| Start Time (24h) | Speaker | Track | Title |
|---|---|---|---|
| 09:45:00 | Mladen Prajdić | Application Database Development | An End-to-End Approach to App and DB Security |
| 09:45:00 | Aleksandar Talev | Application Database Development | Retail Point-of-sale scenario using Azure Cosmos DB |
| 09:45:00 | Blagoj Kostovski | Application Database Development | Adaptive Query processing in action |
| 09:45:00 | Kamil Nowinski | Advanced Analysis Techniques | Azure Databricks 101 |
| 11:00:00 | Marcelo Fernandes | Advanced Analysis Techniques | Effectively Troubleshooting Latency and Failover of Always On Availability Group |
| 11:00:00 | Mladen Prajdić | Application Database Development | Getting To Know SQL Server Encryption Capabilities |
| 11:00:00 | Damir Matesic | Application Database Development | MS SQL New functions, syntaxes, tips tricks |
| 11:00:00 | André Melancia | Analytics and Visualization | BI Powers - The A.I. Who Tagged Me |
| 12:15:00 | Fisnik Doko | Advanced Analysis Techniques | Predictive Analysis with SQL Server Machine Learning Services |
| 12:15:00 | André Melancia | Advanced Analysis Techniques | If a computer can understand, why can't YOU understand Azure Cognitive Services? |
| 12:15:00 | Vlatko Ivanovski | Cloud Application Development Deployment | MLOps – Applying DevOps to Data Science |
| 12:15:00 | Boriana Petrova | BI Platform Architecture, Development Administration | Time intelligence function in DAX |
| 14:00:00 | Mislav Karaula | BI Platform Architecture, Development Administration | What can Azure do for your batch data analytics workloads? |
| 14:00:00 | Dejan Sarka | Application Database Development | Temporal Data in SQL Server |
| 14:00:00 | Dejan Vakanski | Advanced Analysis Techniques | Microsoft SQL Server 2019 big data clusters |
| 14:00:00 | Marjan Pushev | Application Database Development | Handling concurrency in Real-time Online Booking System |
| 15:15:00 | Marcin Szeliga | Advanced Analysis Techniques | AutoML at scale |
| 15:15:00 | Dejan Sarka | Advanced Analysis Techniques | Statistical Analysis with T-SQL |
| 15:15:00 | Aleksandar Talev | Cloud Application Development Deployment | How to choose the right data model: SQL Server or Azure Cosmos DB. Which, When and Where ? |
| 15:15:00 | Kamil Nowinski | BI Platform Architecture, Development Administration | Azure Data Factory v2: Mapping Data Flows - first blood |
| 16:30:00 | Blagoj Kostovski | Application Database Development | Automatic database tuning with Query Store |
| 16:30:00 | Marcin Szeliga | Analytics and Visualization | AI in Power BI |
| 16:30:00 | Tomislav Bronzin | Advanced Analysis Techniques | Deep Learning advancements and why data is the key ingredient of AI revolution today? |
Event Date: 17-04-2021 - Session time: 09:45:00 - Track: Application Database Development
Event Date: 17-04-2021 - Session time: 09:45:00 - Track: Application Database Development
In this presentation we will demonstrate a scenario which is relevant to organizations that need a scalable and elastic architecture for online order processing or more specifically e-commerce and retail point-of-sale transactions such as orders, payments and bills.
This scenario takes an event sourcing approach, using a functional programming model implemented via Azure Cosmos DB change feed processor and additional Azure Services. This approach enables high availability and resiliency, geo-replication, and fast performance.
Event Date: 17-04-2021 - Session time: 09:45:00 - Track: Application Database Development
In this session we will talk about the key components of adaptive query processing, Batch mode Memory grant feedback, Adaptive joins and Interleaved execution of table valued functions. We will also discuss the improvements that are coming in SQL Server 2019. Finally we will show how we implemented this on real business case.
Come to learn how we utilized Adaptive query processing module to boost the performance on our most critical calculation procedure.
Event Date: 17-04-2021 - Session time: 09:45:00 - Track: Advanced Analysis Techniques
Use Databricks to analyse large DataSets at scale, write Python, Scala or SQL command in one notebook to ingest, process and push the data to the required target. Use Databricks' notebook as a part of Azure Data Factory pipeline. We also will try to answer whether Databricks would replace SSIS as a modern ETL/ELT process? If you are wondering about all these things - you should join me in this session.
Event Date: 17-04-2021 - Session time: 11:00:00 - Track: Advanced Analysis Techniques
Event Date: 17-04-2021 - Session time: 11:00:00 - Track: Application Database Development
In this session we'll look at what SQL Server offers in this area:
Event Date: 17-04-2021 - Session time: 11:00:00 - Track: Application Database Development
Accompanied with multiple examples, this session will show you many interesting topics.
