Gartner Expert

Sumit Pal

VP Analyst

Sumit Pal is a VP Analyst in the Data Management and Analytics group on the Gartner for Technical Professionals team. Sumit provides guidance on Lakehouses, Data Lake, Stream Processing Architectures, Modern data architecture (AWS, GCP), NoSQL databases (specializes in Graph Databases, Time Series Databases), Knowledge Graphs, Cloud Data Migration, Data Engineering, Dev and Data Ops and SQL engines for big data low-latency applications and data warehouses.

Previous experience

Prior to Gartner, Mr. Pal was an Independent Consultant working with multiple clients, both enterprise and startup in healthcare, energy and consulting sector developing big data applications and solutioning big data architectures for modern data platforms for batch and streaming workloads.

Sumit has published a book in 2016 called - SQL Engines for Big Data and also developed a MOOC - Big Data Analyst (with Experfy)

He also worked on building ML and AI applications powered by large-scale distributed processing systems like Spark with Scala and Python & Keras.

Big Data Technology

Big Data Solution Architecture and Implementations

Hadoop, HDFS, NoSQL, Spark, Java, Scala, Python, R, Machine Learning, Data Science, Algorithms

Strategy Consulting, Building Modern Data Platforms and Recommending Architectures and solutions.

Mr. Pal has authored a book with Apress - "SQL on Big Data - Technology, Architecture and Innovations" and developed a MOOC - Big Data Analyst with Experfy.

Professional background

Independent Consultant Big Data, ML, AI

Independent Consultant Big Data, ML, AI


Associate Director of Big Data

LeapFrogRX (Acquired by ModelN)

Technical Director / Chief Architect

Areas of coverage

Data Management Solutions

Analytics, BI and Data Science Solutions


M.S., Computer Science, Asian Institute Of Technology, Bangkok, Thailand

B.E., Computer Science, Bengal Engineering College, West Bengal, India

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Top Issues That I Help Clients Address

1Data Lake and Big Data Architectures and Technology Implementations

2Develop Strategy for adopting Modern Data Architectures and Platforms

3NoSQL Database selection and modeling (Graph databases, Time Series) and Knowledge Graphs

4SQL Engines on Big Data Selections and Implementations for Data Mart

5Implement ML and AI Algorithms at Scale with Distributed Processing Systems