A "large data model" that is pre-trained on Snowflake data marketplace to generate useful data models over diverse datasets such as real estate, covid-19, economy atlas, economic indicators, work force, consumer engagement, and vendor analytics.
Business apps drive numerous functions in a company, and they in turn rely on data to power them. Smart data can power these apps to better meet their business objectives while doing more with less.
More businesses are looking to be data-driven in their core operations than ever before. Smart data powered apps can enable these businesses to differentiate while creating unique value for their customers.
Alekh has spent his career blended in academia and industry, propelling award-winning ideas from conception to adoption. His work includes the first MapReduce implementation with performance matching that of parallel DBMSs during his PhD, efficient graph analytics on column stores that outperforms specialized graph systems during his rese
Alekh has spent his career blended in academia and industry, propelling award-winning ideas from conception to adoption. His work includes the first MapReduce implementation with performance matching that of parallel DBMSs during his PhD, efficient graph analytics on column stores that outperforms specialized graph systems during his research at MIT, and learning-based workload optimization for cloud query engines at Microsoft, where he was also driving workload optimization work stream in Azure Data. Most recently he was founding Chief Architect, CTO, and board member at Keebo, where he built a data learning service for cloud data warehouses and helped the company go from 1 to n. Alekh has co-authored 80+ papers, filed 15 patents, and won 4 best paper awards. He has served on the program committee of several leading database conferences.
Shi has spent the last decade building advanced data processing engines that are efficient and performant. He received his PhD from Case Western working on graph analytics. Later he joined Microsoft where he has a track record of building and shipping several industry firsts in the broader area of workload optimization, including automati
Shi has spent the last decade building advanced data processing engines that are efficient and performant. He received his PhD from Case Western working on graph analytics. Later he joined Microsoft where he has a track record of building and shipping several industry firsts in the broader area of workload optimization, including automatic query subexpression reuse, learned cardinality estimation, petabyte scale data shuffle, predictive resource allocation, and predictive query planning. Shi’s work has helped Microsoft save millions of dollars of operational cost. He has a rich experience in applying AI/ML for system problems and in making learning-based automations practical for a cloud environment. Most recently, Shi was the Head of Engines at Keebo, building the next generation platform-agnostic query processing capabilities.
Systems and methods to provide platform agnostic query acceleration., wherein the query request conforms to a particular wire protocol. Read More
An efficient, parametric modeling framework for predictive resource allocations in Spark SQL queries running on Azure Synapse. Read More
An optimizer and recommender aimed at improving the performance of queries or jobs in Microsoft data pipelines, with 650K daily jobs and 70% inter-job dependencies. Read More
Optimize workload instances by adding a workload-driven feedback loop to the Spark query optimizer. Detailed analysis of production Spark workloads. Read More
A workload optimization platform for cloud query engines for representing, categorizing, and prescribing workload-awareness to query engines. Read More
A computation reuse framework, called CloudViews, to address the computation overlap problem in Microsoft’s SCOPE job service. Read More
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