On-Demand Videos

As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for robust data infrastructures tailored for large language model (LLM) training and inference in the cloud. To effectively leverage the breakthroughs in LLM, organizations must ensure low latency, high concurrency, and scalability in production environments.

In this Alluxio-hosted webinar, Shouwei presented on the design and implementation of a distributed caching system that addresses the I/O challenges of LLM training and inference. He explored the unique requirements of data access patterns and offer practical best practices for optimizing the data pipeline through distributed caching in the cloud. The session featured insights from real-world examples, such as Microsoft, Tencent, and Zhihu, as well as from the open-source community. Watch this recording to get a deeper understanding of how to harness scalable, efficient, and robust data infrastructures for LLM training and inference.

Shawn Sun, Alluxio’s software engineer, shares how to get started with Alluxio on Kubernetes in April’s Product School Webinar.

To simplify the DevOps of the stack of Alluxio with a query engine, Alluxio has provided two ways to deploy on Kubernetes, helm and operator. They significantly simplify the deployment, configuration, and life cycle management of resources on Kubernetes.

Through this webinar, you will learn step-by-step how to deploy and run Alluxio on Kubernetes to accelerate analytics workloads.

In March’s Product School session, Beinan, an Alluxio tech lead, Presto committer, and Trino contributor, shares expert tips for tuning Trino performance. In addition, he demonstrates how to integrate Trino with Alluxio as a caching layer using connectors for Hive, Iceberg, Hudi, or Delta Lake.

In February’s product school, Greg Palmer, Lead Solution Engineer at Alluxio, will present a live demo featuring Transparent URI, a key feature in Alluxio Enterprise Edition which provides ease of integration of Alluxio with your existing data stack without any changes to the location metadata of the Hive Metastore. Join us to learn the configurations and other advanced settings for employing Transparent URI to simplify DevOps of Alluxio implementation, allowing users to access their existing storage systems without changing URIs at application level.

In November’s Product School, Adit Madan, Director of Product Management at Alluxio, will highlights new features, enhanced manageability, improved security and performance in Alluxio 2.9 release.

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.

Distributed systems are made up of many components such as authentication, a persistence layer, stateless services, load balancers, and stateful coordination services. These coordination services are central to the operation of the system, performing tasks such as maintaining system configuration state, ensuring service availability, name resolution, and storing other system metadata. Given their central role in the system it is essential that these systems remain available, fault tolerant and consistent. By providing a highly available file system-like abstraction as well as powerful recipes such as leader election, Apache Zookeeper is often used to implement these services. This talk will go over a generic example of stateful coordination service moving from Zookeeper to Raft.

Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access.

In October’s Product School, Alluxio’s Lead Solutions Engineer Greg Palmer will present and demo how Alluxio enables you to embrace the cloud migration strategy or multi-cloud architecture for large-scale analytics and AI workloads. Alluxio also helps scale out your platform adoption for analytics and AI across multiple tenants and applications teams.

In this presentation, Yingjun Wu, Founder @ RisingWave Labs will talk about the birth, the growth, and the prosperity of modern data stack. I will show you why modern data stack is more than a buzzword, and how it will possibly evolve in the next couple of years.

Apache Hudi’s open-source community is very active and healthy. In this talk, an overview of community-driven major features will be presented, followed by a deep-dive into two of those features, metastore and table management service, driven by Bytedance to illustrate Hudi’s platform vision.

Shopee is the leading e-commerce platform in SouthEast Asia. In this presentation, Luo Li from Shopee will share their Data Infra team’s recent project on acceleration with Presto and storage servitization. He will share the details on how Shopee leverages Alluxio to accelerate Presto query and provide standardized methods of accessing data through Alluxio-Fuse and Alluxio-S3.

OceanBase Database, is an open-source, distributed Hybrid Transactional/Real-time Operational Analytics (HTAP) database management system that has set new world records in both the TPC-C and TPC-H benchmark tests. OceanBase Database starts from 2010, and it has been serving all of the critical systems in Alipay. Besides Alipay, OceanBase has also been serving customer from a variety of sectors, including Internet, financial services, telecommunications and retail industry.

In this tech talk, we will talk about the architecture of OceanBase and some typical use cases. This talk will include some technical topic such as Paxos replication, 2PC commit, LSM-Tree like storage, SQL optimizer and executor, city-level disaster recovery, etc.