欢迎关注大数据技术架构与案例微信公众号:过往记忆大数据
过往记忆博客公众号iteblog_hadoop
欢迎关注微信公众号:
过往记忆大数据

Data + AI Summit 2022 超清视频下载

Data + AI Summit 2022 于2022年06月27日至30日举行。本次会议是在旧金山进行,中国的小伙伴是可以在线收听的,一共为期四天,第一天是培训,后面几天才是正式会议。本次会议有超过200个议题,演讲嘉宾包括业界、研究和学术界的专家,本次会议主要分为六大块:

  • 数据分析, BI 以及可视化:了解最新的数据分析、BI 和可视化技术以及客户和社区的解决方案。
  • 数据工程:从实现数据管道到管理数据质量、ETL和数据质量框架再到数据 ops,深入了解最新的数据工程知识。
  • Data Lakes, Data Warehouses and Data Lakehouses:了解数据湖和数据仓库演变为 Data Lakehouses 背后的概念和最佳实践;
  • 数据科学, 机器学习以及 MLOps:了解关于生产数据科学和机器学习管道的技术和最佳实践。
  • 数据安全和治理:
  • 学术研究:致力于学术和先进的工业研究领域,包括大规模调度程序,图表,数据分析和机器学习系统。

会议的全部日程请参见:https://databricks.com/dataaisummit/agenda

Data + AI Summit 2022” class=
如果想及时了解Spark、Hadoop或者HBase相关的文章,欢迎关注微信公众号:过往记忆大数据

本次会议的第一天 KeyNote 宣布了几件重要的事情:Apache Spark 后续发展、下一代 Structured Streaming 解决方案、Delta Lake 的功能全部开源。除了第一天的 KeyNote,下面几个议题也推荐大家看看:

  • Apache Spark SQL Aggregate Improvement at Meta (Facebook)
  • Recent Parquet Improvements in Apache Spark
  • Spark Data Source V2 Performance Improvement: Aggregate Push Down
  • Deep Dive into the New Features of Apache Spark 3.2 and 3.3
  • Managing Straggler Executors at Apache Spark 3.3
  • Apache Spark on Kubernetes—Lessons Learned from Launching Millions of Spark Executors
  • PySpark in Apache Spark 3.3 and Beyond
  • Delta Lake 2.0 Overview
  • Improving Interactive Querying Experience on Spark SQL
  • Moving from Apache Spark 2 to Apache Spark 3: Spark Version Upgrade at Scale in Pinterest
  • Radical Speed on the Lakehouses: Photon under the hood

超清视频下载途径

考虑到大家可能对不同的主题感兴趣,这里给大家整理了所有可以下载的视频,全部是超清,大家可以根据自己的兴趣去下载观看。另外,会议的 PPT 当前还不可以下载,需要 PPT 的同学可以继续关注本公众号,获取相关消息。

