Spark, Tachyon and Mesos internals

Building an intelligent big data application in 30 minutes

Strata Barcelona presentation slides, a live demo of building an intelligent big data application from a web console. The tools and APIs behind are built on top of Spark, Spark SQL/Shark, Tachyon, Mesos, Cassandra, SolrCloud, iPython and include: ELT pipeline (ingestion and transformation), data warehouse explorer, export to NoSql and generated APIs, export to SolrCloud and generated APIs, predictive model building, training and publishing, dashboard UI, monitoring and instrumentation.

Architecture at Scale

Elasticsearch

ALLUXIO (formerly Tachyon): Unify Data at Memory Speed - Effective using Spar...

Alluxio, Inc.

Using Apache Spark in the Cloud—A Devops Perspective with Telmo Oliveira

Toon is a leading brand in the European smart energy market, currently expanding internationally, providing energy usage insights, eco-friendly energy management and smart thermostat use for the connected home. As value added services become ever more relevant in this market, we have the need to ensure that we can easily and safely on-board new tenants into our data platform. In this talk we’re going to guide you across a less discussed side of using Spark in production – devops. We will speak about our journey from an on-premise cluster to a managed solution in the cloud. A lot of moving parts were involved: ETL flows, data sharing with 3rd parties and data migration to the new environment. Add to this the need to have a multi-tenant environment, revamp our toolset and deploy a live public facing service. It’s possible to find a lot of great examples of how Spark is used for data-science purposes. On the data engineering side, we need to deploy production services, ensure data is cleaned, secured and available, and keep the data-science teams happy. We’d like to share some of the options we took and some of the lessons learned from this (ongoing) transition.

Ignite Your Big Data With a Spark!

Progress

Stsg17 speaker yousunjeong

Yousun Jeong

This document provides an overview of SK Telecom's use of big data analytics and Spark. Some key points: - SKT collects around 250 TB of data per day which is stored and analyzed using a Hadoop cluster of over 1400 nodes. - Spark is used for both batch and real-time processing due to its performance benefits over other frameworks. Two main use cases are described: real-time network analytics and a network enterprise data warehouse (DW) built on Spark SQL. - The network DW consolidates data from over 130 legacy databases to enable thorough analysis of the entire network. Spark SQL, dynamic resource allocation in YARN, and integration with BI tools help meet requirements for timely processing and quick

Apache Spark Overview

airisData

Tim Spann will present on learning Apache Spark. He is a senior solutions architect who previously worked as a senior field engineer and startup engineer. airis.DATA, where Spann works, specializes in machine learning and graph solutions using Spark, H20, Mahout, and Flink on petabyte datasets. The agenda includes an overview of Spark, an explanation of MapReduce, and hands-on exercises to install Spark, run a MapReduce job locally, and build a project with IntelliJ and SBT.

A New “Sparkitecture” for Modernizing your Data Warehouse: Spark Summit East ...

Legacy enterprise data warehouse (EDW) architecture, geared toward day-to-day workloads associated with operational querying, reporting, and analytics, are often ill-equipped to handle the volume of data, traffic, and varied data types associated with a modern, ad-hoc analytics platform. Faced with challenges of increasing pipeline speed, aggregation, and visualization in a simplified, self-service fashion, organizations are increasingly turning to some combination of Spark, Hadoop, Kafka, and proven analytical databases like Vertica as key enabling technologies to optimize their EDW architecture. Join us to learn how successful organizations have developed real-time streaming solutions with these technologies for range of use cases, including IOT predictive maintenance.

Optimizing Spark Deployments for Containers: Isolation, Safety, and Performan...

Developers love Linux containers, which neatly package up an application and its dependencies and are easy to create and share. However, this unbeatable developer experience hides some deployment challenges for real applications: how do you wire together pieces of a multi-container application? Where do you store your persistent data if your containers are ephemeral? Do containers really contain and isolate your application, or are they merely hiding potential security vulnerabilities? Are your containers scheduled across your compute resources efficiently, or are they trampling on one another? Container application platforms like Kubernetes provide the answers to some of these questions. We’ll draw on expertise in Linux security, distributed scheduling, and the Java Virtual Machine to dig deep on the performance and security implications of running in containers. This talk will provide a deep dive into tuning and orchestrating containerized Spark applications. You’ll leave this talk with an understanding of the relevant issues, best practices for containerizing data-processing workloads, and tips for taking advantage of the latest features and fixes in Linux Containers, the JDK, and Kubernetes. You’ll leave inspired and enabled to deploy high-performance Spark applications without giving up the security you need or the developer-friendly workflow you want.

