Today, data engineering in modern enterprises has become increasingly more complex and resource-consuming, particularly because (1) the rich amount of organizational data is often distributed across data centers, cloud regions, or even cloud providers, and (2) the complexity of the big data stack has been quickly increasing over the past few years with an explosion in big-data analytics and machine-learning engines (like MapReduce, Hive, Spark, Presto, Tensorflow, PyTorch to name a few).
To address these challenges, it is critical to provide a single and logical namespace to federate different storage services, on-prem or cloud-native, to abstract away the data heterogeneity, while providing data locality to improve the computation performance. [Bin Fan] will share his observation and lessons learned in designing, architecting, and implementing such a system – Alluxio open-source project — since 2015.
Alluxio originated from UC Berkeley AMPLab (used to be called Tachyon) and was initially proposed as a daemon service to enable Spark to share RDDs across jobs for performance and fault tolerance. Today, it has become a general-purpose, high-performance, and highly available distributed file system to provide generic data service to abstract away complexity in data and I/O. Many companies and organizations today like Uber, Meta, Tencent, Tiktok, Shopee are using Alluxio in production, as a building block in their data platform to create a data abstraction and access layer. We will talk about the journey of this open source project, especially in its design challenges in tiered metadata storage (based on RocksDB), embedded state-replicate machine (based on RAFT) for HA, and evolution in RPC framework (based on gRPC) and etc.
Big Data Bellevue Meetup
May 19, 2022
Today, data engineering in modern enterprises has become increasingly more complex and resource-consuming, particularly because (1) the rich amount of organizational data is often distributed across data centers, cloud regions, or even cloud providers, and (2) the complexity of the big data stack has been quickly increasing over the past few years with an explosion in big-data analytics and machine-learning engines (like MapReduce, Hive, Spark, Presto, Tensorflow, PyTorch to name a few).
To address these challenges, it is critical to provide a single and logical namespace to federate different storage services, on-prem or cloud-native, to abstract away the data heterogeneity, while providing data locality to improve the computation performance. [Bin Fan] will share his observation and lessons learned in designing, architecting, and implementing such a system – Alluxio open-source project — since 2015.
Alluxio originated from UC Berkeley AMPLab (used to be called Tachyon) and was initially proposed as a daemon service to enable Spark to share RDDs across jobs for performance and fault tolerance. Today, it has become a general-purpose, high-performance, and highly available distributed file system to provide generic data service to abstract away complexity in data and I/O. Many companies and organizations today like Uber, Meta, Tencent, Tiktok, Shopee are using Alluxio in production, as a building block in their data platform to create a data abstraction and access layer. We will talk about the journey of this open source project, especially in its design challenges in tiered metadata storage (based on RocksDB), embedded state-replicate machine (based on RAFT) for HA, and evolution in RPC framework (based on gRPC) and etc.
Meetup Group
Big Data Bellevue: https://www.meetup.com/big-data-bellevue-bdb/
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Videos
Scaling experimentation in digital marketplaces is crucial for driving growth and enhancing user experiences. However, varied methodologies and a lack of experiment governance can hinder the impact of experimentation leading to inconsistent decision-making, inefficiencies, and missed opportunities for innovation.
At Poshmark, we developed a homegrown experimentation platform, Lightspeed, that allowed us to make reliable and confident reads on product changes, which led to a 10x growth in experiment velocity and positive business outcomes along the way.
This session will provide a deep dive into the best practices and lessons learned from successful implementations of large-scale experiments. We will explore the importance of experimentation, overcome scalability challenges, and gain insights into the frameworks and technologies that enable effective testing.
In the rapidly evolving world of e-commerce, visual search has become a game-changing technology. Poshmark, a leading fashion resale marketplace, has developed Posh Lens – an advanced visual search engine that revolutionizes how shoppers discover and purchase items.
Under the hood of Posh Lens lies Milvus, a vector database enabling efficient product search and recommendation across our vast catalog of over 150 million items. However, with such an extensive and growing dataset, maintaining high-performance search capabilities while scaling AI infrastructure presents significant challenges.
In this talk, Mahesh Pasupuleti shares:
- The architecture and strategies to scale Milvus effectively within the Posh Lens infrastructure
- Key considerations include optimizing vector indexing, managing data partitioning, and ensuring query efficiency amidst large-scale data growth
- Distributed computing principles and advanced indexing techniques to handle the complexity of Poshmark’s diverse product catalog
As machine learning and deep learning models grow in complexity, AI platform engineers and ML engineers face significant challenges with slow data loading and GPU utilization, often leading to costly investments in high-performance computing (HPC) storage. However, this approach can result in overspending without addressing the core issues of data bottlenecks and infrastructure complexity.
A better approach is adding a data caching layer between compute and storage, like Alluxio, which offers a cost-effective alternative through its innovative data caching strategy. In this webinar, Jingwen will explore how Alluxio's caching solutions optimize AI workloads for performance, user experience and cost-effectiveness.
What you will learn:
- The I/O bottlenecks that slow down data loading in model training
- How Alluxio's data caching strategy optimizes I/O performance for training and GPU utilization, and significantly reduces cloud API costs
- The architecture and key capabilities of Alluxio
- Using Rapid Alluxio Deployer to install Alluxio and run benchmarks in AWS in just 30 minutes