Why Attend

The decisions you make about your core data architecture will affect the reliability, performance and utility of your data analytics, data science and machine learning. Increasingly, many organizations are adopting the data lakehouse to improve their data platform. Has the ability to more easily combine structured, unstructured and real-time data workloads created new opportunities for data teams? Has ACID reliability on data lakes simplified how data is used? In this track, get answers to these questions and find out how to adopt a data lakehouse, migrate from data lakes and data warehouses, and integrate lakehouses with other data platforms.

Technologies/topic ideas: lakehouse architecture, Delta Lake, Photon, platform security and privacy, serverless, cost management, data warehouse, data lake, Apache Iceberg, Data Mesh

Data governance, security and compliance are critical — they help guarantee that all data, BI and ML assets are maintained and managed securely across the enterprise and you’re compliant with regulatory frameworks. In this track, learn best practices, frameworks, processes, roles, policies and standards for data governance of structured and unstructured data across clouds.

Technologies/topic ideas: data governance, multicloud, Unity Catalog, security, compliance, privacy

When enterprises can easily exchange data with their customers, partners, suppliers and internal lines of business, they can better collaborate, innovate and unlock value from that data. In this track, discover best practices for sharing data across data platforms and clouds, ways to avoid replication and lock-in, and how to distribute data products through marketplaces.

Technologies/topic ideas: sharing and collaboration, Delta Sharing, data cleanliness, data cleanrooms, data marketplaces

If you want to optimize data processing and reduce costs, this session is for you. You’ll learn how to use a combination of systems and processes to ingest, orchestrate and transform raw data for analytics and machine learning. Dive into best practices for data architectures, software engineering, ETL, data management, data quality, DataOps and orchestration.

Technologies/topic ideas: data pipelines, orchestration, CDC, medallion architecture, Delta Live Tables, dbt, Databricks Workflows, ETL/ELT, DataOps, Parquet, Apache Spark internals

Today’s organization needs to respond in real time as events unfold. In this track, learn how data streaming can help you drive faster decision-making, make more accurate predictions and improve customer experiences. Explore best practices for implementing real-time data pipelines with your favorite tools and languages, find out how to reduce complexity for real-time data workflows and eliminate silos to support all your real-time use cases.

Technologies/topic ideas: Apache Spark Structured Streaming, real-time ingestion, real-time ETL, real-time ML, real-time analytics, real-time applications, Delta Live Tables

Overcome the challenges of putting ML projects into production and operationalizing them at scale. In this track, you’ll find out how to scale ML in production and apply MLOps best practices across the end-to-end machine learning lifecycle. 

Technologies/topic areas: MLOps, Feature Stores, organizational ML, MLflow, Model Serving and more

Machine learning continues to disrupt industries and accelerate business outcomes across use cases and industries. In this track, discover how to apply ML to solve business challenges, explore technologies and best practices, and learn how to integrate data science with the rest of your organization. 

Technologies/topic areas: PyTorch, TensorFlow, Keras, XGBoost, fast.ai, scikit-learn, Python and R ecosystems, deep learning, notebooks, LLMs and more

Data analytics is a vital component of business decision-making. In this track, find out how to build analytics pipelines and integrations and learn about tooling and infrastructure for SQL analytics, BI and visualization.  

Technologies/topic ideas: ANSI SQL, Redash, Databricks SQL, Tableau, Power BI, visualization techniques, Spark SQL and DataFrames, data integration, data warehouse and analytics

In this track — dedicated to academic and advanced industrial research — explore large-scale data analytics and machine learning systems, the hardware that powers them (GPUs, I/O storage devices, etc.) and how these systems are applied to use cases like genomics, astronomy, image scanning, disease detection, vaccine research and more.

Choosing a data lakehouse platform is only the first step in implementing your data strategy. Success requires a thoughtful approach to people and processes. In this track, you’ll find out how to align goals, identify the right use cases, organize and enable teams, mitigate risk and operate at scale so you can be even more successful with data, analytics and AI.

Technologies/topic ideas: data management, data governance, strategy, data teams, data mesh, data democratization