Powering Predictive Mapping at Scale with Spark, Kafka, and Elastic Search: S...

The document discusses powering predictive mapping at scale using the SMACK stack, which includes Spark, Kafka, and Elasticsearch. It describes how the SMACK stack can ingest millions of events per second from connected devices, store the data in Apache Spark, and allow real-time and batch processing of the data. It also provides an example of using the stack for real-time tracking of geo-enabled IoT devices and demonstrates the data flow and a demo of the system.

Spark Operator—Deploy, Manage and Monitor Spark clusters on Kubernetes

The document discusses the Spark Operator, which allows deploying, managing, and monitoring Spark clusters on Kubernetes. It describes how the operator extends Kubernetes by defining custom resources and reacting to events from those resources, such as SparkCluster, SparkApplication, and SparkHistoryServer. The operator takes care of common tasks to simplify running Spark on Kubernetes and hides the complexity through an abstract operator library.

Spark Summit EU talk by Ruben Pulido Behar Veliqi

The document discusses IBM's transition from a single-tenant Hadoop architecture to a multi-tenant Apache Spark architecture for their Watson Analytics for Social Media product. The new architecture aggregates social media data from thousands of tenants into a single stream and uses Spark, Kafka and Zookeeper to provide robust real-time analytics with low latency switching between tenants. Key aspects of the new architecture include separating analytics into tenant-specific and language-specific components, and removing state from processing components.

Accelerating Spark Genome Sequencing in Cloud—A Data Driven Approach, Case St...

Spark data processing is shifting from on-premises to cloud service to take advantage of its horizontal resource scalability, better data accessibility and easy manageability. However, fully utilizing the computational power, fast storage and networking offered by cloud service can be challenging without deep understanding of workload characterizations and proper software optimization expertise. In this presentation, we will use a Spark based programing framework – Genome Analysis Toolkit version 4 (GATK4, under development), as an example to present a process of configuring and optimizing a proficient Spark cluster on Google Cloud to speed up genome data processing. We will first introduce an in-house developed data profiling framework named PAT, and discuss how to use PAT to quickly establish the best combination of VM configurations and Spark configurations to fully utilize cloud hardware resources and Spark computational parallelism. In addition, we use PAT and other data profiling tools to identify and fix software hotspots in application. We will show a case study in which we identify a thread scalability issue of Java Instanceof operator. The fix in Scala language hugely improves performance of GATK4 and other Spark based workloads.

Elastic Stack roadmap deep dive

Elasticsearch

- Elastic provides a search and analytics platform called the Elastic Stack that includes the Elastic Stack, Beats data shippers, and Kibana analytics and visualization tools. - The presentation discussed updates to Elastic's products including performance improvements to search, new features for distributed search across data centers, and enhanced security options for authentication and authorization. - Elastic aims to provide customizable and extensible solutions for users to ingest, store, search, analyze and visualize large volumes of data from various sources.

Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...

Predictive intelligence from machine learning has the potential to change everything in our day to day experiences, from education to entertainment, from travel to healthcare, from business to leisure and everything in between. Modern ML frameworks are batch by nature and cannot pivot on the fly to changing user data or situations. Many simple ML applications such as those that enhance the user experience, can benefit from real-time robust predictive models that adapt on the fly. Join this session to learn how common practices in machine learning such as running a trained model in production can be substantially accelerated and radically simplified by using Redis modules that natively store and execute common models generated by Spark ML and Tensorflow algorithms. We will also discuss the implementation of simple, real-time feed-forward neural networks with Neural Redis and scenarios that can benefit from such efficient, accelerated artificial intelligence. Real-life implementations of these new techniques at a large consumer credit company for fraud analytics, at an online e-commerce provider for user recommendations and at a large media company for targeting content will also be discussed.

