Interactive, Near Real Time analytics is usually a common requirement for many data teams across different fields.

In the field of web security, interactive analytics allows end users to get real time or historical insights about the state of their protected resource at any point of time and take actions accordingly.

One of the hardest aspects of enabling interactive, near-real-time analytics on a massive scale is a low response time. Scanning hundreds of Terabytes of data over a non-aggregated stream of events (a Delta Lake), and still returning an answer within just a few seconds can be a major challenge.

In this talk we will learn: • How did we build a 5PB Delta Lake of non-aggregated security events • What challenges did we see along the way - reducing delta log scan, improving cache affinity, reducing storage throttling errors etc. • How did we overcome them one by one

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The Presentation will cover a novel Demand Forecasting Solution for Intermittent Time-Series developed by Walmart which is currently used to make Granular Demand Predictions in Scale across Walmart Stores. The Solution alleviates the problem of forecasting for slow moving items; which are characterised by intermittency in time, rendering traditional statistical and time-series models ineffective in these scenarios. The Solution involves a Meta-Modelling Approach combining Linear Dynamic Systems and Deep Auto-Regressive Recurrent Networks which has been scaled up for accurate demand forecasts across ~35000 SKUs and ~250 Walmart Stores.

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Change in QSR state from static boards to dynamic and contextualized recommendation. The brain behind the system connects the Starbucks brand and culture with state-of-the-art AI techniques. Review some of the tactics and lessons learnt by running an RL algorithm and deep item collaborative filtering in production over a year.

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Despite being an emerging topic, MLOps is hard and there are no widely established approaches for MLOps. What makes it even harder is that in many companies the ownership of MLOps usually falls through the cracks between data science teams and production engineering teams. Data scientists are mostly focused on modeling the business problems and reasoning about data, features, and metrics, while the production engineers/ops are mostly focused on traditional DevOps for software development, ignoring ML-specific Ops like ML development cycles, experiment tracking, data/model validation, etc. In this talk, we will introduce MLflow Pipelines, an opinionated approach for MLOps. It provides predefined ML pipeline templates for common ML problems and opinionated development workflows to help data scientists bootstrap ML projects, accelerate model development, and ship production-grade code with little help from production engineers.

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The Spark UI and logs have useful information but also include sensitive data that need to be obfuscated.

To obfuscate the data, at Workday we have implemented methods for Apache Spark where the string representations for the TreeNode class can be configured to be obfuscated or non-obfuscated.To do this, we added a custom treenode printer for ui and a custom log4j appender which uses a list of rules based on class name/package name/log message regexes to decide whether to obfuscate third party libraries. In the Spark UI and in the logging, this results in the obfuscation of Spark Plans and column names.

In this talk we will go over the steps we have taken to implement the methods for obfuscation and show what it looks like in the Spark UI and logs. The methods shared have worked out well when deployed to production at workday, and other companies can also benefit from implementing these methods.

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Apache Parquet is a very popular columnar file format supported by Apache Spark. In a typical Spark job, scanning Parquet files is sometimes one of the most time consuming steps, as it incurs high CPU and IO overhead. Therefore, optimizing Parquet scan performance is crucial to job latency and cost efficiency.

Spark currently have two Parquet reader implementations: a vectorized one and a non-vectorized one. The former was implemented from scratch and offers much better performance than the latter. However, it currently doesn’t support complex types (e.g., array, list, map) at the moment and will fallback to the latter when encountering them. In addition to the reader implementation, predicate pushdown is also crucial to Parquet scan performance as it enables Spark to skip those data that do not satisfy the predicates, before the scan. Currently, Spark constructs predicates itself and rely on Parquet-MR to do the heavy lifting, which does the filtering based on various information such as statistics, dictionary, bloom filter or column index.

This talk will go through two recent improvements for Parquet scan performance: 1) vectorized read support for complex types, which allows Spark to achieve 10x+ improvement when reading Parquet data of complex types, and 2) Parquet column index support, which enables Spark to leverage Parquet column index feature during predicate pushdown. Last but not least, Chao go over some future work items that can further enhance Parquet read performance.

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Tredence' s OSA accelerator is a robust quick-start guide that is the foundation for a full Out of Stock or Supply Chain solution. The OSA solution focuses on driving sales through improved stock availability on the shelves. The following components make up the OSA accelerator.

• Identifying OOS Situation: ML models to identify the Out-Of-Stock scenario in a store at a SKU level taking in account the level of phantom inventory • Identifying Off-Sales Behavior: ML models to identify the off-sale behavior of a SKU in particular which is attributable to phantom inventory, stock less than presentation stock or improper operations within the store • Smart Alerts: Alert mechanism for the store manager and merchandizing reps in order to maintain healthy stock in the store and increase the revenue

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Apache Flink is one of the most popular frameworks for unified stream and batch processing. Like every other big data framework, Apache Flink offers connectors to different external systems to read from and write to. We refer to connectors for writing to external systems as sinks. Over the years, multiple frameworks existed inside Apache Flink for building sinks. The Apache Flink community also noticed the latest trend of ingesting real-time data directly into data lakes for further usage. Therefore with Apache Flink 1.15, we released the next iteration of our sink framework. We designed it to accommodate the needs of modern data lake connectors i.e. lazy file compaction, user-defined shuffling.

