Spark Summit East 2015 Advanced Devops Student Slides

Optimizing spark jobs through a true understanding of spark core. Learn: What is a partition? What is the difference between read/shuffle/write partitions? How to increase parallelism and decrease output files? Where does shuffle data go between stages? What is the "right" size for your spark partitions and files? Why does a job slow down with only a few tasks left and never finish? Why doesn't adding nodes decrease my compute time?

eBay is highly using Spark as one of most significant data engines. In data warehouse domain, there are millions of batch queries running every day against 6000+ key DW tables, which contains over 22PB data (compressed) and still keeps booming every year. In machine learning domain, it is playing a more and more significant role. We have introduced our great achievement in migration work from MPP database to Apache Spark last year in Europe Summit. Furthermore, from the vision of the entire infrastructure, it is still a big challenge on managing workload and efficiency for all Spark jobs upon our data center. Our team is leading the whole infrastructure of big data platform and the management tools upon it, helping our customers -- not only DW engineers and data scientists, but also AI engineers -- to leverage on the same page. In this session, we will introduce how to benefit all of them within a self-service workload management portal/system. First, we will share the basic architecture of this system to illustrate how it collects metrics from multiple data centers and how it detects the abnormal workload real-time. We develop a component called Profiler which is to enhance the current Spark core to support customized metric collection. Next, we will demonstrate some real user stories in eBay to show how the self-service system reduces the efforts both in customer side and infra-team side. That's the highlight part about Spark job analysis and diagnosis. Finally, some incoming advanced features will be introduced to describe an automatic optimizing workflow rather than just alerting. Speaker: Lantao Jin

This document provides an overview and summary of Amazon S3 best practices and tuning for Hadoop/Spark in the cloud. It discusses the relationship between Hadoop/Spark and S3, the differences between HDFS and S3 and their use cases, details on how S3 behaves from the perspective of Hadoop/Spark, well-known pitfalls and tunings related to S3 consistency and multipart uploads, and recent community activities related to S3. The presentation aims to help users optimize their use of S3 storage with Hadoop/Spark frameworks.

The document provides an overview of Amazon Elastic MapReduce (EMR) including how to easily launch and manage clusters, leverage Amazon S3 for storage, optimize file formats and storage, and design patterns for batch processing, interactive querying, and server clusters. It also shares lessons learned from Swiftkey including using Parquet and Cascalog for ETL, getting serialization right, avoiding many small files in S3, using spot instances, and experimenting with instance types. The document concludes by mentioning Apache Spark on EMR for faster in-memory processing directly from S3.

This document provides an overview of Apache Flink internals. It begins with an introduction and recap of Flink programming concepts. It then discusses how Flink programs are compiled into execution plans and executed in a pipelined fashion, as opposed to being executed eagerly like regular code. The document outlines Flink's architecture including the optimizer, runtime environment, and data storage integrations. It also covers iterative processing and how Flink handles iterations both by unrolling loops and with native iterative datasets.

Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of Spark SQL and understand how to tune Spark SQL performance.

Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with

This document discusses 5 common mistakes when writing Spark applications: 1) Improperly sizing executors by not considering cores, memory, and overhead. The optimal configuration depends on the workload and cluster resources. 2) Applications failing due to shuffle blocks exceeding 2GB size limit. Increasing the number of partitions helps address this. 3) Jobs running slowly due to data skew in joins and shuffles. Techniques like salting keys can help address skew. 4) Not properly managing the DAG to avoid shuffles and bring work to the data. Using ReduceByKey over GroupByKey and TreeReduce over Reduce when possible. 5) Classpath conflicts arising from mismatched library versions, which can be addressed using sh

The document discusses Apache Flink, an open source stream processing framework. It provides high throughput and low latency processing of both streaming and batch data. Flink allows for explicit handling of event time, stateful stream processing with exactly-once semantics, and high performance. It also supports features like windowing, sessionization, and complex event processing that are useful for building streaming applications.