Automated Metadata Management in Data Lake – A CI/CD Driven Approach

We as data engineers are aware of trade off’s between development speed, metadata governance and schema evolution (or restriction) in rapidly evolving organization. Our day to day activities involve adding/removing/updating tables, protecting PII Information, curating and exposing data to our consumers. While our data lake keeps growing exponentially, there is equal increase in our downstream consumers. Struggle is to maintain balance between quickly promoting metadata changes with robust validation for downstream systems stability. In relational world DDL, DML changes can be managed through numerous options available for every kind of database from the vendor or 3rd party. As engineers we developed a tool which uses centralized git managed repository of data schemas in yml structure with ci/cd capabilities which maintains stability of our data lake and downstream systems. In this presentation Northwestern Mutual Engineers, will discuss how they designed and developed new end-to-end ci/cd driven metadata management tool to make introduction of new tables/views, managing access requests etc in a more robust, maintainable and scalable way, all with only checking in yml files. This tool can be used by people who have no or minimal knowledge of spark. Key focus will be: Need for metadata management tool in a data lake Architecture and Design of the tool Maintaining information on databases/tables/views like schema, owner, PII, description etc in simple to understand yml structure Live demo of creating a new table with CI/CD promotion to production

HBaseCon 2015: Apache Kylin - Extreme OLAP Engine for Hadoop

HBaseCon

Kylin is an open source distributed analytics engine contributed by eBay that provides a SQL interface and OLAP on Hadoop supporting extremely large datasets. Kylin's pre-built MOLAP cubes (stored in HBase), distributed architecture, and high concurrency helps users analyze multidimensional queries via SQL and other BI tools. During this session, you'll learn how Kylin uses HBase's key-value store to serve SQL queries with relational schema.

Fighting Cybercrime: A Joint Task Force of Real-Time Data and Human Analytics...

DataWorks Summit/Hadoop Summit

Cybercrime is big business. Gartner reports worldwide security spending at $80B, with annual losses totalling more than $1.2T (in 2015). Small to medium sized businesses now account for more than half of the attacks targeting enterprises today. The threat actors behind these attacks are continually shifting their techniques and toolkits to evade the security defenses that businesses commonly use. Thanks to the growing frequency and complexity of attacks, the task of identifying and mitigating security-related events has become increasingly difficult. At eSentire, we use a combination of data and human analytics to identify, respond to and mitigate cyber threats in real-time. We capture all network traffic on our customers’ networks, hence ingesting a large amount of time-series data. We process the data as it is being streamed into our system to extract relevant threat insights and block attacks in real-time. Furthermore, we enable our cybersecurity analysts to perform in-depth investigations to: i) confirm attacks and ii) identify threats that analytical models miss. Having security experts in the loop provides feedback to our analytics engine, thereby improving the overall threat detection effectiveness. So how exactly can you build an analytics pipeline to handle a large amount of time-series/event-driven data? How do you build the tools that allow people to query this data with the expectation of mission-critical response times? In this presentation, William Callaghan will focus on the challenges faced and lessons learned in building a human-in-the loop cyber threat analytics pipeline. They will discuss the topic of analytics in cybersecurity and highlight the use of technologies such as Spark Streaming/SQL, Cassandra, Kafka and Alluxio in creating an analytics architecture with missions-critical response times.

Data Warehouse Modernization - Big Data in the Cloud Success with Qubole on O...

Qubole

The effective use of big data is the key to gaining a competitive advantage and outperforming the competition. This change demands that companies consume and blend enormous amount of data created from divergent and inherently mismatched sources, which represents a paradigm shift to the traditional data warehouse. Companies need to modernize their data warehouse, augmenting it with a platform that allows storage, processing, exploration and analysis of large and diverse datasets without limiting the ability to deliver the data access, and flexibility responding to the needs of the business. That’s where Oracle Cloud and Qubole work together delivering a new breed of data platform —capable of storing and processing the overwhelming amount of data that on-premises big data deployments cannot handle. Watch this on-demand webinar to understand: - Why deploying big data on-premises is expensive, complex to maintain and limits your ability to scale across new use cases and data sources - How Oracle Bare Metal Cloud's predictable and fast performance compute and network services deliver the foundation of a cost-effective, high-performance big data platform - How Qubole leverages Oracle Bare Metal Cloud to provide a turnkey big data service that optimizes cost, performance, and scale, enabling self-service data exploration. Qubole delivers a cloud-based, turnkey, self-service big data service that removes the complexity and reduces the cost of doing big data. It leverages Oracle Bare Metal Cloud’s next generation of scalable, inexpensive and performant compute, network and storage public cloud infrastructure to provide a solution that accelerates time to market and reduces the risk of your big data initiatives.

