Instacart has gone through immense growth during the pandemic and the trend continues. Instacart ads is no exception in this growth story. We have launched many new product lines including display and video ads covering the full advertising funnel to address the increasing demand of our retail partners. We have built advanced models to auto-suggest optimal bidding to increase the ROI for our CPG partners. Advertisers’ trust is the utmost priority and thus the quest to build a top-class ads measurement platform.

Ads data processing requires complex data verifications to update ads serving stats. In ETL pipelines these were implemented through files containing thousands of lines of raw SQL which were hard to scale, test, and iterate upon. Our data engineers used to spend hours testing small changes due to a lack of local testing mechanisms. These pain points stress our need for better tools. After some research, we chose Apache Spark™ as our preferred tool to rebuild ETLs, and the Databricks platform made this move easier. In this session, We'll share our journey to move our pipelines to Spark and Delta Lake on Databricks. With Spark, Scala, and Delta we solved many problems which were slowing the team’s productivity. Some key areas that will be covered include:

• Modular and composable code
• Unit testing framework
• Incremental event processing with spark structured streaming
• Granular resource tuning for better performance and cost efficacy

Other than the domain business logic, the problems discussed here are quite common for performing data processing at scale. We hope that sharing our learnings will benefit others who are going through similar growth challenges or migrating to Lakehouse.

Talk by: Devlina Das and Arthur Li

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A client recently asked to optimize their batch and streaming workloads. It happened to be aggregations using DataFrame.groupby operation with a custom Scala UDAF over a data stream from Kafka. Just a single simple-looking request that turned itself up into a a-few-month-long hunt to find a more performant query execution planning than ObjectHashAggregateExec that kept falling back to a sort-based aggregation (i.e., the worst possible aggregation runtime performance). It quickly taught us that an aggregation using a custom Scala UDAF cannot be planned other than ObjectHashAggregateExec but at least tasks don't always have to fall back. And that's just batch workloads. When you throw in streaming semantics and think of the different output modes, windowing and streaming watermark optimizing aggregation can take a long time to do right.

Talk by: Jacek Laskowski

Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

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Unity Catalog provides fully automated data lineage for all workloads in SQL, R, Python, Scala and across all asset types at Databricks. The aggregated view has been available to end users through data explorer and API. In this session, we are excited to share that lineage is available via delta table in their UC metastore. It stores full history of recent lineage records and it is near real time. Additionally, customers can query it through standard SQL interface. With that, customers can get significant operational insights about their workload for impact analysis, troubleshooting, quality assurance, data discovery, and data governance.

Together with the system table platform effort, which provides query history, job run operational data, audit logs and more, lineage table will be a critical piece to link all the data asset and entity asset together, providing better lakehouse observability and unification to customers.

Talk by: Menglei Sun

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Let’s face it -- data is messy. And your company’s business requirements? Even messier. You’re staring at your screen, knowing there is a tool that will let you give your business partners the information they need as quickly as they need it. There’s even a Python version of it now. But…it looks kind of scary. You’ve never used it before, and you don’t know where to start. Yes, we’re talking about the dreaded flatMapGroupsWithState. But fear not - we’ve got you covered.

In this session, we’ll take a real-word use case and use it to show you how to break down flatMapGroupsWithState into its basic building blocks. We’ll explain each piece in both Scala and the newly-released Python, and at the end we’ll illustrate how it all comes together to enable the implementation of arbitrary stateful operations with Spark Structured Streaming.

Talk by: Angela Chu

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Traditional cybersecurity Security Information and Event Management (SIEM) ways do not scale well for data sources with 30TiB per day, leading HSBC to create a Cybersecurity Lakehouse with Delta and Spark. Creating a platform to overcome several conventional technical constraints, the limitation in the amount of data for long-term analytics available in traditional platforms and query languages is difficult to scale and time-consuming to run. In this talk, we’ll learn how to implement (or actually reverse-engineer) a language with Scala and translate it into what Apache Spark understands, the Catalyst engine. We’ll guide you through the technical journey of building equivalents of a query language into Spark. We’ll learn how HSBC business benefited from this cutting-edge innovation, like decreasing time and resources for Cyber data processing migration, improving Cyber threat Incident Response, and fast onboarding of HSBC Cyber Analysts on Spark with Cybersecurity Lakehouse platform.

