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GTC ON-DEMAND

Presentation
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Abstract:
Well present BlazingSQL, RAPIDS open source SQL engine. BlazingSQL eliminates the need to build and deploy a database, enabling users to fully integrate high-performance SQL into their RAPIDS workflows.Its built entirely on the GPU Apache Arrow standard that underpins the RAPIDS ecosystem and the primitives underneath the cuDF and cuIO libraries. BlazingSQL supports a myriad of data sources. Users can query Apache Parquet and JSON in a data lake with in-memory data sources like Apache Arrow or Pandas in a single, intuitive, SQL query that feeds machine learning, deep learning, or graph workloads. We'll launch and run a series of BlazingSQL workloads distributed on a multi-GPU cluster.
Well present BlazingSQL, RAPIDS open source SQL engine. BlazingSQL eliminates the need to build and deploy a database, enabling users to fully integrate high-performance SQL into their RAPIDS workflows.Its built entirely on the GPU Apache Arrow standard that underpins the RAPIDS ecosystem and the primitives underneath the cuDF and cuIO libraries. BlazingSQL supports a myriad of data sources. Users can query Apache Parquet and JSON in a data lake with in-memory data sources like Apache Arrow or Pandas in a single, intuitive, SQL query that feeds machine learning, deep learning, or graph workloads. We'll launch and run a series of BlazingSQL workloads distributed on a multi-GPU cluster.  Back
 
Topics:
Accelerated Data Science
Type:
Talk
Event:
GTC Washington D.C.
Year:
2019
Session ID:
DC91406
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Abstract:

Learn about BlazingSQL, our new, free GPU SQL engine built on RAPIDS open-source software. We will show multiple demo workflows using BlazingSQL to connect data lakes to RAPIDS tools. We'll explain how we dramatically accelerated our engine and made it substantially more lightweight by integrating Apache Arrow into GPU memory and cuDF into RAPIDS. That made it easy to install and deploy BlazingSQL + RAPIDS in a matter of minutes. More importantly, we built a robust framework to help users bring data from data lakes into GPU-Accelerated workloads without having to ETL on CPU memory or separate GPU clusters. We'll discuss how that makes it possible to keep everything in the GPU while BlazingSQL manages the SQL ETL. RAPIDS can then take these results to continue machine learning, deep learning, and visualization workloads.

Learn about BlazingSQL, our new, free GPU SQL engine built on RAPIDS open-source software. We will show multiple demo workflows using BlazingSQL to connect data lakes to RAPIDS tools. We'll explain how we dramatically accelerated our engine and made it substantially more lightweight by integrating Apache Arrow into GPU memory and cuDF into RAPIDS. That made it easy to install and deploy BlazingSQL + RAPIDS in a matter of minutes. More importantly, we built a robust framework to help users bring data from data lakes into GPU-Accelerated workloads without having to ETL on CPU memory or separate GPU clusters. We'll discuss how that makes it possible to keep everything in the GPU while BlazingSQL manages the SQL ETL. RAPIDS can then take these results to continue machine learning, deep learning, and visualization workloads.

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Topics:
Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9798
Streaming:
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Abstract:

Extract analytical value out of your enterprise data lake with a state-of-the-art GPU SQL analytics engine. As businesses continue to consolidate massive datasets into data lake technologies (HDFS, AWS S3, Azure Blob, etc.), they find themselves unable to fully leverage the value these lakes hold. Data engineering departments need to produce unique, costly ETL processes for every dataset and every tool which hopes to interact with said dataset. At BlazingDB we've built an analytics engine that runs SQL directly on open source file formats inside data lakes, currently BlazingDB's Simpatico and Apache Parquet. These file formats can be easily accessed from a variety of different tools, limit duplication of large volumes of data, and support improved data governance. Learn strong practices for ensuring your data lake doesn't turn into a swamp and how to extract the full value of your data lake investment.

Extract analytical value out of your enterprise data lake with a state-of-the-art GPU SQL analytics engine. As businesses continue to consolidate massive datasets into data lake technologies (HDFS, AWS S3, Azure Blob, etc.), they find themselves unable to fully leverage the value these lakes hold. Data engineering departments need to produce unique, costly ETL processes for every dataset and every tool which hopes to interact with said dataset. At BlazingDB we've built an analytics engine that runs SQL directly on open source file formats inside data lakes, currently BlazingDB's Simpatico and Apache Parquet. These file formats can be easily accessed from a variety of different tools, limit duplication of large volumes of data, and support improved data governance. Learn strong practices for ensuring your data lake doesn't turn into a swamp and how to extract the full value of your data lake investment.

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Topics:
Accelerated Data Science, 5G & Edge, AI Startup, Finance
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8484
Streaming:
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