Virtualizing Apache Spark with Justin Murray

This session will examine the many options the data scientist has for running Spark clusters in public and private clouds. We will discuss various environments employing AWS, Mesos, containers, docker, and BlueData EPIC technologies and the benefits and challenges of each. Speakers: Tom Phelan, Co-founder and Chief Architect - BlueData Inc. Tom has spent the last 25 years as a senior architect, developer, and team lead in the computer software industry in Silicon Valley. Prior to co-founding BlueData, Tom spent 10 years at VMware as a senior architect and team lead in the core R&D Storage and Availability group. Most recently, Tom led one of the key projects – vFlash, focusing on integration of server-based Flash into the vSphere core hypervisor. Prior to VMware, Tom was part of the early team at Silicon Graphics that developed XFS, one of the most successful open source file systems. Earlier in his career, he was a key member of the Stratus team that ported the Unix operating system to their highly available computing platform. Tom received his Computer Science degree from the University of California, Berkeley.

12.07.2017 Docker Meetup - POSTGRE SQL ON KUBERNETES

Zalando adtech lab

February 2016 HUG: Running Spark Clusters in Containers with Docker

Yahoo Developer Network

Register to see webinar: http://go.scylladb.com/wbn-spark-scylla-registration.html Spark has become the de-facto analytics tool for data stored in Scylla. In this webinar we will review different workloads using Spark and Scylla, for example Extract, Transform, Load (ETL), creating joins between tables and summaries and reporting. We will also cover data modeling best practices for Scylla-Spark use cases and different deployment scenarios. To conclude, we will share performance tuning settings to utilize both Scylla and Spark at peak performance. Join us to learn... Why using Spark with Scylla is advantageous for analytics workloads How to create reporting using Spark and Scylla Best practices for data modeling and performance tuning for Scylla and Spark

Spark Powered by Scylla

Audience Level Intermediate Synopsis In this presentation, Shunde will show you how to simplify the migration process with a workload migration engine, making the move to OpenStack easy. This talk will address the various difficulties operators and administrators face when migrating workloads and resources between various cloud platforms, including removing time consuming, repetitive and complicated steps. This tool can be applied to many cloud migrations, including between Virtual Machines and OpenStack, between Public and Private clouds, as well as between OpenStack and OpenStack. This tool integrates completely with other OpenStack projects minimising deployment and maintenance efforts. So whether you’re looking to upgrade from your existing traditional virtualisation platform, setup a new OpenStack instance, or upgrade to a newer version of OpenStack, we will show you how to simplify this process using GUTS. Speaker Bio Shunde is a senior software developer in Aptira with over 15 years experience in software development, automation and system administration. He has worked with OpenStack since the Diablo cycle and has been involved in projects from OpenStack infrastructure to distributed systems running on top of OpenStack.

Simplifying the Move to OpenStack

OpenStack

Kubernetes is a fast growing open-source platform which provides container-centric infrastructure. Conceived by Google in 2014, and leveraging over a decade of experience running containers at scale internally, it is one of the fastest moving projects on GitHub with 1000+ contributors and 40,000+ commits. Kubernetes has first class support on Google Cloud Platform, Amazon Web Services, and Microsoft Azure. Unlike YARN, Kubernetes started as a general purpose orchestration framework with a focus on serving jobs. Support for long-running, data intensive batch workloads required some careful design decisions. Engineers across several organizations have been working on Kubernetes support as a cluster scheduler backend within Spark. During this process, we encountered several challenges in translating Spark considerations into idiomatic Kubernetes constructs. In this talk, we describe the challenges and the ways in which we solved them. This talk will be technical and is aimed at people who are looking to run Spark effectively on their clusters. The talk assumes basic familiarity with cluster orchestration and containers.

Apache Spark on Kubernetes Anirudh Ramanathan and Tim Chen

Web後端技術的演變

inwin stack

Virtualizing Apache Spark and Machine Learning with Justin Murray

Technical breakout during Confluent’s streaming event in Munich, presented by Sam Julian, Chief Cloud Engineer at E.On SE. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.