关注微信公众号 过往记忆大数据 或者 Java与大数据架构 并回复 10187 获取 Data + AI Summit 2022 超清视频。

可下载视频的议题

本次可下载视频的议题共 197 个。

  • A Low-Code Approach to 10x Data Engineering
  • A Modern Approach to Big Data for Finance
  • A Practitioner's Guide to Unity Catalog—A Technical Deep Dive
  • Accelerating Hybrid Data Mesh Implementation
  • Accidentally Building a Petabyte-Scale Cybersecurity Data Mesh in Azure With Delta Lake at HSBC
  • Administrator Best Practices and Tips for Future-proofing your Databricks Account
  • Advanced Migrations From Hive to SparkSQL
  • Adversarial Drifts Model Monitoring and Feedback Loops Building Human-in-the-Loop Machine Learning Systems for Content Moderation
  • An Advanced S3 Connector for Spark to Hunt for Cyber Attacks
  • Analyzing Population Health using Healthcare Claims
  • Apache Arrow Flight SQL: High Performance, Simplicity, and Interoperability for Data Transfers
  • Apache Spark SQL Aggregate Improvement at Meta Facebook
  • Apache Spark on Kubernetes—Lessons Learned from Launching Millions of Spark Executors
  • Automate Your Delta Lake or Practical Insights on Building Distributed Data Mesh
  • Automating Model Lifecycle Orchestration with Jenkins
  • Backfill Streaming Data Pipelines in Kappa Architecture
  • Batches Streams and Everything in between Unifying Batch and Stream Storage with Apache Pulsar and Lakehouse Architectures
  • Beyond Daily Batch Processing Operational Trade-Offs of Microbatch Incremental and Real-Time Processing for Your ETLs (and Your Team's Sanity)
  • Beyond Monitoring The Rise of Data Observability
  • Build an Enterprise Lakehouse for Free with Trino and Delta Lake
  • Building Enterprise Scale Data and Analytics Platforms at Amgen
  • Building Metadata and Lineage Driven Pipelines on Kubernetes
  • Building Production-Ready Recommender Systems with Feature Stores
  • Building Scalable & Advanced AI based Language Solutions for R&D using Databricks
  • Building Spatial Applications with Apache Spark and CARTO
  • Building a Lakehouse for Data Science at DoorDash
  • Building a Lakehouse on AWS for Less with AWS Graviton and Photon
  • Building an Operational Machine Learning Organization from Zero and Leveraging ML for Crypto Security
  • Building and Scaling Machine Learning-Based Products in the World's Largest Brewery
  • Chaos Engineering in the World of Large-Scale Complex Data Flow
  • Cloud Native Geospatial Analytics at JLL
  • Cloud and Data Science Modernization of Veterans Affairs Financial Service Center with Azure Databricks
  • Complete Data Security and Governance Powered by Unity Catalog and Immuta
  • Connecting the Dots with DataHub Lakehouse and Beyond
  • Constraints, Democratization, and the Modern Data Stack - Building a Data Platform At Red Ventures with Fivetran and Databricks
  • Coral and Transport Portable SQL and UDFs for the Interoperability of Spark and Other Engines
  • Correlation Over Causation Cracking the Relationship Between User Engagement and User Happiness
  • Cutting the Edge in Fighting Cybercrime Reverse-Engineering a Search Language to Cross-Compile it to PySpark
  • DELETE UPDATE MERGE Operations in Data Source V2
  • Data Lakehouse and Data Mesh—Two Sides of the Same Coin
  • Data Mesh Implementation Patterns
  • Data Warehousing on the Lakehouse
  • DataFusion and Arrow: Supercharge Your Data Analytical Tool with a Rusty Query Engine
  • Databricks Lakehouse Overview
  • Databricks SQL Under the Hood: What's New with Live Demos
  • Day 1 Afternoon Keynote
  • Day 2 Afternoon Keynote
  • Day 2 Opening Keynote
  • Deep Dive How to Build Your Modern Data Stack on Databricks to Solve Modern Problems
  • Deep Dive into the New Features of Apache Spark 3.2 and 3.3
  • Deliver Faster Decision Intelligence From Your Lakehouse
  • Delta Lake, the Foundation of Your Lakehouse
  • Delta Live Tables Modern software engineering and management for ETL
  • Delta Sharing - A New Paradigm for Secure Data Sharing and Data Collaboration on Lakehouse
  • Delta Sharing for Healthcare and Life Sciences
  • Democratizing Metrics at Airbnb
  • Designing Better MLOps Systems
  • Destination Lakehouse All Your Data Analytics and AI on One Platform
  • Distributed Machine Learning at Lyft
  • Dive Deeper into Data Engineering on Databricks
  • Doubling the Capacity of the Data Platform Without Doubling the Cost
