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finished 1,688 days ago
DATA:Scotland 2019
event date
13 Sep 2019
location
Strathclyde University Innovation Centre, 99 George St, Glasgow G1 1RD Glasgow, United Kingdom
website
datascotland.org/
DATA:Scotland is an annual, free training conference, focused on the Microsoft Data Platform and related technologies. Founded in 2017, we have grown from strength to strength. We are organised BY community volunteers FOR the data community.
We return on Friday 13 September 2019!
Sign up to our mailing list to be the first to get updates and information!
finished 1,853 days ago
Call for Speakers
Call opens at 8:00 AM
15 Jan 2019
Call closes at 11:59 PM
31 Mar 2019
Call closes in GMT Daylight Time (UTC+01:00) timezone.
Closing time in your timezone () is .
After two fantastic years we are back in 2019 with more!
We are looking for speakers near and far to come to Scotland's ONLY free Data Platform community event. Sessions can be around any aspect of the Microsoft Data Platform. DBA, BI, Data Warehousing, Machine Learning, Data Science, Automation, DevOps, PowerShell and all the rest! We want to hear your ideas!
We want to hear from new speakers and veterans. Whatever level you are at, you will be given the best Scottish welcome! If you're a new speaker we can help. Choose the New Speaker session format and we'll be in touch!
We look forward to your submissions!
The DATA:Scotland Team
all submitted sessions
publicly listed on this page
132 submissions
Submitted sessions
Alex Whittles
- What's this Machine Learning all about?
- Using Source Control To Open Up A World Of Community and Collaboration
- Improving Your Presentation Skills - Practical Examples by Two Very Different Speakers
- When is data science the answer?
- Shape Maps in Power BI
- CosmosDB in an Hour
- PowerBI Model Designs & Techniques
- Monitoring Power BI
- Checking in with Power Apps, Flow and Power BI
- Enterprise Power BI – Development Lifecycle
- Who, What, When. Monitoring usage of your MS BI stack
- Containers vs Clones: Database provisioning made simple
- Simple database deployments with Azure DevOps Services and SSDT
- DevOps 101 for data folks
- DevOps, Dev Data & Data Breaches: 5 fixes for a GDPR dilemma
- How to screw up your ability to deliver database updates in 16 simple steps
- ABC's of the Power BI REST API
Gianluca Sartori
- Benchmarking in the cloud
- DAX Gotchas
- Take your first steps towards Power BI by using DAX
- Lift and Shift - Moving workloads to IAAS, lessons learned
- ETL with Azure Databricks, The Complete Introduction
- Creating an Enterprise Datalake / warehouse without an Enterprise budget
- SSIS script tasks and components for beginners
- My mother said I never should: Unpacking SQL code smells
- Design patterns for metadata-first ETL process control
- Design Thinking and Innovation Accounting in Power BI
- Power BI - A look into the future with mixed reality
- Microsoft Flow Automation using Power BI API
- The Power Bi interactive custom visuals to spice things up.
- Statistics in Microsoft SQL Server - theory and practice
- Database backups - A DBA's peaceful night's sleep
- Multi-server agent jobs administration with happy faces and pigeons
- 3 ways to bring Power BI under Source Control
- Supercharge your Reporting Services: An essential toolkit
- Advanced Linkedin for Employability
- Biml Tips and Tricks: Not just for SSIS packages!
- Uhms and Bunny Hands: Tips for Improving Your Presentation Skills
- Pipelines and Packages: Introduction to Azure Data Factory
- Stay in control, automatically. Learn how I managed 3000 SQL Servers.
- SQL Server performance baselining with SQLWATCH
- Keeping your Data Secure in SQL Server
- PowerShell Desired State Configuration (DSC)
- SQL 2017 New feature -Graph Databases
- SQL Server Management Studio Tips and Tricks
- Beware of the Dark Side - A Guided Tour of Oracle for the SQL DBA
- Taming the Beast – How a SQL DBA can keep Kerberos under control
- Shard Attack - Horizontal Scaling in Azure SQL Database
- Don't cross the streams! A closer look at Stream Analytics
- The Productive Data Professional
- Introduction to neural networks
- Azure Data Factory V2; The Data Flows
- Be certifiable - certify yourself!
