Data Warehousing with Spark Streaming at Zalando

BigQuery is Google Cloud Platform's interactive big data service that allows users to analyze massive datasets in seconds using SQL-like queries. It offers a scalable and fast way to query terabytes of data without the expense of maintaining servers or databases. BigQuery organizes data into a project-dataset-table hierarchy and uses a distributed architecture to efficiently process queries across servers.

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Looker Studio Data Contracts - Data.Monks.pptx

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The document discusses implementing data contracts and governance for Looker Studio assets like data sources, blends, segments and filters. It notes that as a Looker Studio footprint grows organically, it can become chaotic without proper ownership, auditability, governance and consistency. The document proposes creating self-documenting assets with human and machine-readable data contracts that describe data sources, dimensions, metrics and other details. It provides templates for summary and data source contracts that can be populated from sheets. The goal is to apply structure, rigor and governance to data visualization through recording assets in a library, auditing creation and access, and managing maintenance.

Data Lakehouse Symposium | Day 4

The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.

Demystifying data engineering

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Slides for the talk at AI in Production meetup: https://www.meetup.com/LearnDataScience/events/255723555/ Abstract: Demystifying Data Engineering With recent progress in the fields of big data analytics and machine learning, Data Engineering is an emerging discipline which is not well-defined and often poorly understood. In this talk, we aim to explain Data Engineering, its role in Data Science, the difference between a Data Scientist and a Data Engineer, the role of a Data Engineer and common concepts as well as commonly misunderstood ones found in Data Engineering. Toward the end of the talk, we will examine a typical Data Analytics system architecture.

Big query

Tanvi Parikh

Google BigQuery is Google's fully managed big data analytics service that allows users to analyze very large datasets. It offers a fast and easy to use service with no infrastructure to manage. Developers can stream up to 100,000 rows of data per second for near real-time analysis. BigQuery bills users per project on a pay-as-you-go model, with the first 1TB of data processed each month free of charge.

Set your Data in Motion with Confluent & Apache Kafka Tech Talk Series LME

Airbyte @ Airflow Summit - The new modern data stack

Michel Tricot

The document introduces the modern data stack of Airbyte, Airflow, and dbt. It discusses how ELT addresses issues with traditional ETL processes by separating extraction, loading, and transformation. Extraction and loading involve general-purpose routines to pull and push raw data, while transformation uses business logic specific to the organization. The stack is presented as an open solution that allows composing with best of breed tools for each part of the data pipeline. Airbyte provides data integration, dbt enables data transformation with SQL, and Airflow handles scheduling. The demo shows how these tools can be combined to build a flexible, autonomous, and future proof modern data stack.

Modern Data Flow

The document discusses principles of modern data flow, including: - Streaming to enable real-time data processing - Decentralized data pipelines and data streaming as the central nervous system - Declarative APIs and SQL for data in motion to simplify development - Developer-oriented approaches with code and open platforms at the forefront - Governance including catalogs for discovery, schemas as APIs, and lineage for observability The principles are positioned as an evolution from older batch, centralized, infrastructure-heavy approaches to real-time, distributed, developer-centric data streaming architectures. Governance is discussed as important for enabling innovation while maintaining security and control.

Apache Kafka and the Data Mesh | Michael Noll, Confluent

HostedbyConfluent

Data mesh is a relatively recent term that describes a set of principles that good modern data systems uphold. A kind of “microservices” for the data-centric world. While the data mesh is not technology-specific as a pattern, the building of systems that adopt and implement data mesh principles have a relatively long history under different guises. In this talk, we share our recommendations and picks of what every developer should know about building a streaming data mesh with Kafka. We introduce the four principles of the data mesh: domain-driven decentralization, data as a product, self-service data platform, and federated governance. We then cover topics such as the differences between working with event streams versus centralized approaches and highlight the key characteristics that make streams a great fit for implementing a mesh, such as their ability to capture both real-time and historical data. We’ll examine how to onboard data from existing systems into a mesh, modelling the communication within the mesh, how to deal with changes to your domain’s “public” data, give examples of global standards for governance, and discuss the importance of taking a product-centric view on data sources and the data sets they share.

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How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services? Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business. Join Hortonworks and Informatica as we discuss: - What is a data lake? - The modern data architecture for a data lake - How Hadoop fits into the modern data architecture - Innovative use-cases for a data lake

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Hyperspace is a recently open-sourced (https://github.com/microsoft/hyperspace) indexing sub-system from Microsoft. The key idea behind Hyperspace is simple: Users specify the indexes they want to build. Hyperspace builds these indexes using Apache Spark, and maintains metadata in its write-ahead log that is stored in the data lake. At runtime, Hyperspace automatically selects the best index to use for a given query without requiring users to rewrite their queries. Since Hyperspace was introduced, one of the most popular asks from the Spark community was indexing support for Delta Lake. In this talk, we present our experiences in designing and implementing Hyperspace support for Delta Lake and how it can be used for accelerating queries over Delta tables. We will cover the necessary foundations behind Delta Lake’s transaction log design and how Hyperspace enables indexing support that seamlessly works with the former’s time travel queries.