  • Driving Real-Time Data Capture and Transformation in Delta Lake with Change Data Capture
  • Efficient and Multi-Tenant Scheduling of Big Data and AI Workloads
  • Eliminating AI Risk—One Model Failure at a Time
  • Emerging Data Architectures & Approaches for Real-Time AI using Redis
  • Enable Production ML with Databricks Feature Store
  • Enabling Advanced Analytics at The Department of State using Databricks
  • Enabling BI in a Lakehouse Environment How Spark and Delta Can Help With Automating a DWH Development
  • Enabling Business Users to Perform Interactive Ad-Hoc Analysis over Delta Lake with No Code
  • Ensuring Correct Distributed Writes to Delta Lake in Rust with Formal Verification
  • Entity Resolution
  • Evolution of Data Architectures and How to Build a Lakehouse
  • Financial Services Industry Forum: The Future of Financial Services is Open with Data and AI at Its Core
  • Fugue Tune Distributed Hybrid Hyperparameter Tuning
  • FugueSQL—The Enhanced SQL Interface for Pandas and Spark DataFrames
  • FutureMetrics Using Deep Learning to Create a Multivariate Time Series Forecasting Platform for Economic Strategic Planning
  • Gamer User Toxicity
  • Gazelle-Jni: A Middle Layer to Offload Spark SQL to Native Engines for Execution Acceleration
  • Government Industry Forum Lunch and Program
  • Hassle-Free Data Ingestion into the Lakehouse
  • Healthcare Data Interoperability
  • How AARP Services Inc. automated SAS transformation to Databricks using LeapLogic—A cloud accelerator for transformation of legacy analytics ETL DW & Hadoop
  • How AT&T Data Science Team Solved an Insurmountable Big Data Challenge on Databricks with Two Different Approaches using Photon and RAPIDS Accelerator for Apache Spark
  • How Databricks is driving disruptive digital transformation in the airline industry
  • How EPRI Uses Computer Vision to Mitigate Wildfire Risks for Electric Utilities
  • How McAfee Leverages Databricks on AWS at Scale
  • How Robinhood Built a Streaming Lakehouse to Bring Data Freshness from 24h to Less Than 15 Mins
  • How To Make Apache Spark on Kubernetes Run Reliably on Spot Instances
  • How To Use Databricks SQL for Analytics on Your Lakehouse
  • How to Implement a Semantic Layer for Your Lakehouse
  • How unsupervised machine learning can scale data quality monitoring in Databricks
  • Immuta - Unlocking sensitive use cases with automated data access
  • Implementing a Framework for Data Security and Policy at a Large Public Sector Agency
  • Implementing an End-to-End Demand Forecasting Solution Through Databricks and MLflow
  • Improving Apache Spark Structured Streaming Application Processing Time by Configurations Code Optimizations and Custom Data Source
  • Improving Interactive Querying Experience on Spark SQL
  • Introducing Zipline An Open Source Feature Engineering Platform
  • Introduction to Flux and OSS Replication
  • Lakehouse with Delta Lake Deep Dive
  • Laying the Foundation for Claims Automation
  • Learn to Efficiently Test ETL Pipelines
  • Lessons Learned from Deidentifying 700 Million Patient Notes
  • Leveraging ML-Powered Analytics for Rapid Insights and Action a demonstration
  • Live Analytics: The next user engagement frontier
  • Low-Code Machine Learning on Databricks with AutoML
  • ML on the Lakehouse Bringing Data and ML Together to Accelerate AI Use Cases
  • MLOps at DoorDash
  • MLflow Pipelines Accelerating MLOps from Development to Production
  • Managing Straggler Executors at Apache Spark 3.3
  • Meetup Women in Data and AI
  • Meshing About with Databricks
  • Migrate Your Existing DAGs to Databricks Workflows
  • Migrate and Modernize your Data Platform with Confluent and Databricks
  • Migrating Complex SAS Processes to Databricks - Case Study
  • Migrating SAS to a Lakehouse on Databricks and S3
  • Monitoring and Quality Assurance of Complex ML Deployments via Assertions
  • More Context Less Chaos How Atlan and Unity Catalog Power Column-Level Lineage and Active Metadata
  • Mosaic: A Framework for Geospatial Analytics at Scale
  • Moving from Apache Spark 2 to Apache Spark 3 Spark Version Upgrade at Scale in Pinterest
  • Multi-Touch Attribution
  • Multimodal Deep Learning Applied to E-commerce Big Data
  • Near Real-Time Analytics with Event Streaming Live Tables and Delta Sharing
