Sumit Pal

Sumit Pal

Strategic Technology Director at Ontotext

Sumit is an Ex Gartner VP Analyst in Data Management & Analytics space where he advised CTOs, CDOs, CDAOs, Enterprise Architects and Data Architects on Data Strategy, Data Architectures, Data Engineering for building data platforms. Sumit spans the spectrum - from formulating data strategy with CDO/CTO teams to architecting, designing and building data platforms and solutions to writing, deploying and debugging. With more than 25y of experience in data and Software Industry roles spanning companies from startups to enterprise organizations in building, managing, guiding teams to build scalable software systems across the stack from data layer, analytics using BigData, NoSQL, DB Internals, Data Warehousing, Data Modeling, Data Science and AI. Sumit has experience in building, managing and guiding teams and building scalable software systems across the stack from middletier, data layer, Analytics , ML, Data Engineering, DataOps, Data Architectures, Data Lakes, Data Lakehouses, NoSQL, DB Internals, Data Warehousing, Dimensional Modeling, Data Science and Java / J2EE aspects of the technology. Published author of a book on SQLEngines and developed MOOC course on Big Data Hiked to Mt. Everest Base Camp in Oct 2016. Blogs at https://sumitpal.wordpress.com/

All Sessions by Sumit Pal

Building Knowledge Graphs

Intermediate

Knowledge graphs are all around us and we are using them everyday. Lot of the emerging Data management products like Data Catalogs/Fabric, MDM products are leveraging Knowledge Graphs as their engines. A knowledge graph is not a one-off engineering project. Building a KG requires collaboration between functional domain experts, data engineers, data modelers and key sponsors. It also combines technology, strategy and organizational aspects; focusing only on technology leads to a high risk of a KG’s failure. KGs are effective tools for capturing and structuring a large amount of structured, unstructured and semistructured data. As such, KGs are becoming the backbone of different systems, including semantic search engines, recommendation systems, and conversational bots and data fabric. This session guides data and analytics professionals to show the value of Knowledge Graphs and how to build build semantic applications.

Building Knowledge Graphs

Intermediate

Knowledge graphs are all around us and we are using them everyday. Lot of the emerging Data management products like Data Catalogs/Fabric, MDM products are leveraging Knowledge Graphs as their engines. A knowledge graph is not a one-off engineering project. Building a KG requires collaboration between functional domain experts, data engineers, data modelers and key sponsors. It also combines technology, strategy and organizational aspects; focusing only on technology leads to a high risk of a KG’s failure. KGs are effective tools for capturing and structuring a large amount of structured, unstructured and semistructured data. As such, KGs are becoming the backbone of different systems, including semantic search engines, recommendation systems, and conversational bots and data fabric. This session guides data and analytics professionals to show the value of Knowledge Graphs and how to build build semantic applications.

Building Knowledge Graphs

Intermediate

Knowledge graphs are all around us and we are using them everyday. Lot of the emerging Data management products like Data Catalogs/Fabric, MDM products are leveraging Knowledge Graphs as their engines. A knowledge graph is not a one-off engineering project. Building a KG requires collaboration between functional domain experts, data engineers, data modelers and key sponsors. It also combines technology, strategy and organizational aspects; focusing only on technology leads to a high risk of a KG’s failure. KGs are effective tools for capturing and structuring a large amount of structured, unstructured and semistructured data. As such, KGs are becoming the backbone of different systems, including semantic search engines, recommendation systems, and conversational bots and data fabric. This session guides data and analytics professionals to show the value of Knowledge Graphs and how to build build semantic applications.

12:10 pm - 12:40 pm

Building Knowledge Graphs

<span class=“etn-schedule-location”> <span class=“secfocus”> Intermediate</span>

Knowledge graphs are all around us and we are using them everyday. Lot of the emerging Data management products like Data Catalogs/Fabric, MDM products are leveraging Knowledge Graphs as their engines. A knowledge graph is not a one-off engineering project. Building a KG requires collaboration between functional domain experts, data engineers, data modelers and key sponsors. It also combines technology, strategy and organizational aspects; focusing only on technology leads to a high risk of a KG’s failure. KGs are effective tools for capturing and structuring a large amount of structured, unstructured and semistructured data. As such, KGs are becoming the backbone of different systems, including semantic search engines, recommendation systems, and conversational bots and data fabric. This session guides data and analytics professionals to show the value of Knowledge Graphs and how to build build semantic applications.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from - Youtube
Vimeo
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google