Why Attend - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingWhy AttendExplore everything Data + AI Summit has to offer — from the latest innovations and technologies to thought-provoking panel discussions to networking opportunities where you can connect with other data professionals in your industry.Register nowSession informationWith 10 amazing tracks and over 20 technologies to learn about, get the right experience for your level.
 
Data Lakehouse ArchitectureThe 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 GovernanceData 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
Data SharingWhen 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
Data Engineering 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
Data Streaming 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
DSML: Production ML/MLOpsOvercome 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
DSML: ML Use Cases/TechnologiesMachine 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 Warehouses, BI and Visualization 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
ResearchIn 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.
Data StrategyChoosing 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 
Content tailored to your roleData + AI Summit has something for everyone — explore, learn and connect with us and the data, analytics and AI community.
 
 
Data EngineerIncrease efficiency, control costs and reduce data risks — all in a day’s work for today’s data engineer. Broaden your knowledge and expand your skills by learning from industry experts and your peers how to deliver high-quality, reliable data for every use case from BI to AI.
Explore sessionsData ScientistSuddenly, AI is everywhere and that’s putting pressure on data scientists and ML engineers to quickly deliver results. Learn how lakehouse and tools like MLflow can accelerate productionization of models, increase productivity, reduce risk and increase ROI on new models.
Explore sessionsData AnalystData analytics has moved beyond the warehouse. Join the global community of data analysts at Data + AI Summit and discover ways to support your stakeholders in making informed and timely decisions, achieve line of business KPIs and reduce rework risks.
Explore sessionsIT Decision MakerData and AI are now mission critical for every business. Learn how the lakehouse architecture unifies data, analytics and AI on a single platform for better performance, lower TCO and faster innovation.
Explore sessionsIndustry ExperiencesJoin and learn along with your industry peers in these specialized sessions focused on the unique challenges, use cases, and the data, analytics and AI requirements of your sector.
 
Financial ServicesLearn how to minimize risk, deliver superior customer experience and accelerate innovation on lakehouse.
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Government and Public SectorLearn how lakehouse is helping to unlock the full potential of data to deliver mission objectives and better serve citizens.
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Healthcare and Life SciencesLearn ways to accelerate research and improve patient outcomes on lakehouse.
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ManufacturingLearn how the lakehouse helps optimize supply chains, boost product innovation, increase operational efficiencies, predict fulfillment needs and reduce overall costs.
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Communications, Media & EntertainmentLearn ways to accelerate audience and advertiser outcomes — 360° view of audience, lower churn, increased ARPU — with lakehouse.
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Retail and Consumer GoodsLearn how lakehouse is helping to harness the full power of data so retailers, suppliers and partners can collaborate across the value chain.
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