  • Nixtla: Deep Learning for Time Series Forecasting
  • Opening the Floodgates Enabling Fast Unmediated End User Access to Trillion-Row Datasets with SQL Data Warehouses
  • Operational Analytics: Expanding the Reach of Data in the Lakehouse Era
  • Optimizing Speed and Scale of User-Facing Analytics Using Apache Kafka and Pinot
  • Orchestration Made Easy with Databricks Workflows
  • OvalEdge End-To-End Data Governance
  • Patient Cohort Building with NLP and Knowledge Graphs
  • Powering Up the Business with a Lakehouse
  • Practical Data Governance in a Large Scale Databricks Environment
  • Predicting Repeat Admissions to Substance Abuse Treatment with Machine Learning
  • Predicting and Preventing Machine Downtime with AI and Expert Alerts
  • Propensity Scoring Demo
  • Protecting Personally Identifiable Information (PII)/PHI Data in Data Lake via Column Level Encryption
  • Pushing the limits of scale and performance for enterprise-wide analytics: A fire-side chat with Akamai
  • PySpark in Apache Spark 3.3 and Beyond
  • Radical Speed on the Lakehouse Photon Under the Hood
  • Real Time Bidding
  • Real Time Retail Demo
  • Real World Evidence and Propensity Score Matching
  • Real-Time Search and Recommendation at Scale Using Embeddings and Hopsworks
  • Real-time Risk Management with Confluent & Databricks
  • Realize the Promise of Streaming with the Databricks Lakehouse Platform
  • Recent Parquet Improvements in Apache Spark
  • Regulatory Reporting: Automatically translate enterprise data models into efficient data pipelines
  • Retail Industry Forum
  • Rethinking Orchestration as Reconciliation Software-Defined Assets in Dagster
  • Running a Low Cost Versatile Data Management Ecosystem with Apache Spark at Core
  • SAS Migration
  • Scaling AI Workloads with the Ray Ecosystem
  • Scaling Deep Learning on Databricks
  • Scaling ML at CashApp with Tecton
  • Scaling Salesforce In-Memory Streaming Analytics Platform for Trillion Events Per Day
  • Scaling Your Workloads with Databricks Serverless
  • Search and Aggregations Made Easy with OpenSearch and NodeJS
  • Serverless Kafka and Apache Spark in a Multi-Cloud Data Lakehouse Architecture
  • Serving Near Real-Time Features at Scale
  • Simplifying Migrations to Lakehouse—the Databricks Way
  • Sink Framework Evolution in Apache Flink
  • Smart Manufacturing Real-time Process Optimization with Databricks
  • So Fresh and So Clean: Learn How to Build Real-Time Warehouses on Lakehouse
  • Sound Data Engineering in Rust—From Bits to DataFrames
  • Spark Inception: Exploiting the Apache Spark REPL to Build Streaming Notebooks
  • Stadium Analytics
  • Streaming Data into Delta Lake with Rust and Kafka
  • Streaming ML Enrichment Framework Using Advanced Delta Table Features
  • Supercharge your SaaS applications with a modern cloud-native database
  • Survey of Production ML Tech Stacks
  • Tackling Challenges of Distributed Deep Learning with Open Source Solutions
  • Take Databricks Lakehouse to the Max with Informatica
  • Technical and Tactical Football Analysis Through Data
  • The Databricks Notebook Front Door of the Lakehouse
  • The Future is Open - a Look at Google Cloud’s Open Data Ecosystem
  • The Future of Data - What’s Next with Google Cloud
  • The Road to a Robust Data Lake Utilizing Delta Lake and Databricks to Map 150 Million Miles of Roads a Month
  • Tools for Assisted Apache Spark Version Migrations From 2.1 to 3.2+
  • Towards Dynamic Microstructure The Role of Machine Learning in the Next Generation of Exchanges
  • Tredence On Shelf Availability
  • Turbocharge your AI/ML Databricks workflows with Precisely
  • Turning Fan Data Into an Asset
  • Unifying Data Science and Business Artificial Intelligence Augmentation and Integration into Production Business Applications
  • What to Do When Your Job Goes OOM in the Night Flowcharts
  • Why a Data Lakehouse is Critical During the Manufacturing Apocalypse
  • You Have BI. Now What Activate Your Data
  • Your fastest path to Lakehouse and beyond
  • dbt + Machine Learning What Makes a Great Baton Pass
  • dbt and Databricks: Analytics Engineering on the Lakehouse
本博客文章除特别声明,全部都是原创!
原创文章版权归过往记忆大数据(过往记忆)所有,未经许可不得转载。
本文链接: 【Data + AI Summit 2022 超清视频下载】(https://www.iteblog.com/archives/10187.html)
喜欢 (1)
分享 (0)
发表我的评论
取消评论

表情
本博客评论系统带有自动识别垃圾评论功能,请写一些有意义的评论,谢谢!