Big Challenges in Data Modeling: Data Modeling at High Speed

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It's May, which brings this former Hoosier thinking of racetracks and Indy cars. I'm also a runner and that means I'm always thinking about pace and timings…and feeling guilty about not training hard enough. This got me musing about how data modelers can speed up the data modeling process -- not just during a development projects, but at all points in our work day. So let's have a discussion about. In this month's webinar, we'll talk about: The Need for Speed Sprints, marathons and training Race cars, horses, carts, and feet Qualifiers and Races Pace cars Backseat drivers Rules, tickets and enforcement Fads, gadgets and automation Red, yellow, green and checkered flags How do you know when to stop racing?

Tecnologia Diversão e humor

YourModerator
Karen Lopez
Sr. Project Manager / Architect
Infoadvisors
@datachick
#BCDModeling
YourPanelist
Donna Burbank
VP Information Management
Services, Enterprise Architects
@donnaburbank
Carol Lehn
Database Designer,
PepsiCo
@lehnca

The Need for Speed
Faster Data Models…and Data Modelers
Karen Lopez

Welcome!
#BCDModeling
Chat
Q&A
Slides
Aftershow
2

Need for Speed
Sprints, marathons and training
Race cars, horses, carts, and feet
Qualifiers and Races
Rules, tickets and enforcement
Fads, gadgets and automation
Red, yellow, green and checkered
flags
How do you know when to stop
racing?
3

Sprints, Marathons & Training
Agile & SCRUM
Training versus racing
Sprints
LSDs
Walkbreaks
Hurdles
Rest
4

Make it faster…
1. Know the process/methodology
2. Refuse to sprint a marathon
3. Insist on walk breaks
4. Insist on resources for
maintenance
5. Put hurdles in writing, then
report on their status
5

Race cars, Horses & Feet
Maintaining cars, horses and feet
Pit crew
Training the crew
Spare parts & shoes
Upgrades
6

Make it faster…
1. Maintain stuff
2. Train your team mates
3. Have spares
4. Don’t use old tools
5. Use professional tools
7

“
”
Trained team members put up
fewer obstacles and get their
own jobs done faster.
- Karen Lopez
Quote me… 8

Qualifiers & Races
Resumes versus Skills
Tests versus portfolios
Size matters
Collaboration matters more
9

“
”
In my observations, the project
gains from having an experienced
data modeler: 2-3x faster to
deliver usable models.
- Karen Lopez
Quote me… 10

Make it faster…
1. Hire good people with real
world experience
2. Train inexperienced people
3. Mentor inexperienced people
4. Rate fit high
11

Rules, Tickets and Enforcement
Our Rules
Your Rules
Inspection
Automation
12

Make it faster…
1. Automate everything you
can, and more
2. Inspect and report, don’t
tattletale
3. Log it in issue management
system
13

Fads, Gadgets and Automation
Methodologies
Macros & Scripts
Lazy Data Modeler is Best
Gadgets, Tablets, Laptops
14

Make it faster…
1. Automate everything you
can, and more
2. Be lazy…automate more…
3. Make use of modern
technology
15

“
”
The best data modeler is
a lazy data modeler
- Karen Lopez
Quote me…. 16

Red, Yellow, Green and Checkered flags
Priorities
Perfection versus success
Deliver!
17

Make it faster…
1. Automate everything you can,
and more
2. Understand deliverables…yours
versus theirs
3. Do maintenance during down
times
4. Have a pit crew
18

How do you know when to stop racing and go back
to training? 19

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Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

apidays

Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Zilliz

presentation ICT roal in 21st century education

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Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Six Myths about Ontologies: The Basics of Formal Ontology

johnbeverley2021

Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

apidays

Exploring Multimodal Embeddings with Milvus

Zilliz

Big Challenges in Data Modeling: Data Modeling at High Speed

1. YourModerator Karen Lopez Sr. Project Manager / Architect Infoadvisors @datachick #BCDModeling YourPanelist Donna Burbank VP Information Management Services, Enterprise Architects @donnaburbank Carol Lehn Database Designer, PepsiCo @lehnca

2. The Need for Speed Faster Data Models…and Data Modelers Karen Lopez

3. Welcome! #BCDModeling Chat Q&A Slides Aftershow 2

4. Need for Speed Sprints, marathons and training Race cars, horses, carts, and feet Qualifiers and Races Rules, tickets and enforcement Fads, gadgets and automation Red, yellow, green and checkered flags How do you know when to stop racing? 3

5. Sprints, Marathons & Training Agile & SCRUM Training versus racing Sprints LSDs Walkbreaks Hurdles Rest 4

6. Make it faster… 1. Know the process/methodology 2. Refuse to sprint a marathon 3. Insist on walk breaks 4. Insist on resources for maintenance 5. Put hurdles in writing, then report on their status 5

7. Race cars, Horses & Feet Maintaining cars, horses and feet Pit crew Training the crew Spare parts & shoes Upgrades 6

8. Make it faster… 1. Maintain stuff 2. Train your team mates 3. Have spares 4. Don’t use old tools 5. Use professional tools 7

9. “ ” Trained team members put up fewer obstacles and get their own jobs done faster. - Karen Lopez Quote me… 8

10. Qualifiers & Races Resumes versus Skills Tests versus portfolios Size matters Collaboration matters more 9

11. “ ” In my observations, the project gains from having an experienced data modeler: 2-3x faster to deliver usable models. - Karen Lopez Quote me… 10

12. Make it faster… 1. Hire good people with real world experience 2. Train inexperienced people 3. Mentor inexperienced people 4. Rate fit high 11

13. Rules, Tickets and Enforcement Our Rules Your Rules Inspection Automation 12

14. Make it faster… 1. Automate everything you can, and more 2. Inspect and report, don’t tattletale 3. Log it in issue management system 13

15. Fads, Gadgets and Automation Methodologies Macros & Scripts Lazy Data Modeler is Best Gadgets, Tablets, Laptops 14

16. Make it faster… 1. Automate everything you can, and more 2. Be lazy…automate more… 3. Make use of modern technology 15

17. “ ” The best data modeler is a lazy data modeler - Karen Lopez Quote me…. 16

18. Red, Yellow, Green and Checkered flags Priorities Perfection versus success Deliver! 17

19. Make it faster… 1. Automate everything you can, and more 2. Understand deliverables…yours versus theirs 3. Do maintenance during down times 4. Have a pit crew 18

20. How do you know when to stop racing and go back to training? 19

Big Challenges in Data Modeling: Data Modeling at High Speed

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