Date/Time
Date(s) - 20/04/2019
9:00 am - 4:30 pm
Location
Microsoft Campus
Knowledge Partner(s)
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Data Platform Day. Free (Full Day) Multi-Track Event – Schedule
Time | Session Title | Abstract | Track | Speaker |
09: 30 am – 10: 30 am | Azure Cosmos DB in Enterprise Architectures | Azure Cosmos DB is Microsoft’s globally distributed, multi-model database service that enables us to build applications and manage data at Planet scale. In this talk we will compare Cosmos DB with various data store models in the current world. And then dive into various Design and architecture patterns followed in Cosmos DB applications. | Azure | Kranthi Kumar Medam (Microsoft) |
10: 30 am – 10: 45 am | – | (Tea/Coffee Break) | – | – |
10: 45 am – 11: 30 am | – | DataPlatformGeeks Introduction | – | – |
11: 30 am – 11: 45 am | – | (Tea/Coffee Break) | – | – |
11: 45 am – 12: 45 pm | Machine Learning (Mulitple linear regression and Decision Trees) | – | Data Science | Deepak Pradeep Kumar (Microsoft) |
12: 45 pm – 01: 30 pm | – | (Lunch Break) (You need to buy your own lunch. Please see below) | – | – |
01: 30 pm – 02: 30 pm | Machine Learning (Dynamic Classifier Selection) | Over last decades, research from worldwide communities are investing lot of efforts in identifying and defining approaches which can predict possibility of defect occurrence of source code files based on data captured as static software metrics for Object Oriented design. These approaches use machine learning to classify classes into buggy or not buggy or providing possibility that a class can show faulty behaviors in future. Empirical evaluation of various approaches available to predict fault behavior indicated that there is no machine learning classifier which is providing best accuracy. Since there are no classifiers which can provide best accuracy ensemble methods are developed to estimate the bug-proneness of a class by combining predication outcome obtained from different classifiers. Following on the grounds of ensemble learning we will create an adaptive approach for performing bug prediction by dynamically selecting one classifier from set of machine learning classifiers that predicts if a class is prone to bug or not based on characteristics of static software metrices captured for class. Therefore, I am proposing new Adaptive Approach for Bug Prediction using Dynamically Classifier Selection (ADCS) which dynamically selects best Supervised learner which better predicts bug-proneness of a class based on characteristics of class. | Data Science | Ashutosh Upadhyay (Microsoft) |
02: 30 pm – 02: 45 pm | – | (Tea/Coffee Break) | – | – |
02: 45 pm – 03: 45 pm | SQL Server Backup and Restore | – | Database Administration and Development | Satya Ramesh (Technical Consultant, SQLMaestros) |
03: 45 pm – 04: 00 pm | – | (Closing & Prize Distribution) | – | – |
Time | Session Title | Abstract | Track | Speaker |
09: 30 am – 10: 30 am | Building the Data Lake using Azure Databricks | – | Azure | Himanshu Gohel (Microsoft) |
10: 30 am – 10: 45 am | – | (Tea/Coffee Break) | – | – |
10: 45 am – 11: 30 am | – | DataPlatformGeeks Introduction | – | – |
11: 30 am – 11: 45 am | – | (Tea/Coffee Break) | – | – |
11: 45 am – 12: 45 pm | Structured Streaming with Azure Databricks | – | Azure | Veeren Kumar Chimbili (Microsoft) & Ramakrishna Vatrakunta (Microsoft) |
12: 45 pm – 01: 30 pm | – | (Lunch Break) (You need to buy your own lunch. Please see below) | – | – |
01: 30 pm – 02: 30 pm | Real World Query Tuning in SQL Server (HD Recorded Session) | – | Database Administration & Development | Amit Bansal (SQLMaestros) |
02: 30 pm – 02: 45 pm | – | (Tea/Coffee Break) | – | – |
02: 45 pm – 03: 45 pm | SQL Server Data Partitioning | – | Database Administration and Development | Vijay Reddy (Regional Mentor, DPG) |
03: 45 pm – 04: 00 pm | – | (Closing & Prize Distribution) | – | – |
Lunch Arrangements (please read carefully): |
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City/Location | Hyderabad |
Date & Time | 20th April 2019 (9:00 AM – 4:30 PM) Note: Come by 8:00 AM for registration and security formalities. Please be seated by 8:45 AM. |
Venue | Survey No: 210, Microsoft Campus (Gachibowli. Hyderabad), Building 3, Mannikonda Jagir, Gachibowli, Hyderabad, Telangana 500032 |
FAQ | • Why is the lunch coupon compulsory? As stated earlier, Data Platform Day is a full day event targeted towards people who wish to learn with us for the whole day and are willing to spend their Saturday with us, learning & networking. To ensure that lunch is available for our attendees who are there for the whole day, we have to give Microsoft Cafeteria a confirmed number so that lunch can be prepared accordingly. To achieve that, buying a lunch coupon @ INR 124 is compulsory. And we strongly discourage half-day attendance. Lunch coupon is just 124 rupees (highly subsidized). • Can I attend the event half-day? • Can I come in second half? • I don’t want to have lunch at Microsoft Cafeteria. I prefer to eat outside and come back. Is that possible? • Apart from buying the lunch coupon, do I need to pay for anything else? • Is the amount refundable? |
Bookings
Bookings are closed for this event.