Prabhas Kumar Samanta

Software Engineer

Redmond, Washington, United States14 yrs 9 mos experience
Most Likely To SwitchHighly Stable

Key Highlights

  • Nine years of experience in data management systems.
  • Expertise in query processing and distributed systems.
  • Proven track record in product development across multiple domains.
Stackforce AI infers this person is a Data Management and Software Development expert with a focus on distributed systems.

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Skills

Core Skills

Distributed SystemsSoftware DevelopmentData StructuresSql

Other Skills

AlgorithmsCC++EclipseJavaPythonShell Scripting

About

Nine years of work experience in data management system internals. Contributed to product development across domains varying OLTP, OLAP, In-Memory and big data management. Experience in hands-on development, end-to-end feature design along with the technical leadership on areas like query processing, data partitioning, distributed system, data-structures and algorithms.

Experience

Microsoft

Software Engineer

Feb 2020Present · 6 yrs 1 mo

  • Working in Azure Data Query Processing team.
Data StructuresJavaSQLDistributed SystemsSoftware Development

Qubole

Member Of Technical Staff

Apr 2019Feb 2020 · 10 mos · Bengaluru Area, India

  • Worked as a part of HIVE team. Focused on projects to make queries interactive and faster.
SQLData StructuresAlgorithms

Sybase

Software Engineer

Jul 2011Apr 2019 · 7 yrs 9 mos · Pune Area, India

  • Unbalanced Partition in HANA
  • Introducing a new flexible multilevel partitioning scheme in HANA. Replacing traditional scheme where user is limited to two level partitioning and fixed format. Enables user to create a suitable partitioning structure based on data distribution, skewness as well as use of a new extended property set.
  • Synopsis Partition Pruning in HANA
  • Extends scope of partition pruning beyond partition column. Metadata for these columns are dynamically maintained and used during query execution to prune an irrelevant partition.
  • Simplified Native Access Plan
  • Achieves fast query processing by just-in-time compilation of incoming queries. By producing machine code at run time we avoid the overhead of traditional interpretation systems. Generated code is tailor made for each individual query and this results in reduced number of machine instruction and better instruction locality.
  • Data Federation from ASE to Other Remote Server
  • Adds ability in ASE to talk to non-ASE remote server and execute a query which may use table from both local and remote server. ASE prepares query plan and depending on the plan, query could be fully or partially executed at remote server.
  • Precompiled Result Set
  • PRS is equivalent to materialized view. Result set of a query is materialized and maintained when the base tables change. The main challenge is to automatically refresh the resultset when base tables change without recomputing the whole query. To optimize, we just compute the delta and apply in the resultset.
Data StructuresC++SQLAlgorithms

Education

Indian Institute of Technology, Bombay

MTech — Computer Science

Jan 2009Jan 2011

West Bengal University of Technology, Kolkata

Bachelor of Technology - BTech — Information Technology

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