In this talk, we first give a brief historical glimpse of the evolution of the frameworks that started as a kind of a simple map operation until a custom operator model that simplified two-phase commit semantics. Secondly, we do a deep dive into Apache Flink’s fault tolerance model to explain how the last iteration of the sink framework supports exactly-once processing and complex operations important for delta lakes. In summary, this talk introduces the principles behind the sink framework in Apache Flink and gives a starting point for developers building a new connector for Apache Flink.

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This video is private.

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We've come a long way from the way data analysis used to be done. The emergence of the analytics engineering workflow, with dbt at its center, has helped usher in a new era of productivity. Not quite data engineering or data analysis, analytics engineering has enabled new levels of collaboration between two key sets of practitioners.

But that's not the only coming together happening right now. Enabled by the open lakehouse, the worlds of data analysis and AI/ML are also converging under a single roof, hinting at a new future of intertwined workloads and silo-free collaboration. It's a future that's tantalizing, and entirely within reach. Let's talk about making it happen.

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Almost all distributed computing systems cannot avoid data skew. If data skew is not dealt with, the long-tail task will seriously slow down the execution of the job, or even cause the failure of the job. In this talk, we will introduce how Spark AQE processes SkewedJoin and how we optimize the implementation based on workload in ByteDance. The main points are as follows: 1. Address the risks associated with increasing statistical accuracy to solve the problem of not being able to identify data skew. 2. Optimize the split logic of skew data to achieve a better optimization effect. 3. Compared to the community's implementation, more complex optimization scenarios are supported, which has basically covered all SkewedJoin scenarios. By February 2021, Spark AQE SkewedJoin optimization covers 13000+ Spark jobs per day in ByteDance. The average performance of optimized Spark jobs increased by 35%.

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Data + AI Summit 2022 was a great opportunity to check-in on the partnership between AWS and Databricks!

Data-centric AI is a growing movement which shifts the engineering focus in AI systems from the model to the data. However, Data-centric AI faces many open challenges, including measuring data quality, data iteration and engineering data as part of the ML project workflow, data management tools, crowdsourcing, data augmentation & data synthesis as well as responsible AI. This talk names the key pillars of Data-centric AI, identifies the trends in Data-centric AI movement, and sets a vision for taking ideas applied intuitively by a handful of experts and synthesizing them into tools that make the application systematic for all.

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Day 1 Afternoon Keynote | Data + AI Summit 2022 Supercharging our data architecture at Coinbase using Databricks Lakehouse | Eric Sun | Keynote Partner Connect & Ecosystem Strategy | Zaheera Valani What are ELT and CDC, and why are all the cool kids doing it? |George Fraser Analytics without Compromise | Francois Ajenstat Fireside Chat with Zhamak Dehghani and Arsalan Tavakoli

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Day 1 Morning Keynote | Data + AI Summit 2022 Welcome & "Destination Lakehouse" | Ali Ghodsi Apache Spark Community Update | Reynold Xin Streaming Lakehouse | Karthik Ramasamy Delta Lake | Michael Armbrust How Adobe migrated to a unified and open data Lakehouse to deliver personalization at unprecedented scale | Dave Weinstein Data Governance and Sharing on Lakehouse |Matei Zaharia Analytics Engineering and the Great Convergence | Tristan Handy Data Warehousing | Shant Hovespian Unlocking the power of data, AI & analytics: Amgen’s journey to the Lakehouse | Kerby Johnson

Get insights on how to launch a successful lakehouse architecture in Rise of the Data Lakehouse by Bill Inmon, the father of the data warehouse. Download the ebook: https://dbricks.co/3ER9Y0K

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The future of Financial Services is open with data and AI at its core. Welcome data teams and executives in Financial Services! This year’s Data + AI Summit is jam-packed with talks, demos and discussions on how Financial Services leaders are harnessing the power of data and analytics to digitally transform, minimize risk, accelerate time to market and drive sustainable value creation To help you take full advantage of the Financial Services industry experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

Financial Services Industry Forum: Our flagship event for Financial Services attendees at Summit featuring keynotes and panel discussions with ADP, Northwestern Mutual, Point72 Asset Management, S&P Global and EY, followed by networking. More details in the agenda below. Financial Services Lounge: Stop by our lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from our partners including Accenture, Avanade, Deloitte and others. Session Talks: Over 15 technical talks and demos on topics including hyper-personalization, AI-fueled forecasting, enterprise analytics in cloud, scaling privacy and cybersecurity, MLOps in cryptocurrency, ethical credit scoring and more.

