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Prateek Gaur

from San Jose, CA
Age ~36
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Also known as:
Prateek Graur
Phone and address:
2060 Mendocino Ln, San Jose, CA 95124
Connected to:
Anjali Gaur, 31Ashok K Gaur, 72Atul Gaur, 56Babita Gaur, 48Kavita Gaur, 40Lakshmi K Gaur, 70Neil K Gaur, 42Nidhi Gaur, 36Prachi Gaur, 39Sudhir Gaur, 51

Prateek Gaur Phones & Addresses

  • 2060 Mendocino Ln, San Jose, CA 95124
  • Mountain View, CA
  • San Francisco, CA

Work

Company: Pocket gems Oct 2012 to Sep 2014 Position: Software engineer

Education

School / High School: Indian Institute of Technology, Madras 2007 to 2012 Specialities: Computer Science, Engineering, Computer Science and Engineering

Skills

Golang • C/C++ • Objective C • Ios • Python • Android • Mobile Applications • Git

Languages

English • Hindi

Interests

Health 2 • Big Data • Job Interviews • Vim • Data Science • Mobile Healthcare • Rock Health • Mobile Social Games • Elon Musk • Health Informatics • Mobile Game Design and Development • Startups In India • Startups • Data Mining • Online and Mobile Payments

Industries

Computer Software

Resumes

Resumes

Prateek Gaur Photo 1

Staff Software Engineer

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Location:
Palo Alto, CA
Industry:
Computer Software
Work:
Pocket Gems Oct 2012 - Sep 2014
Software Engineer
Silicon Tech Labs Jul 2012 - Sep 2012
Software Developer
Indian Institute of Technology, Madras Jan 2012 - Apr 2012
Teaching Assistant
Cuhk May 2011 - Jul 2011
Software Intern
Karlsruher Institut Für Technologie (Kit) May 2010 - Jul 2010
Software Intern
Education:
Indian Institute of Technology, Madras 2007 - 2012
Delhi Public School, Jodhpur 2004 - 2006
Delhi Public School, Jodhpur 2002 - 2004
Skills:
Golang
C/C++
Objective C
Ios
Python
Android
Mobile Applications
Git
Interests:
Health 2
Big Data
Job Interviews
Vim
Data Science
Mobile Healthcare
Rock Health
Mobile Social Games
Elon Musk
Health Informatics
Mobile Game Design and Development
Startups In India
Startups
Data Mining
Online and Mobile Payments
Languages:
English
Hindi

Publications

Us Patents

Aggregation Operations In A Distributed Database

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US Patent:
20220309067, Sep 29, 2022
Filed:
Mar 26, 2021
Appl. No.:
17/214247
Inventors:
- Sunnyvale CA, US
Ambareesh Sreekumaran Nair Jayakumari - Cupertino CA, US
Prateek Gaur - San Jose CA, US
Donko Donjerkovic - San Mateo CA, US
International Classification:
G06F 16/2455
G06F 16/27
G06F 16/22
G06F 16/248
Abstract:
Querying a distributed database including a table sharded into shards distributed to database instances includes receiving a data-query that includes an aggregation clause on a first column and a grouping clause on a second column; obtaining and outputting results data. Obtaining the results data includes receiving, by a query coordinator, intermediate results data; and combining, by the query coordinator, the intermediate results to obtain the results data. Receiving the intermediate results data includes receiving, from a first database instance, first aggregation values indicating, on a per-group basis in accordance with the grouping clause, a respective aggregation value of distinct values of the first column in accordance with the aggregation clause, and receiving, from a second database instance, second aggregation values indicating, on a per-group basis in accordance with the grouping clause, a respective aggregation value of distinct values of the first column in accordance with the aggregation clause.