Oracle Real Application Clusters 19c provides best practices and new features for upgrading to Oracle 19c. It discusses upgrading Oracle RAC to Linux 7 with minimal downtime using node draining and relocation techniques. Oracle 19c allows for upgrading the Grid Infrastructure management repository and patching faster using a new Oracle home. The presentation also covers new resource modeling for PDBs in Oracle 19c and improved Clusterware diagnostics.

In this hands-on Apache Flink presentation, you will learn in a step-by-step tutorial style about: • How to setup and configure your Apache Flink environment: Local/VM image (on a single machine), cluster (standalone), YARN, cloud (Google Compute Engine, Amazon EMR, ... )? • How to get familiar with Flink tools (Command-Line Interface, Web Client, JobManager Web Interface, Interactive Scala Shell, Zeppelin notebook)? • How to run some Apache Flink example programs? • How to get familiar with Flink's APIs and libraries? • How to write your Apache Flink code in the IDE (IntelliJ IDEA or Eclipse)? • How to test and debug your Apache Flink code? • How to deploy your Apache Flink code in local, in a cluster or in the cloud? • How to tune your Apache Flink application (CPU, Memory, I/O)?

Flink Forward San Francisco 2022. Task Managers constantly running out of memory? Flink job keeps restarting from cryptic Akka exceptions? Flink job running but doesn’t seem to be processing any records? We share practical learnings from running thousands of Flink Jobs for different use-cases and take a look at common challenges they have experienced such as out-of-memory errors, timeouts and job stability. We will cover memory tuning, S3 and Akka configurations to address common pitfalls and the approaches that we take on automating health monitoring and management of Flink jobs at scale. by Hong Teoh & Usamah Jassat

Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team

Nowadays, people are creating, sharing and storing data at a faster pace than ever before, effective data compression / decompression could significantly reduce the cost of data usage. Apache Spark is a general distributed computing engine for big data analytics, and it has large amount of data storing and shuffling across cluster in runtime, the data compression/decompression codecs can impact the end to end application performance in many ways. However, there’s a trade-off between the storage size and compression/decompression throughput (CPU computation). Balancing the data compress speed and ratio is a very interesting topic, particularly while both software algorithms and the CPU instruction set keep evolving. Apache Spark provides a very flexible compression codecs interface with default implementations like GZip, Snappy, LZ4, ZSTD etc. and Intel Big Data Technologies team also implemented more codecs based on latest Intel platform like ISA-L(igzip), LZ4-IPP, Zlib-IPP and ZSTD for Apache Spark; in this session, we’d like to compare the characteristics of those algorithms and implementations, by running different micro workloads as well as end to end workloads, based on different generations of Intel x86 platform and disk. It’s supposedly to be the best practice for big data software engineers to choose the proper compression/decompression codecs for their applications, and we also will present the methodologies of measuring and tuning the performance bottlenecks for typical Apache Spark workloads.

This document discusses Spark shuffle, which is an expensive operation that involves data partitioning, serialization/deserialization, compression, and disk I/O. It provides an overview of how shuffle works in Spark and the history of optimizations like sort-based shuffle and an external shuffle service. Key concepts discussed include shuffle writers, readers, and the pluggable block transfer service that handles data transfer. The document also covers shuffle-related configuration options and potential future work.

This presentation introduces Apache Flink, a massively parallel data processing engine which currently undergoes the incubation process at the Apache Software Foundation. Flink's programming primitives are presented and it is shown how easily a distributed PageRank algorithm can be implemented with Flink. Intriguing features such as dedicated memory management, Hadoop compatibility, streaming and automatic optimisation make it an unique system in the world of Big Data processing.

Watch video at: http://youtu.be/Wg2boMqLjCg Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications

This document discusses Databricks Cloud, a platform for running Apache Spark workloads that aims to accelerate time-to-results from months to days. It provides a unified platform with notebooks, dashboards, and jobs running on Spark clusters managed by Databricks. Key benefits include zero management of clusters, interactive queries and streaming for real-time insights, and the ability to develop models and visualizations in notebooks and deploy them as production jobs or dashboards without code changes. The platform is open source with no vendor lock-in and supports various data sources and third party applications. It is being used by over 3,500 organizations for applications like data preparation, analytics, and machine learning.

