This session will provide a comprehensive overview of Iceberg's journey, its current role within the data ecosystem, and the promising future it holds with the integration of Polaris (incubating). We will discuss how these technologies redefine table formats and catalog management, empowering organizations to efficiently manage and analyze large-scale data. Attendees will gain valuable insights into the evolving landscape, ensuring they remain at the forefront of innovation and continue to shape thought leadership in the data ecosystem.

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Legacy data platforms often fall short of the performance, processing and scaling requirements for robust AI/ML initiatives. This is especially true in complex multi-cloud (public, private, edge, airgapped) environments. The combined power of MinIO and Dremio creates a data lakehouse platform that overcomes these challenges, delivering scalability, performance and efficiency to ensure successful AI initiatives. Watch Brenna Buuck, Sr. Technical Evangelist at MinIO and Alex Merced, Sr. Technical Evangelist at Dremio provided insights on: - AI Workflows: How a data lakehouse simplifies critical AI tasks like model training, refinement, feature selection and real-time inference for faster decisions - Scalability and Performance: How a data lakehouse architecture scales seamlessly to meet the fast-growing demands of AI applications - Data Management Efficiency: How a data lakehouse streamlines data management for IT teams, allowing them to focus on innovation

Dremio unveiled new features in our latest release that enhance the creation, performance, and management of Apache Iceberg data lakehouses. You will learn how Dremio delivers market-leading SQL query and write performance, improved federated query security and management as well as streamlined data ingestion by delivering: - Live Reflections on Iceberg tables that will accelerate performance, ensure up-to-date data and reduce management overhead. - Result Set Caching that can accelerate query performance up to 28X - Merge-on-Read that can enhance write and ingestion speed - Auto Ingest Pipes that eliminate complex pipeline setup and maintenance - User Impersonation for federated queries that allows for granular permissions, better access control, and user workload tracking

Organizations want to empower teams with data at their fingertips and in every part of their business. They want their teams to move quickly with data never as a bottleneck, but an accelerant to decision making —all without the curiosity tax too common in consumption-based cloud platforms. Dremio enables data teams to unify all of their disparate data, from Snowflake to Iceberg and other sources, by combining an intelligent semantic layer with a powerful SQL platform that eliminates silos, optimizes costs through intelligent query acceleration, and enables self-service analytics. You will how Dremio enables Snowflake users to: - Unify all of your data from Snowflake and all sources - Optimize analytics costs and performance - Use easy self-service analytics for faster time-to-insight - Ensure Apache Iceberg native compatibility for future-proof data access

Learn how to master semantic layers with Dremio. We will provide a high-level overview of their purpose in modern analytics, showing how they act as a bridge between complex data sources and business users. You’ll learn how semantic layers simplify data access, ensure consistency, and empower users to derive meaningful insights from data, regardless of their technical expertise. - The definition and core purpose of a semantic layer in data analytics: How it acts as a bridge between complex data and business users, simplifying data access and interpretation. - Key benefits and use cases of semantic layers: How they enable self-service analytics, ensure data consistency, and accelerate time-to-insight. - How Dremio's semantic layer technology can transform your data strategy: Dremio makes it easier to manage and leverage your data for faster, data-driven decision-making.

Watch Vishnu Vardhan, Director of Product Management StorageGRID at NetApp and Alex Merced,Senior Technical Evangelist at Dremio, as they explore the future of data lakes and discover how NetApp and Dremio can revolutionize your analytics by delivering the next generation of lakehouse with Apache Iceberg. Transitioning to a modern data lakehouse environment allows organizations to increase business insight, reduce management complexity, and lower overall TCO of their analytics environments. The growing adoption of Apache Iceberg is a key enabler for building the next generation lakehouse. Its robust feature set, coupled with an open ecosystem for analytics use cases, including ACID transactions, time travel, and schema evolution, continues to drive rapid adoption. Vishnu and Alex will delve into market trends surrounding Iceberg, as well as key drivers for lakehouse adoption and modernization. You will learn about: - Iceberg adoption trends - NetApp StorageGRID and its benefits - The Dremio and NetApp data lakehouse solution - Key Iceberg data lakehouse modernization use cases - Customer examples

Watch and learn about Apache Iceberg. A 10 part web series designed to help you master Apache Iceberg. https://hello.dremio.com/webcast-an-apache-iceberg-lakehouse-crash-course-reg.html?utm_medium=social-free&utm_source=youtube&utm_content=webcast-gdw-se-the-architecture-of-apache-iceberg-apache-hudi-and-delta-lake-intro&utm_campaign=webcast-gdw-se-the-architecture-of-apache-iceberg-apache-hudi-and-delta-lake-intro The "An Apache Iceberg Lakehouse Crash Course" an in-depth webinar series designed to provide a comprehensive understanding of Apache Iceberg and its pivotal role in modern data lakehouse architectures. Over the course of ten sessions, you'll explore a wide range of topics: - foundational concepts like data lakehouses - table formats to advanced features such as partitioning, optimization, and streaming with Apache Iceberg - Each session will offer detailed insights into the architecture and capabilities of Apache Iceberg, alongside practical demonstrations of data ingestion using tools like Apache Spark and Dremio.