1. Welcome to 2019 SQL Saturday in Los Angeles (#891)
Let’s Get Started!
Welcome to 2019 SQL Saturday in Los Angeles (#891)

2. Background Mark Mucchetti
Career spanning IT, SQL Server DBA, release/ops engineer, developer, software architect in finance, healthcare, e-commerce
Currently managing resources in Amazon Web Services and Google Cloud Platform in a primarily serverless stack (Node.js / C#)
Passion projects include home automation, retrocomputing, cloud tooling, and Raspberry Pi
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3. Please Support Our Sponsors
SQL Saturday in Los Angeles is made possible with the generous support of these sponsors.
You can support them by opting-in and visiting them in the sponsor area.
#SQLSatLA

4. Orange County User Group Orange County Power BI User Group
Regional Local User Groups
LA Data Platform
(a.k.a. SQL Malibu)
3rd Wednesday of each month
ladataplatform.pass.org - Join us remotely!
L.A. User Group
3rd Thursday of each odd month
sqlla.pass.org
Los Angeles - Korean
Every Other Tuesday
sqlangeles.pass.org
San Diego User Group
1st & 3rd Thursday of each month
meetup.com/sdsqlug
meetup.com/sdsqlbig
Orange County User Group
2rd Thursday of each month
bigpass.pass.org
Orange County Power BI User Group
meetup.com/OC-Power-BI-User-Group/
Sacramento User Group
1st Wednesday of each month
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5. Regional 2019 SQL Saturday events
SQL Saturday in Sacramento
When: August 17, 2019
Where: Patrick Hays Learning Center
#SQLSatSac #SQLSat885
SQL Saturday in San Diego
When: TBD
Where: San Diego
#SQLSatSD
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6. Google Cloud Platform: Introduction
This session will be a very broad survey of technologies offered by Google Cloud Platform
Far too much ground to cover in a single session
Hopefully will spark some interest in related areas
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7. Basics Started in 2008 with App Engine
Built using early containerization techniques modeled after internal Google services
Later led to Kubernetes (and the GCP specific implementation, Google Kubernetes Engine)
Now a group of services as robust as AWS and Azure, each could cover several sessions on its own (and does, at Google Next)
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8. Products Compute Data Analytics Databases Networking Security Storage
Compute
Data Analytics
Databases
Networking
Security
Storage
ML, IoT, Multi-Cloud, Migration, 100+ listed on the products page
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9. Let’s Cover a Few Areas
Compute
Data
AI / Machine Learning
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10. Compute
App Engine
Cloud Run
Compute Engine
Cloud Functions
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11. Data
BigQuery
Cloud Pub/Sub
Cloud SQL
Cloud Firestore
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12. AI/Machine Learning Dialogflow Cloud AutoML Cloud AutoML Tables
Vision AI
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13. G Suite
G Suite isn’t part of Google Cloud Platform, but it is part of Google Cloud
We’re going to use Hangouts during this exercise because it’s convenient
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14. What Exercise? This is Bort Bort is our tech department chatbot
He answers simple questions about our data and engineering
He’s got a little bit of attitude
And he happens to be fully serverless on GCP
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15. It usually goes something like this…
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16. Major differences between our demo and a production system
No security
You would want to secure access to any sensitive information appropriately with asymmetric encryption, SSL, etc.
All in the browser!
I’m not going to be dropping to VSCode today. All samples are inline to the browser, which is kind of amazing in itself. In reality you’d want a full IDE.
No complicated natural language intents
We’re not going to be building a very robust conversational interface. Relatively easy to do but takes a lot of time.
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17. Bort’s GCP Landscape Many interacting services
Cloud Storage – holds our assets
BigQuery – holds our database for analytics
Cloud Functions – holds our code for data access
Dialogflow – handles the natural language processing for the chatbot
Hangouts – hosts the chatbot (again because it’s easier than configuring Slack or something else)
StackDriver – helps us monitor our functions to make sure everything is working properly
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18. DEMO
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19. We could add a lot more services!
Pub/Sub
Queuing system to accept events
Works with Cloud Functions and BigQuery and can pipe data in from outside sources
Can be used to create messaging pipelines among the other services
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20. We could add a lot more services!
Cloud Vision
Bort could process uploaded images and annotate them
Could automatically read text out of images and use it as the basis of analytics searches
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21. We could add a lot more services!
BigQuery ML
You can run machine learning models directly against the BigQuery datasets
(ML.PREDICT against direct MODELs in your system)
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22. We could add a lot more services!
Cloud Firestore
My department uses this extensively as a NoSQL operational store
It also can use PubSub to BigQuery, be called by Cloud Functions, etc.
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23. Questions?
Please leave a session evaluation or do it online later!
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