Event Date: 17-04-2021 - Session time: 11:00:00 - Track: Analytics and Visualization
Disclaimer: A.I. will eventually take over the world. Unlike Doctor Evil, it won't ask for a ransom.
Event Date: 17-04-2021 - Session time: 12:15:00 - Track: Advanced Analysis Techniques
In this session we will talk how you can use SQL Server Machine Learning Services (SQL Server R Services in SQL Server 2016), which gives the ability to run R/Python scripts in SQL Server. Avoid unnecessary data movements and execute scripts in-database without moving data outside SQL Server or over the network.
This session will show how Microsoft SQL Server can help meet an increasingly “predictive” world by supporting the R/Python language inside the database.
Demonstration of using R and SQL Server Machine Learning Services.
Event Date: 17-04-2021 - Session time: 12:15:00 - Track: Advanced Analysis Techniques
If you are a developer and know nothing about Azure Cognitive Services for free, this is a good place to start. Disclaimer: Cognitive services will never guess a doctor's handwriting. That's just impossible.
Event Date: 17-04-2021 - Session time: 12:15:00 - Track: Cloud Application Development Deployment
In this session, you’ll learn how to apply DevOps practices to your machine learning projects using Azure DevOps and Azure Machine Learning Service. We’ll set up automated training, scoring, and storage of versioned models and wrap the models in docker containers and deploy them to Azure Container Instances and Azure Kubernetes Service. We’ll even collect continuous feedback on model behavior so we know when to retrain.
Event Date: 17-04-2021 - Session time: 12:15:00 - Track: BI Platform Architecture, Development Administration
Event Date: 17-04-2021 - Session time: 14:00:00 - Track: BI Platform Architecture, Development Administration
Event Date: 17-04-2021 - Session time: 14:00:00 - Track: Application Database Development
This session introduces the temporal problems, deals with solutions that go beyond SQL Server 2016 to 2019 support, and shows out-of-the-box solution in SQL Server, including: • Defining temporal data • Application versioned tables • System versioned tables • What kind of temporal support is still missing
Event Date: 17-04-2021 - Session time: 14:00:00 - Track: Advanced Analysis Techniques
Event Date: 17-04-2021 - Session time: 14:00:00 - Track: Application Database Development
We will discuss the challenges that appear in the online booking process and will provide solutions
Event Date: 17-04-2021 - Session time: 15:15:00 - Track: Advanced Analysis Techniques
Come and see how Azure Machine Learning Service reduce the demand for data scientists by enabling domain experts to automatically build machine learning applications without expert programming skills and extensive knowledge of machine learning algorithms.
Event Date: 17-04-2021 - Session time: 15:15:00 - Track: Advanced Analysis Techniques
Event Date: 17-04-2021 - Session time: 15:15:00 - Track: Cloud Application Development Deployment
Event Date: 17-04-2021 - Session time: 15:15:00 - Track: BI Platform Architecture, Development Administration
But, has that been only UI introduction? Why and how Databricks does work under the hood? Do you want to know this new feature of ADF and reveal the power of modern big data processes without knowledge of such languages like Python or Scala? We will review this new feature of ADFv2, do deep dive to understand the mentioned techniques, compare them to SSIS and/or T-SQL and learn how modelled data flow runs Scala behind the scenes.
Event Date: 17-04-2021 - Session time: 16:30:00 - Track: Application Database Development
Some of the scenarios where the Query Store feature can be useful are: • Find the most expensive queries for CPU, I/O, Memory etc. • Get full history of query executions • Get information about query regressions (new execution plan is worse than the older one) • Force specific query plan and Automatic plan correction • Automatic index tuning • Provide useful insights about needed code improvements This session will include overview of the Query Store capabilities, configuration instructions and live demos showing the main capabilities. We will also cover Query Store improvements for SQL Server 2017 and SQL Server 2019 versions.
Event Date: 17-04-2021 - Session time: 16:30:00 - Track: Analytics and Visualization
Although Power BI is extremely popular, not many users know that how to take analytict to next level by correctly leveraging advanced AI capabilities. Artificial Intelligence features in Power BI can be used for data understanding and preparation. During this session we will have a closer look at AI visuals and enrich our dataset using Power Query functions calling Cognitive Services API.
Event Date: 17-04-2021 - Session time: 16:30:00 - Track: Advanced Analysis Techniques
Traditionally AI systems have been tested in chess and the same has been done to demonstrate the power of AlphaZero. But, instead of playing against human, it played against the best (at the time) chess program Stockfish. It learned about the game by playing a large number of games with itself, learning in the process.
Session will also explain what is so revolutionary in AlphaZero approach to AI and how this new approach can be used in different areas of processing visual information, bio-medicine, autonomous driving, robotics and AI generated images/videos of humans. IoT and large amounts of machine-generated data is the main ingredient of AI today.