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Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.

Breaking the Monolith

VMware Tanzu

How do you grapple with a legacy portfolio? What strategies do you employ to get an application to cloud native? How do you grapple with a legacy portfolio? What strategies do you employ to get an application to cloud native? This talk will cover tools, process and techniques for decomposing monolithic applications to Cloud Native applications running on Pivotal Cloud Foundry (PCF). The webinar will build on ideas from seminal works in this area: Working Effectively With Legacy Code and The Mikado Method. We will begin with an overview of the technology constraints of porting existing applications to the cloud, sharing approaches to migrate applications to PCF. Architects & Developers will come away from this webinar with prescriptive replatforming and decomposition techniques. These techniques offer a scientific approach for an application migration funnel and how to implement patterns like Anti-Corruption Layer, Strangler, Backends For Frontend, Seams etc., plus recipes and tools to refactor and replatform enterprise apps to the cloud. Go beyond the 12 factors and see WHY Cloud Foundry is the best place to run any app - cloud native or non-cloud native. Speakers: Pieter Humphrey, Principal Product Manager; Pivotal Rohit Kelapure, PCF Advisory Solutions Architect; Pivotal Hungry for more? Check out this blog from Kenny Bastani: http://www.kennybastani.com/2016/08/strangling-legacy-microservices-spring-cloud.html

Apache Kafka 0.8 basic training - Verisign

Michael Noll

Apache Kafka 0.8 basic training (120 slides) covering: 1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka 2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers 3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning 4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps 5. Playing with Kafka using Wirbelsturm Audience: developers, operations, architects Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/ Verisign is a global leader in domain names and internet security. Tools mentioned: - Wirbelsturm (https://github.com/miguno/wirbelsturm) - kafka-storm-starter (https://github.com/miguno/kafka-storm-starter) Blog post at: http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/ Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!

Data Product Architectures

Benjamin Bengfort

This document discusses data product architectures and provides examples of different architectures for data products, including the lambda architecture, analyst architecture, recommender architecture, and partisan discourse architecture. It also discusses common design principles for data product architectures, such as using microservices with stateful backend services and database-backed APIs. Key aspects of data product architectures include handling training data and models, making predictions via APIs, updating models and annotations, and designing flexible systems that can incorporate new models and data.

Journey of Building Streaming Data Pipelines - BED-Con 2019

This document discusses the journey of building streaming data pipelines at Zalando. It describes how Zalando has transitioned from a centralized data warehouse to using Spark streaming to process data in real-time from multiple sources like Kafka. Spark streaming allows for easy processing of unstructured data at scale with low latency. The document also covers best practices like using Scala instead of SQL for streaming ETL, persisting state data, integrating multiple streaming applications, and using Databricks Delta as the data lake storage format.

TDWI Schweiz 2019 - Building Streaming Data Warehouses

Zalandos AI-driven products and distributed landscape of analytical data marts cannot wait for long-running, hard-to-recover, monolithic batch jobs taking all night to calculate already outdated data. Modern data integration pipelines need to deliver fast and easy to consume data sets in high quality. Based on Spark Streaming and Delta, the central data warehousing team was able to deliver widely-used master data as S3 or Kafka streams and snapshots at the same time. The talk will cover challenges in our fashion data platform and a detailed architectural deep dive about separation of integration from enrichment, providing streams as well as snapshots and feeding the data to distributed data marts. Finally, lessons learned and best practices about Delta's MERGE command, Scala API vs Spark SQL and schema evolution give more insights and guidance for similar use cases.

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Lambda Architecture in the Cloud with Azure Databricks with Andrei Varanovich

The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising. In this talk we demonstrate the blueprint for such an implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component. We go back to some core principles of functional programming and link them to the capabilities of Apache Spark for various end-to-end big data analytics scenarios. We also illustrate the “Lambda architecture in use” and the associated tread-offs using the real customer scenario – Rijksmuseum in Amsterdam – a terabyte-scale Azure-based data platform handles data from 2.500.000 visitors per year.

