| Start Time (24h) | Speaker | Track | Title |
|---|---|---|---|
| 09:00:00 | Miguel Arturo Valle Pelaez | Ai and Machine Learning | IA SQL Server 2019 Machine Learning Service |
| 09:00:00 | Gian Carlo Poggi Escobar | Data Warehousing | Azure Synapse Analytics - Power BI Integration Performance |
| 09:00:00 | Raul Martin Sarachaga Diaz | Power BI | Consultando Millones de Registros con Power BI |
| 10:00:00 | Alex Rostan | Ai and Machine Learning | AI Power - Cognitive Services and Power BI |
| 10:00:00 | Martha Chávez | Data Warehousing | Working with Data Factory |
| 10:00:00 | Miguel Martinez | Power BI | 3 formas de monitorear datos en tiempo real con Power BI |
| 11:00:00 | Kamal Valero | Ai and Machine Learning | Detección de anomalias en Azure Stream Analytics usando Machine Learning |
| 11:00:00 | Ana Maria Bisbé York | Power BI | Escenarios de modelado en DAX, el súper lenguaje de Power BI. |
| 11:00:00 | Ahias Portillo | Data Warehousing | Técnicas de consultas avanzadas para Datawarehouse |
| 13:00:00 | Maximiliano Damian Accotto | Data Warehousing | SQL Server Machine Learning y Big Data cluster |
| 13:00:00 | Cristobal Ibarra | Ai and Machine Learning | AI Video Insights with Azure |
| 13:00:00 | Alejandro Humberto Sánchez Núñez | Power BI | Power BI Intelligence - All Types Calendars |
| 14:00:00 | Gaston Cruz | Power BI | Ventajas en Enterprise BI con XMLA Endpoints |
| 14:00:00 | Ricardo Estrada | Data Warehousing | Patrones avanzados con SSIS |
| 14:00:00 | Raul Martin Sarachaga Diaz | Ai and Machine Learning | Computer Vision con Azure Cognitive Services |
| 15:00:00 | Javier Villegas | Power BI | Casos de uso de Power BI y Analysis Services |
| 15:00:00 | Mariano Irvin Lopez Jaramillo | BI Information Delivery | Building an EDSS with Delta Lake in Databricks |
| 15:00:00 | Juan Rafael | Power BI | PowerBI con Dynamics 365 for Sales. |
| 15:00:00 | Sonia Bravo | Data Warehousing | Integración y entrega continua (CI/CD) para procesos de analítica de datos sobre Azure Databricks |
Event Date: 22-08-2020 - Session time: 09:00:00 - Track: Ai and Machine Learning
SQL SERVER Machine Learning Services es una característica que brinda la capacidad de ejecutar scripts Python y R con datos relacionales. Puede usar paquetes y marcos de código abierto, y los paquetes Microsoft Python y R, para análisis predictivo y aprendizaje automático.
Event Date: 22-08-2020 - Session time: 09:00:00 - Track: Data Warehousing
Event Date: 22-08-2020 - Session time: 09:00:00 - Track: Power BI
Event Date: 22-08-2020 - Session time: 10:00:00 - Track: Ai and Machine Learning
Event Date: 22-08-2020 - Session time: 10:00:00 - Track: Data Warehousing
Event Date: 22-08-2020 - Session time: 10:00:00 - Track: Power BI
Event Date: 22-08-2020 - Session time: 11:00:00 - Track: Ai and Machine Learning
Event Date: 22-08-2020 - Session time: 11:00:00 - Track: Power BI
En la sesión se analizan y solucionan un grupo de escenarios de modelado con DAX y Power BI. Estos escenarios son los que nos encontramos con frecuencia en proyectos.
Event Date: 22-08-2020 - Session time: 11:00:00 - Track: Data Warehousing
Aprenderás: • Uso de CTE. • Uso de Windows Functions. • Expandiendo las fronteras de las funciones de agregación.
Event Date: 22-08-2020 - Session time: 13:00:00 - Track: Data Warehousing
También haremos una introducción a la arquitectura de SQL Server 2019 Big Data cluster
Event Date: 22-08-2020 - Session time: 13:00:00 - Track: Ai and Machine Learning
Event Date: 22-08-2020 - Session time: 13:00:00 - Track: Power BI
Event Date: 22-08-2020 - Session time: 14:00:00 - Track: Power BI
Event Date: 22-08-2020 - Session time: 14:00:00 - Track: Data Warehousing
Event Date: 22-08-2020 - Session time: 14:00:00 - Track: Ai and Machine Learning
Event Date: 22-08-2020 - Session time: 15:00:00 - Track: Power BI
Event Date: 22-08-2020 - Session time: 15:00:00 - Track: BI Information Delivery
Data warehouses have a long history in decision support and business intelligence applications, though were not suited or were expensive for handling unstructured data, semi-structured data, and data with high variety, velocity, and volume.
Data lakes then emerged to handle raw data in a variety of formats on cheap storage for data science and machine learning, though lacked critical features from the world of data warehouses: they do not support transactions, they do not enforce data quality, and their lack of consistency/isolation makes it almost impossible to mix appends and reads, and batch and streaming jobs.
Data teams consequently stitch these systems together to enable BI and ML across the data in both these systems, resulting in duplicate data, extra infrastructure cost, and security challenges.
Data lakehouses are enabled by
Event Date: 22-08-2020 - Session time: 15:00:00 - Track: Power BI
Event Date: 22-08-2020 - Session time: 15:00:00 - Track: Data Warehousing