Apache Spark is a next-generation processing engine optimized for speed, ease of use, and advanced analytics well beyond batch. The Spark framework supports streaming data and complex, iterative algorithms, enabling applications to run 100x faster than traditional MapReduce programs. With Spark, developers can write sophisticated parallel applications for faster business decisions and better user outcomes, applied to a wide variety of architectures and industries. Learn What Apache Spark is and how it compares to Hadoop MapReduce, How to filter, map, reduce, and save Resilient Distributed Datasets (RDDs), Who is best suited to attend the course and what prior knowledge you should have, and the benefits of building Spark applications as part of an enterprise data hub.

R is the latest language added to Apache Spark, and the SparkR API is slightly different from PySpark. With the release of Spark 2.0, the R API officially supports executing user code on distributed data. This is done through a family of apply() functions. In this talk, Hossein Falaki gives an overview of this new functionality in SparkR. Using this API requires some changes to regular code with dapply(). This talk will focus on how to correctly use this API to parallelize existing R packages. Most important topics of consideration will be performance and correctness when using the apply family of functions in SparkR. Speaker: Hossein Falaki This talk was originally presented at Spark Summit East 2017.

The document provides an agenda for a DevOps advanced class on Spark being held in June 2015. The class will cover topics such as RDD fundamentals, Spark runtime architecture, memory and persistence, Spark SQL, PySpark, and Spark Streaming. It will include labs on DevOps 101 and 102. The instructor has over 5 years of experience providing Big Data consulting and training, including over 100 classes taught.

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. The document discusses Spark's architecture including its core abstraction of resilient distributed datasets (RDDs), and demos Spark's capabilities for streaming, SQL, machine learning and graph processing on large clusters.

This document provides an agenda and overview for an introductory Spark development class. The class will cover the history of big data and Spark, RDD fundamentals, the Databricks UI, transformations and actions, DataFrames, Spark UIs, and resource managers. It includes surveys of students' backgrounds and use cases. Databricks is a platform for building data pipelines and advanced analytics with Spark.

Hortonworks Data Platform (HDP) 2.3 includes several new capabilities: 1) It improves the user experience with more guided configuration, customizable dashboards, and improved workload management. 2) It enhances security with new data encryption at rest and extends data governance. 3) It adds proactive cluster monitoring through Hortonworks SmartSense to enhance support.

Tathagata 'TD' Das presented at Bay Area Apache Spark Meetup. This talk covers the merits and motivations of Structured Streaming, and how you can start writing end-to-end continuous applications using Structured Streaming APIs.

Apache Spark 2.0 was released this summer and is already being widely adopted. In this presentation Matei talks about how changes in the API have made it easier to write batch, streaming and realtime applications. The Dataset API, which is now integrated with DataFrames, makes it possible to benefit from powerful optimizations such as pushing queries into data sources, while the Structured Streaming extension to this API makes it possible to run many of the same computations in a streaming fashion automatically.

Stable and robust data pipelines are a critical component of the data infrastructure of enterprises. Most commonly, data pipelines ingest messy data sources with incorrect, incomplete or inconsistent records and produce curated and/or summarized data for consumption by subsequent applications. In this talk, we go over new and upcoming features in Spark that enabled it to better serve such workloads. Such features include isolation of corrupt input records and files, useful diagnostic feedback to users and improved support for nested type handling which is common in ETL jobs. Speaker: Sameer Agarwal This talk was originally presented at Spark Summit East 2017.

Ambari 2.1 includes several new features and improvements, including manual Kerberos configuration, customizable dashboards, guided configurations, rack awareness, and views framework enhancements. It adds support for Storm Nimbus HA, Ranger HA, RHEL/CentOS 7, and new JDKs. The views framework now supports auto-configuration using cluster properties and auto-creation of view instances when cluster requirements are met. Standalone Ambari servers allow running views without managing a cluster.

