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Date/Time
Date(s) - 09/03/2019
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 |
| 9: 30 am – 10: 30 am | BoT Implementation in BI / Data Warehousing Space | Raising above the conventional BoT development methods, the new BoT Framework allows you to build, test, deploy and manage a BoT all at one place. It helps in integrating NLP (Natural Language Processing) with LUIS (Language understanding Intelligent Services), QnA maker, Speech recognition and other such technologies. In Business Insights and Data Warehousing, BoTs can be of great help in dealing with cases of User Education, Hot Path Scenarios, and other scenarios where the user wants to get information of the existing systems without undergoing the hassle of creating and traversing through different apps and reports. BoT AI makes it possible to get this critical information faster, quicker and cheaper. | Data Science | Sanjli Agarwal (Microsoft) & Prakhar Agarwal (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 | Data Science using R services in SQL Server 2016 | R Services is an add-on to a SQL Server 2016 database engine instance, used for executing R code and functions on SQL Server. Code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R statements, or as R code containing T-SQL.R Services includes a base distribution of R, overlaid with enterprise R packages from Microsoft so that you can load and process large amounts of data on multiple cores and aggregate the results into a single consolidated output. Microsoft’s R functions and algorithms are engineered for both scale and utility: delivering predictive analytics, statistical modeling, data visualizations, and leading-edge machine learning algorithms in a commercial server product engineered and supported by Microsoft. R libraries include RevoScaleR, MicrosoftML (R), and others. Because R Services is integrated with the database engine, you can keep analytics close to the data and eliminate the costs and security risks associated with data movement. | Data Science | Sudhansu Taparia (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 Basics using R & Python | When computers were introduced, People wondered if there could be a machine that can think on its own and become intelligent eventually. Today, Artificial Intelligence (AI) is a thriving field with many practical applications and lot of active research topics. There’s no field where Artificial Intelligence is not used or cannot be implemented. Researchers tried solving many problems in the early phase of evolution which were relatively straight forward that have been described by the list of mathematical / Statistical rules. Artificial intelligence, Deep Learning are kind of machine learning techniques. To begin with, we should have a good understanding of the basic principles of Machine Learning Algorithms that can help train the machines to make it intelligent. Machine learning is essentially a form of applied statistics with lot of emphasis on the use of computers to statistically estimate complicated functions and find patterns on sample set / new set of data. A machine learning algorithms are set of algorithms that are able to learn from data about the experiences, trends, tasks, performance measures and improves the experience with new set of data. Some of the common machine learning tasks include Regression, Classification, Synthesis and Sampling, Imputation of missing values. We can look into few machine learning algorithms to train the machines and apply them to real world examples. | Data Science | Deepak Pradeep Kumar (Microsoft) |
| 02: 30 pm – 02: 45 pm | – | (Tea/Coffee Break) | – | – |
| 02: 45 pm – 03: 45 pm | Deep Dive Into Databricks Delta | Azure Databricks brings together the best of Microsoft Azure and the best of Databricks to form a unified Analytics platform. With the General Availability of Databricks Delta, many challenges faced in traditional Analytics are simplified. In this session we will dig deeper into what is Databricks Delta and how it builds on top of Parquet file format to further enhance file access speed. Further, we will look into how Databricks delta combines batch and streaming data pipelines into one and provides a simple unified pipeline that can serve both streaming and Reporting analytics. | Data Science/Big Data | Kranthi Kumar Medam (Microsoft) |
| 03: 45 pm – 04: 00 pm | – | (Closing & Prize Distribution) | – | – |
| Time | Session Title | Abstract | Track | Speaker |
| 09: 30 am – 10: 30 am | Real Time Analytics using Structured Streaming | This session talks about how to build real time pipelines for event based scenarios ready to be consumed and available through reporting platforms and how it is different from traditional BI ETLs. Concepts covered: Streaming datasets ,Lambda architecture, | Azure | Vidhi Raheja (Microsoft) & Vivek Gupta (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 | Spark Essentials for SQL DBA | This session will be focused on the architecture of spark and how to create and run spark sql, rdd, datasets on spark. What is Microsoft doing in this space with SQL Server 2019 and its integration of spark and ML platform and how this can be leveraged by anyone who is trying to re-skill from SQL. | Database Administration and Development | Hitesh Chouhan (Microsoft) and Chinmay Das (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 | SQL Data Compression & Column Store Indexes | – | Database Administration and Development | Vijay Reddy (Regional Mentor, DPG) |
| 02: 30 pm – 02: 45 pm | – | (Tea/Coffee Break) | – | – |
| 02: 45 pm – 03: 45 pm | Temporary Tables Vs Table Variables – SQL Server | – | Database Administration and Development | Satya Ramesh (Technical Consultant, SQLMaestros) |
| 03: 45 pm – 04: 00 pm | – | (Closing & Prize Distribution) | – | – |
| Lunch Arrangements (please read carefully): |
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| City/Location | Hyderabad |
| Date & Time | 09th March 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.