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Join Scott Haines (Databricks Beacon) as he teaches you to write your own Notebook style service (like Jupyter / Zeppelin / Databricks) for both fun (and profit?). Cause haven't we all just been a little curious how Notebook environments work? From the outside things probably seem magical, however just below the surface there is a literal world of possibilities waiting to be exploited (both figuratively and literally) to assist in the building of unimaginable new creations. Curiosity is of course the foundation for creativity and novel ideation, and when armed with the knowledge you'll pick up in this session, you'll have gained an additional perspective and way of thinking (mental model) for solving complex problems using dynamic procedural (on-the-fly) code compilation.

Did I mention you'll use Spark Structured Streaming in order to generate a "live" communication channel between your Notebook service and the "outside world"?

Overview During this session you'll learn to build your own Notebook-style service on top of Apache Spark & the Scala ILoop. Along the way, you'll uncover how to harness the SparkContext to manage, drive, and scale your own procedurally defined Apache Spark applications by mixing core configuration and other "magic". As we move through the steps necessary to achieve this end result, you'll learn to run individual paragraphs, or the entire synchronous waterfall of paragraphs, leading to the dynamic generation of applications.

Deep dive into the world of possibilities that fork from a solid understanding of procedurally generated, on-the-fly, code compilation (live injection), the security ramifications (cause of course this is unsafe!), but come away with a new mental model focused on architecting composite applications, or auto-generated

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This talk will look at the current state of tools to automate library and language upgrades in Python and Scala and apply them to upgrading to new version of Apache Spark. After doing a very informal survey, it seems that many users are stuck on no longer supported versions of Spark, so this talk will expand on the first attempt at automating upgrades (2.4 - 3.0) to explore the problem all the way back to 2.1.

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dbt has done a great job of building an elegant, common interface between data engineers and data analysts: uniting on SQL. As the data industry evolves, there's plenty of pain and room to grow in building that interface between data scientists and data analysts. There isn't a good answer for when things go wrong in the machine learning arena: should the data analyst own fine-tuning the pre-processing data(think: prepping transformed data even more for machine learning models to better work with the data). Should we increase the SQL surface area to build ML models or should we leave that to non-SQL interfaces(python/scala/etc.)? Does this have to be an either/or future? Whatever the interface evolves into, it must center people, create a low bar and high ceiling, and focus on outcomes and not the mystique of features/tools behind a learning curve.

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After three years of hard work by the Delta community, we are proud to announce the release of Delta Lake 2.0. Completing the work to open-source all of Delta Lake while tens of thousands of organizations were running in production was no small feat and we have the ever-expanding Delta community to thank! Join this session to learn about how the wider Delta community collaborated together to bring these features and integrations together.

Join this session to learn about how the wider Delta community collaborated together to bring these features and integrations together. This includes the Integrations with Apache Spark™, Apache Flink, Apache Pulsar, Presto, Trino, and more.

Features such as OPTIMIZE ZORDER, data skipping using column stats, S3 multi-cluster writes, Change Data Feed, and more.

Language APIs including Rust, Python, Ruby, GoLang, Scala, and Java.

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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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In recent years, latest privacy laws & regulations bring a fundamental shift in the protection of data and privacy, placing new challenges to data applications. To resolve these privacy & security challenges in big data ecosystem without impacting existing applications, several hardware TEE (Trusted Execution Environment) solutions have been proposed for Apache Spark, e.g., PySpark with Scone and Opaque etc. However, to the best of our knowledge, none of them provide full protection to data pipelines in Spark applications. An adversary may still get sensitive information from unprotected components and stages. Furthermore, some of them greatly narrowed supported applications, e.g., only support SparkSQL. In this presentation, we will present a new PPMLA (privacy preserving machine learning and analytics) solution built on top of Apache Spark, BigDL, Occlum and Intel SGX. It ensures all spark components and pipelines are fully protected by Intel SGX, and existing Spark applications written in Scala, Java or Python can be migrated into our platform without any code change. We will demonstrate how to build distributed end-to-end SparkML/SparkSQL workloads with our solution on untrusted cloud environment and share real-world use cases for PPMLA.

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