This document compares query performance times between Apache Hive versions 0.10 and 0.13 using a benchmark of 50 SQL queries on a 30TB dataset. The results show that Hive 0.13 was over 100 times faster for 6 queries and averaged 52 times faster for all queries compared to Hive 0.10. Significant performance improvements were achieved through optimizations made during the Stinger Initiative involving 145 developers from 44 companies over 13 months.

This document provides information about using Scalding on Tez. It begins with prerequisites for using Scalding on Tez, including having a YARN cluster, Cascading 3.0, and the TEZ runtime library in HDFS. It then discusses setting memory and Java heap configuration flags for Tez jobs in Scalding. The document provides a mini-tutorial on using Scalding on Tez, covering build configuration, job flags, and challenges encountered in practice like Guava version mismatches and issues with Cascading's Tez registry. It also presents a word count plus example Scalding application built to run on Tez. The document concludes with some tips for debugging Tez jobs in Scalding using Cascading's

This webinar focuses on introducing Scalding for developers and writing applications for Hadoop and YARN using Scalding. Guest speaker Jonathan Coveney from Twitter provides an overview, use cases, limitations, and core concepts.

Part of the Hortonworks YARN Ready Webinar Series, this session is about management of Apache Hadoop and YARN using Apache Ambari. This series targets developers and we will feature a demo on Ambari.

http://hortonworks.com/hadoop/spark/ Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=03debab5ba04b34a033dc5c2f03c7967 As the ratio of memory to processing power rapidly evolves, many within the Hadoop community are gravitating towards Apache Spark for fast, in-memory data processing. And with YARN, they use Spark for machine learning and data science use cases along side other workloads simultaneously. This is a continuation of our YARN Ready Series, aimed at helping developers learn the different ways to integrate to YARN and Hadoop. Tools and applications that are YARN Ready have been verified to work within YARN.

This document discusses how Hortonworks Data Platform (HDP) can enable enterprises to build a modern data architecture centered around Hadoop. It describes how HDP provides a centralized platform for managing all types of data at scale using technologies like YARN. Case studies are presented showing how companies have used HDP to optimize costs, develop new analytics applications, and work towards creating a unified "data lake". The document outlines the key components of HDP including its support for any application, any data, and deployment anywhere. It also highlights how partners extend HDP's capabilities and how Hortonworks provides enterprise-grade support.

The complete deck from Hands on Introduction to Hadoop, HDF, Hive and Pig: Part 1 Meetup held at Hortonworks HQ. hortonworkersR4apache

The document is an agenda for an intro to Spark development class. It includes an overview of Databricks, the history and capabilities of Spark, and the agenda topics which will cover RDD fundamentals, transformations and actions, DataFrames, Spark UIs, and Spark Streaming. The class will include lectures, labs, and surveys to collect information on attendees' backgrounds and goals for the training.

Committed to the goal of building open-source frameworks, tools, and algorithms that make building real-time applications decisions on live data with stronger security, The RISELab is set to innovate and enhance Spark

This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.

Introduction and background Spark RDD API Introduction to Scala Spark DataFrames API + SparkSQL Spark Execution Model Spark Shell & Application Deployment Spark Extensions (Spark Streaming, MLLib, ML) Spark & DataStax Enterprise Integration Demos

This document provides an introduction and overview of Apache Spark. It discusses what Spark is, its performance advantages over Hadoop MapReduce, its core abstraction of resilient distributed datasets (RDDs), and how Spark programs are executed. Key features of Spark like its interactive shell, transformations and actions on RDDs, and Spark SQL are explained. Recent new features in Spark like DataFrames, external data sources, and the Tungsten performance optimizer are also covered. The document aims to give attendees an understanding of Spark's capabilities and how it can provide faster performance than Hadoop for certain applications.