Kafka Deployment to Steel Thread

confluent

State of the Container Ecosystem

Vinay Rao

AKS

girish goudar

DUG'20: 10 - Storage Orchestration for Composable Storage Architectures

Andrey Kudryavtsev

Storage os kubernetes clusters need persistent data

LibbySchulze

Introducing Kubestr - A New Way to Explore Your Kubernetes Storage Options

LibbySchulze

Scylla Summit 2018: Scylla 3.0 and Beyond

Scylla Summit 2016: Scylla at Samsung SDS

I'm going to cover something which could be seen as essential for Cassandra but which hasn't gotten much attention in the Cassandra community and literature. It's schema migrations--how you go about pushing out and versioning changes to your keyspace and table definitions across environments. This is an area that has established solutions in the relational database world, with tools like Liquibase(http://www.liquibase.org/) and Flyway (http://flywaydb.org/) and in web frameworks like Rails and Grails. I'll explain the different types of migrations but then focus, for most of the talk, on schema migrations. I'll explain how schema migrations have been done in the Cassandra community and the roadblocks teams have faced trying to use Liquibase and Flyway to manage Cassandra migrations. Then I'll share an elegant, lightweight schema migrations system that we at GridPoint built on top of Flyway. I'll use our system as a context for discussing schema migration best practices for Cassandra and the various choices teams have for their migrations and table definitions, including when NOT to use a tool like Flyway. I'll also touch on the other types of migrations besides keyspace and table definitions that can be versioned and driven off source control.

Managing (Schema) Migrations in Cassandra

DataStax Academy

When low latency (P99) and high performance are core requirements, what NoSQL database attributes should you consider, and what tradeoffs are key? While we live in a world of multi-CPU, multi-core servers capable of storing tens of terabytes of data, if your database isn’t architected to take advantage of this, you’re being penalized on performance or cost. Join this webinar to learn about the critical elements for a high-performance, low-latency NoSQL database. ScyllaDB’s engineers will discuss how they addressed core database performance challenges, including the pros and cons of each, and provide a detailed explanation of the architectural principles they applied to achieve their performance objectives. We’ll take a deep dive into the strategies applied to: Achieve precise control over I/O and compute-intensive workloads Avoid locks and contention on the CPU level Bypass kernel bottlenecks Squeeze the most out of modern multi-core hardware Satisfy SLAs while maintaining system stability

Critical Attributes for a High-Performance, Low-Latency Database

Building and deploying an analytic service on Cloud is a challenge. A bigger challenge is to maintain the service. In a world where users are gravitating towards a model where cluster instances are to be provisioned on the fly, in order for these to be used for analytics or other purposes, and then to have these cluster instances shut down when the jobs get done, the relevance of containers and container orchestration is more important than ever. Container orchestrators like Kubernetes can be used to deploy and distribute modules quickly, easily, and reliably. The intent of this talk is to share the experience of building such a service and deploying it on a Kubernetes cluster. In this talk, we will discuss all the requirements which an enterprise grade Hadoop/Spark cluster running on containers bring in for a container orchestrator. This talk will cover in details how Kubernetes orchestrator can be used to meet all our needs of resource management, scheduling, networking, and network isolation, volume management, etc. We will discuss how we have replaced our home grown container orchestrator with Kubernetes which used to manage the container lifecycle and manage resources in accordance to our requirements. We will also discuss the feature list as container orchestrator which is helping us deploy and patch 1000s of containers and also a list which we believe need improvement or can be enhanced in a container orchestrator. Speaker Rachit Arora, SSE, IBM

Persist your data in an ephemeral k8 ecosystem

LibbySchulze

Cisco: Cassandra adoption on Cisco UCS & OpenStack

DataStax Academy

Mais procurados (20)