- Azure Databricks - concepts, use-case and overview
- Big Data with Azure: where to begin? Concepts and best practices
- Azure Cosmos DB - apps, fasten your seatbelts
- Modernise your data migration with Azure Data Migration Service
- simplifying managed instance migrations using dbatools
- Simplifying High Availability with dbatools
- SQL Notebooks in Azure Data Studio for the DBA
- Guillotines, Sat-Nav and the Query Optimiser
- SQL Server 2019 with a touch of Linux
- Maintaining your larger SQL Server databases better
- Database Adventure
- Azure Update Management
- Use Azure Tooling to be successful
- Striking the right balance while staying relevant
- Start your datacentre transformation journey with Azure Migrate
- Feel Validated with dbachecks
- Data Engineering at Scale: An Azure SQL Data Warehouse Performance Guide
- Advanced Data Factory: Let the Data Flow!
- Advancing Databricks - Taking your ETL to the Next Level
- Databricks for the Uninitiated
- SQL Server Encryption for the Layman
- An Introduction to Azure for Data Professionals
- NOLOCK - The Good, the Bad & the Ugly
- How to disrupt the AI Disruption with an AI Strategy
- Delivering your first presentation: Tips and hints
- Azure SQL Data Warehouse Gen2 - Zero to Hero
- Exploring the SQL Tiger Team Toolbox
- How to create Custom Visuals for Power BI
- Maps in Power BI
- Cloud Scale Straming Analytics with Databricks
- Deep Dive with Azure SQL Datawarehouse
- Use parameters to add extra flexibility to your Power BI content
- AI Capabilities for Power BI to make AI Accessible for Everyone
- Storytelling & interactive reports with Power BI Bookmarks & Drillthrough
- End-to-end monitoring for a successful Power BI implementation
- Enterprise and multi-tier Power BI deployments with Azure DevOps.
- Perfectionism, Impostor Syndrome, Anxiety and Learning to be Kind to Yourself
- You can run SQL Server on AWS?
- Migrating SQL to Azure
- SQL Server and Kubernetes
- AWS or Azure – Which is right for you?
- "Kubernetify" your Containers
- Introduction to Containers
- Database CI/CD with Containers and Azure DevOps
- An Introduction to SQL Server 2019 Big Data Clusters
- Beyond IoT Real-time Data Ingestion with Azure Stream Analytics
- Complex Azure Orchestration with Dynamic Data Factory Pipelines
- ETL in Azure made easy with Data Factory Data Flow
- It's broken, now what?! (Practical problem solving)
- Fort Knox for your data - securing Power BI using Microsoft 365
- Crazy Data Science
- Moving advanced analytics to your SQL Server databases
- Advanced analytics in the cloud: AzureML
- Azure SQL Database - Lessons learned from the trenches
- Azure SQL Managed Instance - Experiencies from the Field
- Power BI Cheat Sheet explained!
- Fun with Legal Information in SQL Server: Correlating Offers and Counter-offers
- Fun with Legal Information in SQL Server: Determining the Law to Be Used
- Implementing Supertypes and Subtypes in SQL Server
- FILETABLEs: What They Are and How to Use Them
- Learn how to build a real-time analytics solution
- Talk tech to me - improving your technical presentation skills
- SQL Server hates you(?) - what the DBAs never told the developers
- The Force Awakens - Azure SQL Server for the on-prem DBA
- Learning to swim - an introduction to Azure data lake
- Azure machine learning for the absolute beginner
- Arguing with myself - self-service BI from an infrastructure perspective
- SQL Dashboard v1 - A Python/Django Tool for implementing Database as Service.
- Working smarter, not harder with Azure SQL Database
- 10 Alerts that you should have it running in your SQL Server
- Stop everything! T-SQL tips to make a Developer life easier
- 15 can't miss commands in dbatools 1.0
- Automating Machine Learning
- Automating Power BI Deployments
- 6 months to 5 minutes, a horror story
- State of Machine Learning in Azure in 2019
- The Haberdashery of Machine Learning
- Driving Power BI automation through monitoring
- Applying DevOps practices to Machine Learning