Apache Spark is a cluster computing framework that allows for fast, easy, and general processing of large datasets. It extends the MapReduce model to support iterative algorithms and interactive queries. Spark uses Resilient Distributed Datasets (RDDs), which allow data to be distributed across a cluster and cached in memory for faster processing. RDDs support transformations like map, filter, and reduce and actions like count and collect. This functional programming approach allows Spark to efficiently handle iterative algorithms and interactive data analysis.

This document discusses Apache Spark, a fast and general engine for big data processing. It describes how Spark generalizes the MapReduce model through its Resilient Distributed Datasets (RDDs) abstraction, which allows efficient sharing of data across parallel operations. This unified approach allows Spark to support multiple types of processing, like SQL queries, streaming, and machine learning, within a single framework. The document also outlines ongoing developments like Spark SQL and improved machine learning capabilities.

Brief introduction to Apache Spark, prior to a demo of Databricks Cloud by Timothy Hunter. Sponsored by Prof. Reza Zadeh @ Stanford ICME

1. MapReduce Review 2. Introduction to Spark and RDDs 3. Generality of RDDs (e.g. streaming, ML) 4. DataFrames 5. Internals

The document is a presentation about Apache Spark given on August 25th, 2015 in Pittsburgh by Sneha Challa. It introduces Spark as a fast and general cluster computing engine for large-scale data processing. It discusses Spark's Resilient Distributed Datasets (RDDs) and transformations/actions. It provides examples of Spark APIs like map, reduce, and explains running Spark on standalone, Mesos, YARN, or EC2 clusters. It also covers Spark libraries like MLlib and running machine learning algorithms like k-means clustering and logistic regression.

Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yNuLGF. Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code. Filmed at qconsf.com. Matei Zaharia is an assistant professor of computer science at MIT, and CTO of Databricks, the company commercializing Apache Spark.

This document discusses Spark Streaming and its use for near real-time ETL. It provides an overview of Spark Streaming, how it works internally using receivers and workers to process streaming data, and an example use case of building a recommender system to find matches using both batch and streaming data. Key points covered include the streaming execution model, handling data receipt and job scheduling, and potential issues around data loss and (de)serialization.

The document discusses Spark, an open-source cluster computing framework. It describes Spark's Resilient Distributed Dataset (RDD) as an immutable and partitioned collection that can automatically recover from node failures. RDDs can be created from data sources like files or existing collections. Transformations create new RDDs from existing ones lazily, while actions return values to the driver program. Spark supports operations like WordCount through transformations like flatMap and reduceByKey. It uses stages and shuffling to distribute operations across a cluster in a fault-tolerant manner. Spark Streaming processes live data streams by dividing them into batches treated as RDDs. Spark SQL allows querying data through SQL on DataFrames.

Spark is an open-source distributed computing framework used for processing large datasets. It allows for in-memory cluster computing, which enhances processing speed. Spark core components include Resilient Distributed Datasets (RDDs) and a directed acyclic graph (DAG) that represents the lineage of transformations and actions on RDDs. Spark Streaming is an extension that allows for processing of live data streams with low latency.

This document provides a summary of Spark RDDs and the Spark execution model: - RDDs (Resilient Distributed Datasets) are Spark's fundamental data structure, representing an immutable distributed collection of objects that can be operated on in parallel. RDDs track lineage to support fault tolerance and optimization. - Spark uses a logical plan built from transformations on RDDs, which is then optimized and scheduled into physical stages and tasks by the Spark scheduler. Tasks operate on partitions of RDDs in a data-parallel manner. - The scheduler pipelines transformations where possible, truncates redundant work, and leverages caching and data locality to improve performance. It splits the graph into stages separated by shuffle operations or parent RDD boundaries

Spark is an open-source cluster computing framework. It started as a project in 2009 at UC Berkeley and was open sourced in 2010. It has over 300 contributors from 50+ organizations. Spark uses Resilient Distributed Datasets (RDDs) that allow in-memory cluster computing across clusters. RDDs provide a programming model for distributed datasets that can be created from external storage or by transforming existing RDDs. RDDs support operations like map, filter, reduce to perform distributed computations lazily.