Scylla Virtual Workshop 2020

12.07.2017 Docker Meetup - POSTGRE SQL ON KUBERNETES

February 2016 HUG: Running Spark Clusters in Containers with Docker

Spark Powered by Scylla

Simplifying the Move to OpenStack

Apache Spark on Kubernetes Anirudh Ramanathan and Tim Chen

Web後端技術的演變

Virtualizing Apache Spark and Machine Learning with Justin Murray

Kafka Deployment to Steel Thread

State of the Container Ecosystem

AKS

DUG'20: 10 - Storage Orchestration for Composable Storage Architectures

Storage os kubernetes clusters need persistent data

Introducing Kubestr - A New Way to Explore Your Kubernetes Storage Options

Scylla Summit 2018: Scylla 3.0 and Beyond

Scylla Summit 2016: Scylla at Samsung SDS

Managing (Schema) Migrations in Cassandra

Critical Attributes for a High-Performance, Low-Latency Database

Persist your data in an ephemeral k8 ecosystem

Cisco: Cassandra adoption on Cisco UCS & OpenStack

Semelhante a Virtualizing Apache Spark with Justin Murray

Why Kubernetes as a container orchestrator is a right choice for running spar...

DataWorks Summit

This introductory workshop is aimed at data analysts & data engineers new to Apache Spark and exposes them how to analyze big data with Spark SQL and DataFrames. In this partly instructor-led and self-paced labs, we will cover Spark concepts and you’ll do labs for Spark SQL and DataFrames in Databricks Community Edition. Toward the end, you’ll get a glimpse into newly minted Databricks Developer Certification for Apache Spark: what to expect & how to prepare for it. * Apache Spark Basics & Architecture * Spark SQL * DataFrames * Brief Overview of Databricks Certified Developer for Apache Spark

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

Yao Yao Mooyoung Lee https://github.com/yaowser/learn-spark/tree/master/Final%20project https://www.youtube.com/watch?v=IVMbSDS4q3A https://www.academia.edu/35646386/Teaching_Apache_Spark_Demonstrations_on_the_Databricks_Cloud_Platform https://www.slideshare.net/YaoYao44/teaching-apache-spark-demonstrations-on-the-databricks-cloud-platform-86063070/ Apache Spark is a fast and general engine for big data analytics processing with libraries for SQL, streaming, and advanced analytics Cloud Computing, Structured Streaming, Unified Analytics Integration, End-to-End Applications

Teaching Apache Spark: Demonstrations on the Databricks Cloud Platform

Yao Yao

Big Telco - Yousun Jeong

Spark Summit

Big Telco Real-Time Network Analytics

Yousun Jeong

Simplify and Boost Spark 3 Deployments with Hypervisor-Native Kubernetes

In-Memory Computing frameworks such as Spark are gaining tremendous popularity for Big Data processing as their in-memory primitives make it possible to eliminate disk I/O bottleneck. Logically, the more available memory they have, the better performance they can achieve. However, unpredicted GC activity from on-heap memory management, high cost for serialization/de-serialization (SerDe), and burst temporary object creation/destruction greatly impacts their performance and scale-out ability. For example in Spark, when the volume of datasets are much larger than the system memory volume, SerDe makes significant impact on almost every in-memory computing steps such as caching, checkpoint, shuffling/dispatching, data loading and Storing. With fast growing advanced server platform with significant increased non-volatile memory such as Intel 3D Xpoint technology powered NVMe and Fast SSD Array Storage, how to best use various hybrid memory-like resources from DRAM to NVMe/SSD determines Big Data applications performance and scalability. In this presentation, we will first introduce our non-volatile generic Java object programming model for In-Memory Computing. This programming model defines in-memory non-volatile objects which can be directly operated on memory-like resources. We then discuss our structured data in-memory persistence library that can be used to load/store non-volatile generic Java object from/to underlying heterogeneous memory-like resources, such as DRAM, NVMe, even SSD. We then present a non-volatile computing case using Spark. We will introduce that this model can (1) Lazily loads data to minimize memory footprint, (2) Naturally fits both non-volatile RDD and off-heap RDD, (3) Uses non-volatile/off-heap RDDs to transform Spark datasets, (4) Avoids memory caching by using in-place non-volatile datasets. Finally we will present that up to 2X performance boost can be achieved on Spark ML tests after applying this non-volatile computing approach that removed SerDe, caching hot data, and reducing GC pause time dramatically.

