Roy Hasson

Enterprise data is fragmented, and AI pays the price. See how Microsoft OneLake makes Apache Iceberg a first-class citizen, letting Fabric, Databricks, Snowflake, Clickhouse, Spark, and Trino read and write the same tables with no copies or ETL.

This hands on workshop walks through building a modern data architecture with shared storage, based on Iceberg. As data volumes increase and AI workloads proliferate, copying and ETL’ing data across systems is no longer sustainable.

While the industry has converged on Apache Iceberg as the standard for the open lakehouse, the engineering reality of building a high-scale Iceberg offering is fraught with complex trade-offs. This panel brings together engineering leaders to discuss the technical hurdles of supporting a truly open ecosystem.

Most Microsoft Fabric initiatives don’t fail because of dashboards or models, they stall when the underlying data isn’t trusted.Join us for building a production-ready master data management (MDM) solution natively in Microsoft Fabric.

Microsoft Fabric is the platform of choice for developers, engineers and ISV partners building the future with data and AI. While Fabric offers a range of native workloads like PowerBI, Spark, data warehousing and more, the Fabric Workload Hub additionally offers many new experiences like data cleansing, data wrangling, data quality and more.

Building a data platform is hard. You wade through hundreds of blogs, videos and whitepapers by vendors, engineers and social media influencers, and at the end you’re paralyzed, not sure where to start or what to build.

dbt and Airflow is a popular combination for creating and scheduling batch data modeling and transformation jobs that execute in a data warehouse like Snowflake. Presto users querying the data lake need a similar solution that is simple to use and makes it easy to ingest, model, transform and mainta

AWS Lake Formation is a service that allows data platform users to set up a secure data lake in days. Creating a data lake with Presto and Lake Formation is as simple as defining data sources and what data access and security policies you want to apply. At Ahana and Amazon, engineers are working on

Amazon Athena is a fully managed serverless service for querying and analyzing data in Amazon S3 using the power of Apache Presto.  In this session you will be introduced to Athena and learn how Agari, an email threat prevention and protection service, built a data processing and analytics platform

In this chalk talk, we discuss Amazon Athena’s support in enforcing AWS Lake Formation–based policies. Lake Formation¬¬–based policies enforce fine-grained access control on new or existing databases, tables, and columns defined in the AWS Glue Data Catalog for data stored in Amazon Simple Storage Service (Amazon S3).