This document provides an agenda and summaries for a meetup on introducing DataFrames and R on Apache Spark. The agenda includes overviews of Apache Spark 1.3, DataFrames, R on Spark, and large scale machine learning on Spark. There will also be discussions on news items, contributions so far, what's new in Spark 1.3, more data source APIs, what DataFrames are, writing DataFrames, and DataFrames with RDDs and Parquet. Presentations will cover Spark components, an introduction to SparkR, and Spark machine learning experiences.

In this talk we’ll explore Apache Spark — the most popular cluster computing framework right now. We’ll look at the improvements that Spark brought over Hadoop MapReduce and what makes Spark so fast; explore Spark programming model and RDDs; and look at some sample use cases for Spark and big data in general. This talk will be interesting for people who have little or no experience with Spark and would like to learn more about it. It will also be interesting to a general engineering audience as we’ll go over the Spark programming model and some engineering tricks that make Spark fast.

This document provides an overview of Spark's RDD abstraction and the life cycle of a Spark application. It defines RDDs as distributed collections characterized by partitions, dependencies, a compute function, and optional properties like a partitioner. It describes how Spark builds a DAG of stages from transformations and actions, and schedules tasks across executors. The document also covers performance debugging techniques like identifying slow stages, stragglers, garbage collection issues, and profiling tasks locally. Debugging tools discussed include the Spark UI, executor logs, jstack, and YourKit.

Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming. Presented at the Desert Code Camp: http://oct2016.desertcodecamp.com/sessions/all

Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming. Presented at the Desert Code Camp: http://oct2016.desertcodecamp.com/sessions/all

Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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!

Ansys Mechanical enables you to solve complex structural engineering problems and make better, faster design decisions. With the finite element analysis (FEA) solvers available in the suite, you can customize and automate solutions for your structural mechanics problems and parameterize them to analyze multiple design scenarios. Ansys Mechanical is a dynamic tool that has a complete range of analysis tools.

Connectors integrate Apache Kafka® with external data systems, enabling you to move away from a brittle spaghetti architecture to one that is more streamlined, secure, and future-proof. However, if your team still spends multiple dev cycles building and managing connectors using just open source Kafka Connect, it’s time to consider a faster and cost-effective alternative.

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Splunk Presentation

Are you wondering how to migrate to the Cloud? At the ITB session, we addressed the challenge of managing multiple ColdFusion licenses and AWS EC2 instances. Discover how you can consolidate with just one EC2 instance capable of running over 50 apps using CommandBox ColdFusion. This solution supports both ColdFusion flavors and includes cb-websites, a GoLang binary for managing CommandBox websites.

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Our world runs on software. It governs all major aspects of our life. It is an enabler for research and innovation, and is critical for business competitivity. Traditional software engineering techniques have achieved high effectiveness, but still may fall short on delivering software at the accelerated pace and with the increasing quality that future scenarios will require. To attack this issue, some software paradigms raise the automation of software development via higher levels of abstraction through domain-specific languages (e.g., in model-driven engineering) and empowering non-professional developers with the possibility to build their own software (e.g., in low-code development approaches). In a software-demanding world, this is an attractive possibility, and perhaps -- paraphrasing Andy Warhol -- "in the future, everyone will be a developer for 15 minutes". However, to make this possible, methods are required to tweak languages to their context of use (crucial given the diversity of backgrounds and purposes), and the assistance to developers throughout the development process (especially critical for non-professionals). In this keynote talk at ICSOFT'2024 I presented enabling techniques for this vision, supporting the creation of families of domain-specific languages, their adaptation to the usage context; and the augmentation of low-code environments with assistants and recommender systems to guide developers (professional or not) in the development process.

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In this session, we will be discussing major outages that happened in major enterprises. We will analyse the actual thread dumps, heap dumps, GC logs, and other artifacts captured at the time of the problem. After this session, troubleshooting CPU spikes, OutOfMemoryError, response time degradations, network connectivity issues, and application unresponsiveness may not stump you.

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