221;</li></ul>

De-Mystifying Data Science

“There are things in every single company that should be done in the cloud. On the flip side of the coin, not everything belongs in the cloud.” — Marc Clark



Not sure what data and analytics scenarios make the most sense to do in the cloud? Maybe more importantly, are you sure which don’t?



Marc Clark, Teradata&#8217;s&nbsp;Director Global Sales Enablement and Strategy, shares some clarity on the cloud.&nbsp;What are the right use cases for cloud analytics? What are the wrong ones? What’s the best way for a company to dip their toes into cloudy waters?

The Cloud Isn’t Magic (But It’s Kinda Magic)

In this episode, Rob Armstrong talks about the evolution and future of analytics.

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