DataPlatformGeeks successfully executed FOUR webinars and TWO In-person events in the month of October, 2019.

  • Webinar on DELETE vs TRUNCATE – Myths & Misconceptions by Satya Ramesh (Sr. Consultant, SQLMaestros)
  • Webinar on Plan Caching & Recompilation in SQL Server by Amit Bansal (MVP, MCM, SQLMaestros)
  • Webinar on Art of Feature Engineering for Machine Learning by Sandip Pani (Sr. Data Architect)
  • Webinar on Power Apps and Flow Best Practices – Working with SQL Server by Manohar Punna (Data Platform MVP, Australia)
  • DELETE vs TRUNCATE – Myths & Misconceptions

    Abstract: DELETE and TRUNCATE are two commands that helps in data deletion. Even though these commands are considered to be most basic ones, still there are lot of misconceptions around them, especially TRUNCATE. TRUNCATE can’t be logged, TRUNCATE can’t be rolled back etc. In this session, we will try to debunk all the myths and misconceptions.

    Art of Feature Engineering for Machine Learning

    Abstract: Building ML model is easy if you know your data well. According to a survey in Forbes, data scientists spend 80% of their time on data preparation. The features in your data are important to the predictive models you use and will influence the results you are going to achieve. Though Deep learning takes care of feature engineering process by itself, but many ML model still require to go through this Feature Engineering phase. In this session I will be presenting on various feature engineering techniques such as Imputation, Handling Outliers, Binning, Log Transform, One-Hot Encoding, Grouping Operations, Feature Split, Scaling, Handling Date. It will be a demo heavy session where we will explore different techniques of Feature engineering. Building ML model is easy if you know your data well. According to a survey in Forbes, data scientists spend 80% of their time on data preparation. The features in your data are important to the predictive models you use and will influence the results you are going to achieve. Though Deep learning takes care of feature engineering process by itself, but many ML model still require to go through this Feature Engineering phase. In this session I will be presenting on various feature engineering techniques such as Imputation, Handling Outliers, Binning, Log Transform, One-Hot Encoding, Grouping Operations, Feature Split, Scaling, Handling Date. It will be a demo heavy session where we will explore different techniques of Feature engineering.

    Power Apps and Flow Best Practices – Working with SQL Server

    Abstract: Microsoft PowerApps and Flow have seen tremendous adoption in recent years. With an ever-growing need for rapid and agile solution development, these two products have the potential to solve business problems with very minimal development efforts. However, as with any product, with great simplification comes greater problems. As a data professional, it is our responsibility to make sure the data layer is accessed with proper security and efficiency. In this session, I will explore different methods to connect to the data from PowerApps and Flow. In this demo filled session, I will cover different scenarios for sending and receiving data in PowerApps using Flows.

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