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Join Zhamak Dehghani - creator of Data Mesh and Arsalan Tavakoli Co-founder and SVP Field Engineering of Databricks

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Welcome data teams and executives in the Healthcare and Life Sciences industry! This year’s Data + AI Summit is jam-packed with talks, demos and discussions on the biggest innovations in patient care and drug R&D. To help you take full advantage of the Healthcare and Life Sciences experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

Healthcare and Life Sciences Industry Forum: Our capstone event for Healthcare and Life Sciences attendees at Summit featuring keynotes and panel discussions with Walgreens, Takeda, Optum, and Humana followed by networking. More details in the agenda below. Healthcare and Life Sciences Lounge: Stop by our industry lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from our partners including ZS Associates, John Snow Labs and others. Session Talks: Over 10 technical talks on topics including healthcare NLP, knowledge graphs for R&D, commercial analytics, and predicting hospital readmissions.

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In this keynote talk, David Weinstein, VP of Engineering for Adobe Experience Cloud, will share Adobe’s journey from a simple data lake to a unified, open Lakehouse architecture with Databricks. Adobe can now deliver personalized experiences at scale to diverse customers with greater speed, operational efficiency and faster innovation across the Experience Cloud portfolio. Learn why they chose to migrate from Iceberg to Delta Lake to drive its open standard development and accelerate innovation of their Lakehouse, and they’ll also share how leveraging the Delta Lake table format has allowed for techniques to support change data capture and significantly improve operational efficiency.

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Welcome data teams and executives in the Manufacturing industry! This year’s Data + AI Summit is jam-packed with talks, demos and discussions on the biggest innovations around improving manufacturing operations, building agile supply chains and enabling an AI-driven business. To help you take full advantage of the Manufacturing experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

Manufacturing Industry Forum: Our capstone event for Manufacturing attendees at Summit featuring keynotes and panel discussions with John Deere, Honeywell and Collins Aerospace followed by networking. More details in the agenda below. Manufacturing Lounge:Stop by our lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from The Global Solution Integrator and Tredence. Session Talks: Insightful talks on predicting and preventing machine downtime, real-time process optimization and leveraging informational and operational technology data to make enterprise decisions.

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Welcome data teams and executives in Media and Entertainment! This year’s Data + AI Summit is jam-packed with talks, demos and discussions focused on how organizations are using data to personalize, monetize and innovate the audience experience. To help you take full advantage of the Communications, Media & Entertainment experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

Communications, Media & Entertainment Forum: Our capstone event for the industry at Summit featuring fireside chats and panel discussions with HBO, Warner Bros. Discovery, LaLiga, and Condé Nast followed by networking. More details in the agenda below. Industry Lounge: Stop by our lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from our partners including Cognizant, Fivetran, Labelbox, and Lovelytics. Session Talks: Over 10 technical talks on topics including Telecommunication Data Lake Management at AT&T, Data-driven Futbol Analysis from LaLiga, Improving Recommendations with Graph Neural Networks from Condé Nast, Tools for Assisted Spark Version Migrations at Netflix, Real-Time Cost Reduction Monitoring and Alerting with HuuugeGames and much more.

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“What does this mean? Who created it? How is it being used? Is it up to date?” Ever fielded these types of questions about your Databricks assets?

Today, context is a huge challenge for data teams. Everyone wants to use your company’s data, but often only a few experts know all of its tribal knowledge and context. The result — they get bombarded with endless questions and requests.

Atlan — the active metadata platform for modern data teams, recently named a Leader in The Forrester Wave: Enterprise Data Catalogs for DataOps — has launched an integration with Databricks Unity Catalog. By connecting to UC’s REST API, Atlan extracts metadata from Databricks clusters and workspaces, generates column-level lineage, and pairs it with metadata from the rest of your data assets to create true end-to-end lineage and visibility across your data stack.

In this session, Prukalpa Sankar (Co-Founder at Atlan and a lifelong data practitioner) and Todd Greenstein (Product Manager with Databricks) will do a live product demo to show how Atlan and Databricks work together to power modern data governance, cataloging, and collaboration.

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Apache Spark has become Pinterest’s dominant distributed batch processing framework, and Pinterest is migrating its Spark Platform and most production Spark jobs to Spark 3.

In this talk, we’ll share how Pinterest performed the Spark 3 version migration at scale. Moving to Spark 3 is a huge version upgrade that brings many incompatibilities and major differences compared with Spark 2. We’ll first introduce the motivation of the migration, then talk about the major challenges, approaches we took, how we handled different Spark job types during the migration, how we address the incompatibilities between Spark 2 and Spark 3, like Scala version support, and how we efficiently and safely migrated our existing production Spark jobs at scale without impacting stability & SLO with the help of Auto Migration Service (AMS). We’ll then further discuss our current performance improvements, cost saving, as well as the future plans and improvements that we’ll work on.

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Opening Production Machine Learning | Patrick Wendell MLflow 2.0 | Kasey Uhlenhuth | Keynotes Data + AI Summit 2022

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