Data.SQL.Saturday.LA is a free training event for Microsoft Data Platform professionals and those wanting to learn about Public/Private Clouds, Data Platforms, SQL and No-SQL Databases, Business Intelligence, and Analytics.
On the event date, if you were sick or have had a fever in the last 72 hours please stay home!
-> This year's event is intended to be onsite in Los Angeles, California.
Virtual 2021 Data.SQL.Saturday.LA
Virtual 2020 SQL Saturday in Los Angeles
2019 SQL Saturday in Los Angeles
2018 SQL Saturday in Los Angeles
2017 SQL Saturday in Los Angeles
This event is organized by a Data Driven Technologies, Inc 501.c.3 non-profit.
Data SQL Saturday LA has been delivering on prem and virtual events since 2017. This year's event is intended to be onsite in Los Angeles, California on August 13, 2022. If you will be in the area at that time, we welcome your submission.
We try and provide our attendees with 4 to 5 different tracks over 6 blocks (24-30 sessions per annual event). This provides valuable content for a wide variety of attendees whether you're a DBA, Dev Op, Developer, Reporting enthusiast or Cloudy we should have something good for you to attend.
Length: 60 minutes
Topics: Any data-related or non-technical topic
Session Info: Write a full description of what this session will be, what the audience will learn, and what pre-requisites are expected
Max No. of Sessions per Speaker: 5 submissions max
Difficulty level: Not everyone is an IT god, not everyone is a novice, we are looking for a wide range to appeal to all of our attendees. If you are submitting more than 1 session, please provide sessions at different levels.
Examples for data topics:
RDBMS: SQL Server/PostgreSQL/MySQL/Oracle/MariaDB
NoSQL: MongoDB, Cassandra, Elasticsearch, Couchbase
Data Visualization: Power BI / Tableau / Qlik / SSRS
Infrastructure: DBA/DevOps on Private, Hybrid, Public Clouds: Microsoft Azure / Amazon AWS / Google GCP / IBM SoftLayer
Development: Business Intelligence, Data Warehouse, Database Development, Data Engineering, Integration
Data Science: Python/R, Machine Learning, Artificial Intelligence, Deep Learning
Examples of Non-technical: Women in Tech, Personal Branding, Job Hunting, Building your Own Tech Company, Mental Health, Careers in Tech, Project Management
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