The data lake is a fantastic, low-cost place to put data at rest for offline analytics, but we've built it under the terms of a terrible bargain: all that cheap storage at scale was a great thing, but we gave up schema management and transactions along the way. Apache Iceberg has emerged as king of the Open Table Formats to fix this very problem.
Built on the foundation of Parquet files, Iceberg adds a simple yet flexible metadata layer and integration with standard data catalogs to provide robust schema support and ACID transactions to the once ungoverned data lake. In this talk, we'll build Iceberg up from the basics, see how the read and write path work, and explore how it supports streaming data sources like Apache Kafka™. Then we'll see how Confluent's Tableflow brings Kafka together with open table formats like Iceberg and Delta Lake to make operational data in Kafka topics instantly visible to the data lake without the usual ETL—unifying the operational/analytical divide that has been with us for decades.
The data lake is a fantastic, low-cost place to put data at rest for offline analytics, but we've built it under the terms of a terrible bargain: all that cheap storage at scale was a great thing, but we gave up schema management and transactions along the way. Apache Iceberg has emerged as king of the Open Table Formats to fix this very problem.
Built on the foundation of Parquet files, Iceberg adds a simple yet flexible metadata layer and integration with standard data catalogs to provide robust schema support and ACID transactions to the once ungoverned data lake. In this talk, we'll build Iceberg up from the basics, see how the read and write path work, and explore how it supports streaming data sources like Apache Kafka™. Then we'll see how Confluent's Tableflow brings Kafka together with open table formats like Iceberg and Delta Lake to make operational data in Kafka topics instantly visible to the data lake without the usual ETL—unifying the operational/analytical divide that has been with us for decades.
Videos:
Presentation Slides:
The data lake is a fantastic, low-cost place to put data at rest for offline analytics, but we've built it under the terms of a terrible bargain: all that cheap storage at scale was a great thing, but we gave up schema management and transactions along the way. Apache Iceberg has emerged as king of the Open Table Formats to fix this very problem.
Built on the foundation of Parquet files, Iceberg adds a simple yet flexible metadata layer and integration with standard data catalogs to provide robust schema support and ACID transactions to the once ungoverned data lake. In this talk, we'll build Iceberg up from the basics, see how the read and write path work, and explore how it supports streaming data sources like Apache Kafka™. Then we'll see how Confluent's Tableflow brings Kafka together with open table formats like Iceberg and Delta Lake to make operational data in Kafka topics instantly visible to the data lake without the usual ETL—unifying the operational/analytical divide that has been with us for decades.
Complete the form below to access the full overview:
.png)
Videos
Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