Introduction to Apache Geode (Cork, Ireland)

Anthony Baker

The Fast Path to Building Operational Applications with Spark

SingleStore

DevOps Supercharged with Docker on Exadata

MarketingArrowECS_CZ

Apache Spark

masifqadri

EMC SRM vs. Sentinel Navigator - Deep dive

sansentinel

MySQL and Spark machine learning performance on Azure VMsbased on 3rd Gen AMD...

Principled Technologies

IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...

In-Memory Computing Summit

Introduce_non-volatile_generic_object_programming_model_for_In-Memory_Computing

YanpingWang

Spark introduction and architecture

Sohil Jain

Spark introduction and architecture

Sohil Jain

Spark

fatemehjamalii

Scaling Spark – Vertically: The mantra of Spark technology is divide and conquer, especially for problems too big for a single computer. The more you divide a problem across worker nodes, the more total memory and processing parallelism you can exploit. This comes with a trade-off. Splitting applications and data across multiple nodes is nontrivial, and more distribution results in more network traffic which becomes a bottleneck. Can you achieve scale and parallelism without those costs? We’ll show results of a variety of Spark application domains including structured data, graph processing and common machine learning in a single, high-capacity scaled-up system versus a more distributed approach and discuss how virtualization can be used to define node size flexibly, achieving the best balance for Spark performance.

Dr. Ike Nassi, Founder, TidalScale at MLconf NYC - 4/15/16

MLconf

Hybrid Transactional/Analytics Processing with Spark and IMDGs

Ali Hodroj

Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data. In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas: Agenda: • Overview of Spark Fundamentals & Architecture • What’s new in Spark 2.x • Unified APIs: SparkSessions, SQL, DataFrames, Datasets • Introduction to DataFrames, Datasets and Spark SQL • Introduction to Structured Streaming Concepts • Four Hands On Labs You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL. Level: Beginner to intermediate, not for advanced Spark users. Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional. Bio: Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.

Jump Start on Apache® Spark™ 2.x with Databricks

Semelhante a Virtualizing Apache Spark with Justin Murray (20)

Why Kubernetes as a container orchestrator is a right choice for running spar...

Spark Saturday: Spark SQL & DataFrame Workshop with Apache Spark 2.3

Teaching Apache Spark: Demonstrations on the Databricks Cloud Platform

Big Telco - Yousun Jeong

Big Telco Real-Time Network Analytics

Simplify and Boost Spark 3 Deployments with Hypervisor-Native Kubernetes

Introduction to Apache Geode (Cork, Ireland)

The Fast Path to Building Operational Applications with Spark

DevOps Supercharged with Docker on Exadata

Apache Spark

EMC SRM vs. Sentinel Navigator - Deep dive

MySQL and Spark machine learning performance on Azure VMsbased on 3rd Gen AMD...

IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...

Introduce_non-volatile_generic_object_programming_model_for_In-Memory_Computing

Spark introduction and architecture

Spark

Dr. Ike Nassi, Founder, TidalScale at MLconf NYC - 4/15/16

Hybrid Transactional/Analytics Processing with Spark and IMDGs

Jump Start on Apache® Spark™ 2.x with Databricks

Mais de Databricks

DW Migration Webinar-March 2022.pptx

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.

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

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.

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

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

Democratizing Data Quality Through a Centralized Platform

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.

Learn to Use Databricks for Data Science

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.

Why APM Is Not the Same As ML Monitoring

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.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Stage Level Scheduling Improving Big Data and AI Integration

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.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

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 your Data Pipelines with Apache Spark on Kubernetes

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.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

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.

Sawtooth Windows for Feature Aggregations

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

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

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.

Re-imagine Data Monitoring with whylogs and Spark

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.

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

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.

Processing Large Datasets for ADAS Applications using Apache Spark

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!

Massive Data Processing in Adobe Using Delta Lake

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5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

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

Processing Large Datasets for ADAS Applications using Apache Spark

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