The hybrid cloud model, where cloud resources run Spark or Presto jobs against data stored on-premises, is an appealing solution to reduce resource contention in on-premise environments while also saving in overall costs. One key flaw in a hybrid model is the overhead associated with transferring data between the two environments. Data and metadata locality within the compute application must be achieved in order to maintain the similar performance of analytics jobs as if the entire workload was run on-premises.
In this office hour, we demonstrate how a “zero-copy burst” solution helps to speed up Spark and Presto queries in the public cloud while eliminating the process of manually copying and synchronizing data from the on-premise data lake to cloud storage. This approach allows compute frameworks to decouple from on-premise data sources and scale efficiently by leveraging Alluxio and public cloud resources such as AWS.
We will cover:
- Typical challenges of moving data to the cloud and expanding compute capacity.
- Details about “zero-copy” hybrid cloud solution for burst computing
- A demo of running Presto analytic queries using remote on-prem HDFS data with Alluxio deployed in AWS EMR
The hybrid cloud model, where cloud resources run Spark or Presto jobs against data stored on-premises, is an appealing solution to reduce resource contention in on-premise environments while also saving in overall costs. One key flaw in a hybrid model is the overhead associated with transferring data between the two environments. Data and metadata locality within the compute application must be achieved in order to maintain the similar performance of analytics jobs as if the entire workload was run on-premises.
In this office hour, we demonstrate how a “zero-copy burst” solution helps to speed up Spark and Presto queries in the public cloud while eliminating the process of manually copying and synchronizing data from the on-premise data lake to cloud storage. This approach allows compute frameworks to decouple from on-premise data sources and scale efficiently by leveraging Alluxio and public cloud resources such as AWS.
We will cover:
- Typical challenges of moving data to the cloud and expanding compute capacity.
- Details about “zero-copy” hybrid cloud solution for burst computing
- A demo of running Presto analytic queries using remote on-prem HDFS data with Alluxio deployed in AWS EMR
Video:
Slides:
The hybrid cloud model, where cloud resources run Spark or Presto jobs against data stored on-premises, is an appealing solution to reduce resource contention in on-premise environments while also saving in overall costs. One key flaw in a hybrid model is the overhead associated with transferring data between the two environments. Data and metadata locality within the compute application must be achieved in order to maintain the similar performance of analytics jobs as if the entire workload was run on-premises.
In this office hour, we demonstrate how a “zero-copy burst” solution helps to speed up Spark and Presto queries in the public cloud while eliminating the process of manually copying and synchronizing data from the on-premise data lake to cloud storage. This approach allows compute frameworks to decouple from on-premise data sources and scale efficiently by leveraging Alluxio and public cloud resources such as AWS.
We will cover:
- Typical challenges of moving data to the cloud and expanding compute capacity.
- Details about “zero-copy” hybrid cloud solution for burst computing
- A demo of running Presto analytic queries using remote on-prem HDFS data with Alluxio deployed in AWS EMR
Videos:
Presentation Slides:
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Videos
Deepseek’s recent announcement of the Fire-flyer File System (3FS) has sparked excitement across the AI infra community, promising a breakthrough in how machine learning models access and process data.
In this webinar, an expert in distributed systems and AI infrastructure will take you inside Deepseek 3FS, the purpose-built file system for handling large files and high-bandwidth workloads. We’ll break down how 3FS optimizes data access and speeds up AI workloads as well as the design tradeoffs made to maximize throughput for AI workloads.
This webinar you’ll learn about how 3FS works under the hood, including:
✅ The system architecture
✅ Core software components
✅ Read/write flows
✅ Data distribution/placement algorithms
✅ Cluster/node management and disaster recovery
Whether you’re an AI researcher, ML engineer, or infrastructure architect, this deep dive will give you the technical insights you need to determine if 3FS is the right solution for you.