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1. SAP's BI strategy can be confusing but focuses on cloud-first, with on-premise solutions still supported and hybrid models available for customers not ready to move fully to the cloud. 2. SAP Analytics Cloud and SAP Data Warehouse Cloud were positioned as a great match, with the latter providing an enterprise-grade cloud data warehouse. 3. On-premise solutions like BW/HANA will continue to be supported, and hybrid scenarios combining on-premise and cloud capabilities are seen as an important way to transition customers to the cloud.

Databricks + Snowflake: Catalyzing Data and AI Initiatives

"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"

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This document summarizes Walmart's transition to building an enterprise data platform on Azure Databricks to enable machine learning and data science at scale. Previously, Walmart had a complex and slow legacy technology stack. The new platform goals were to centralize data in the cloud, increase productivity with data science tools, and reduce costs. Key aspects of the new platform included using Azure and Databricks for data processing and machine learning, Airflow for orchestration, and building several machine learning models for applications like fraud detection and product recommendations. Challenges in the transition included optimizing performance and managing resources across the platforms.

Successful AI/ML Projects with End-to-End Cloud Data Engineering

Trusted, high-quality data and efficient use of data engineers’ time are critical success factors for AI/ML projects. Enterprise data is complex—it comes from several sources, in a variety of formats, and at varied speeds. For your machine learning projects on Apache Spark, you need a holistic approach to data engineering: finding & discovering, ingesting & integrating, server-less processing at scale, and data governance. Stop by this session for an overview on how to set up AI/ML projects for success while Informatica takes the heavy lifting out of your data engineering.

ETL Made Easy with Azure Data Factory and Azure Databricks

This document summarizes Mark Kromer's presentation on using Azure Data Factory and Azure Databricks for ETL. It discusses using ADF for nightly data loads, slowly changing dimensions, and loading star schemas into data warehouses. It also covers using ADF for data science scenarios with data lakes. The presentation describes ADF mapping data flows for code-free data transformations at scale in the cloud without needing expertise in Spark, Scala, Python or Java. It highlights how mapping data flows allow users to focus on business logic and data transformations through an expression language and provides debugging and monitoring of data flows.

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Spotfire

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The document discusses TIBCO Spotfire, an analytics platform. It shows how Spotfire connects various clients to data sources via servers. It provides visualizations, analytic engines, and automation services. Spotfire Application Data Services connects Spotfire to enterprise systems like SAP, Siebel, and Oracle by introspecting their data models and delivering the data using SQL. The rest of the document focuses on how Spotfire connects specifically to SAP Business Warehouse (BW) data, discussing the challenges of differing data structures and query languages between Spotfire and BW, and how Spotfire's adapter generates optimized queries and allows unified access to BW data in Spotfire.

Snowflake for Data Engineering

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Hybrid Integration with SAP

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This document discusses hybrid integration with SAP using Azure services. It demonstrates preparing the SAP and BizTalk environments for integration, including configuring the RFC client and property schema. The document shows connecting to SAP traditionally using RFC and IDOC and executing BAPI calls. It then discusses using Azure services like Service Bus Relay and BizTalk Services for integration. Finally, it proposes developing microservices using BizTalk features to enable integration between SAP and other systems in a modern way.

Managing data analytics in a hybrid cloud

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Managing Data Analytics in a Hybrid Cloud discusses challenges with traditional analytics approaches and proposes using shared data lakes with dynamic compute clusters. Common challenges include explosive analytics team growth leading to resource contention, and duplicating large datasets for each cluster. The proposed approach uses shared object storage to hold unified datasets accessed by multiple ephemeral analytics clusters provisioned on-demand. This allows teams independent resources while avoiding duplicate storage costs and improving agility. The document outlines example architectures and benefits of this shared data lake approach when implemented on a private or public cloud.

DataStax: Making a Difference with Smart Analytics

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Most enterprises jumping onto the Big Data Analytics bandwagon are trying to build an hammer and then look for nails to hit. Unfortunately offerings from competing vendors within the analytics space encourages the one-size-fits-all mindset. This talk drills into how to use DSE Analytics to create smart analytically-driven applications that delight customers and deliver the information needed by enterprises to make smart business decisions.

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The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.

Data Lakehouse Symposium | Day 1 | Part 1

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

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In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.

Democratizing Data Quality Through a Centralized Platform

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

Learn to Use Databricks for Data Science

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

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Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

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Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.

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In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Scaling your Data Pipelines with Apache Spark on Kubernetes

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Sawtooth Windows for Feature Aggregations

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Re-imagine Data Monitoring with whylogs and Spark

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Raven: End-to-end Optimization of ML Prediction Queries

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Processing Large Datasets for ADAS Applications using Apache Spark

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Massive Data Processing in Adobe Using Delta Lake

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Machine Learning CI/CD for Email Attack Detection