State-Sequence Pathing

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US Patent:
20230083123, Mar 16, 2023
Filed:
Sep 6, 2022
Appl. No.:
17/903571
Inventors:
- San Jose CA, US
Tushar Marda - Jaipur, IN
Bhanu Prakash - Bengaluru, IN
Sreenivas Kandhade - Bengaluru, IN
Sandeep Gottimukkala - Santa Clara CA, US
Jibin Thomas - Milpitas CA, US
Prateek Gaur - San Jose CA, US
Amit Prakash - Saratoga CA, US
International Classification:
G06F 16/242
G06F 11/34
Abstract:
State-sequence pathing in a low-latency data access and analysis system includes obtaining, by the low-latency data access and analysis system, predicate data responsive to a request for data expressed in previously obtained data expressing usage intent, obtaining, by the low-latency data access and analysis system, state-sequence pathing criteria identified with respect to the predicate data, obtaining, by the low-latency data access and analysis system, state-sequence path data in accordance with the predicate data and the state-sequence pathing criteria, wherein the state-sequence path data aggregates data representing multiple state-sequence paths, wherein a respective state-sequence path represents an ordered sequence of states of a system, wherein the states are represented individually by the predicate data, generating, by the low-latency data access and analysis system, state-sequence path visualization data for presenting a visualization of the state-sequence path data, and outputting, by the low-latency data access and analysis system, the state-sequence path visualization data.

Compacted Table Data Files Validation

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US Patent:
20230035166, Feb 2, 2023
Filed:
Jul 30, 2021
Appl. No.:
17/444078
Inventors:
- Sunnyvale CA, US
Nitin Motiani - Santa Clara CA, US
Prateek Gaur - San Jose CA, US
International Classification:
G06F 16/23
G06F 16/22
Abstract:
Database replay log compaction verification includes identifying at least one replay log of a table that includes first database manipulation commands; obtaining a compacted replay log that includes second database manipulation commands that are insert commands, where an insert command includes a column and a corresponding value; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.

Query Execution On Compressed In-Memory Data

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US Patent:
20210109974, Apr 15, 2021
Filed:
Oct 13, 2020
Appl. No.:
17/069162
Inventors:
- Sunnyvale CA, US
Prateek Gaur - Mountain View CA, US
Amit Prakash - Saratoga CA, US
Abhishek Rai - Mountain View CA, US
International Classification:
G06F 16/903
G06F 3/06
Abstract:
Query execution on compressed in-memory data includes receiving, at a processor of an instance of a distributed in-memory database, a query for data from a table stored in the distributed in-memory database as compressed table data, obtaining results data responsive to the query from the table, and outputting the results data for presentation to a user. Obtaining results data includes allocating memory to identify allocated memory for decompressing the compressed table data, obtaining uncompressed table data by decompressing the compressed table data into the allocated memory, and obtaining the results data from the uncompressed table data. The allocated memory is deallocated in response to obtaining the results data. Compressing a table to form compressed table data is also described.

Machine Language Query Management For Low-Latency Database Analysis System

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US Patent:
20210089530, Mar 25, 2021
Filed:
Sep 18, 2020
Appl. No.:
17/024790
Inventors:
- Sunnyvale CA, US
Satyam Shekhar - San Jose CA, US
Prateek Gaur - Mountain View CA, US
Amit Prakash - Saratoga CA, US
International Classification:
G06F 16/2453
G06F 16/27
G06F 16/242
G06F 16/248
G06F 16/2455
Abstract:
Data-query execution with distributed machine-language query management in a low-latency database analysis system may include obtaining, at a distributed in-memory database, a data-query expressing a request for data in a defined structured query language associated with the distributed in-memory database, automatically generating a high-level language query representing at least a portion of the data-query, obtaining a machine language query corresponding to the high-level language query, executing the machine language query to obtain results data, and outputting the results data. Obtaining the machine language query may include determining whether the machine language query is cached, and in response to a determination that the machine language query is unavailable, sending a request for the machine language query to a distributed machine-language-query management instance.
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Prateek Gaur from San Jose, CA, age ~36 Get Report