Companies are trying to gain a competitive edge by building strong analytical foundations and upping their skills in data engineering, data science and business intelligence. But these new technologies and paradigms come with their shortcomings, pitfalls and antipatterns that novice and experienced data professionals can fall into and mistakenly generate technical debt.
Join our seasoned Avanade data experts in this session covering architecture design patterns, common mistakes when implementing an analytical foundation with Microsoft Azure and how to resolve them. The following topics will be covered:
- Elements to consider when starting an analytics initiative
- Architecture Design Patterns with Databricks, Data Factory, SQL Server, Synapse and Power BI
- Common Mistakes and Solutions (organisation, teams, communications, SDLC)
- Best practices to succeed in your analytics initiative
Data Engineering, Group Manager
Data Engineering, Senior Consultant