Introduction and HDInsight best practices

Ashish Thapliyal

This document discusses Azure HDInsight, a managed Apache Hadoop and Spark platform. It provides a secure environment for building data lakes in the cloud. Key capabilities include ingesting and analyzing data from various sources using technologies like Apache Spark, Hive, Kafka and HBase. It also discusses data storage options, performance, security features and tools for management and monitoring of HDInsight clusters.

Quark Virtualization Engine for Analytics

Rajat Venkatesh from Qubole presented on Quark, a virtualization engine for analytics. Quark uses a multi-store architecture to optimize queries using materialized views, predicate injection, and denormalized/sorted tables. It supports multiple SQL and storage engines. The roadmap includes improvements to the cost-based optimizer, support for OLAP cubes, and developing Quark as a service. Coordinates for the Quark GitHub and mailing list were provided.

IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing Hub

In-Memory Computing Summit

Today, many companies are faced with a huge quantity of data and a wide variety of tools with which to process it. This potentially allows for great opportunities to satisfy customers’ needs and bring user experience to the next level. However, in order to achieve this and provide a competitive solution, sophisticated and complex data processing is needed. Such processing can rarely be done with one tool or framework — a number of tools are often involved, each having prowess in a particular field of the processing pipeline. In this session, we will see the latest endeavors of Apache Ignite to integrate with other big data platforms and provide its in-memory computing strengths for data processing pipelines. In particular we will have a closer look at how it can be integrated and used with Apache Kafka and/or Flume, and outline several use scenarios.

Sparking up Data Engineering: Spark Summit East talk by Rohan Sharma

Learn about the Big Data Processing ecosystem at Netflix and how Apache Spark sits in this platform. I talk about typical data flows and data pipeline architectures that are used in Netflix and address how Spark is helping us gain efficiency in our processes. As a bonus – i’ll touch on some unconventional use-cases contrary to typical warehousing / analytics solutions that are being served by Apache Spark.

Large Scale Feature Aggregation Using Apache Spark with Pulkit Bhanot and Ami...

Aggregation based features account for a quarter of the several 1000s features used by the ML-based decisioning system by the Risk team at Uber. We observed several repetitive, cumbersome steps needed for onboarding a feature, every single time. Therefore, to accelerate developer velocity, and to enable Feature Engineering at scale, we decided to develop a generic spark based infrastructure to simplify the process to no more than a simple spec file, containing a parameterized query, along with some metadata on where the feature should be aggregated and stored. In the presentation, we will describe the architecture of the final solution, highlighting some of the advanced capabilities like backfill support and self-healing for correctness. We will showcase how, using data stored in Hive and using Spark, we developed a highly scalable solution to carry out feature aggregation in an incremental way. By dividing data aggregation responsibility across the realtime access layer, and the batch computation components, we ensured that only entities for which there is actual value changes are dispersed to our real-time access store (Cassandra). We will share how we did data modeling in Cassandra using its native capabilities such as counters, and how we worked around some of the limitations of Cassandra. We will also cover the details about the access service how we do different types of feature stitching together. How, based on our data model we were able to ensure that all the feature for an entity with the same aggregation window, were queried via a single query. Finally, we will cover some of the details on how these incremental aggregated features have enabled shorter turnaround times for the models using such features.

Efficiently Triaging CI Pipelines with Apache Spark: Mixing 52 Billion Events...

Continuous integration (CI) pipelines generate massive amounts of messy log data. At Pure Storage engineering, we run over 65,000 tests per day creating a large triage problem. Spark’s flexible computing platform allows us to write a single application for both streaming and batch jobs to understand the state of our CI pipeline. Spark indexes log data for real-time reporting (Streaming), uses Machine Learning for performance modeling and prediction (Batch job), and re-indexes old data for newly encoded patters (Batch job). Previous work on a mixed streaming and batch environment describes the options for persisting data and their trade-offs: 1) short interval buckets which hurts batch performance 2) long interval buckets which increases micro batch time windows 3) additional software on the background to compact the short interval buckets which adds complexity. This talk will go over how we use the filesystem metadata of our disaggregated compute and storage layers to write over half a million files per day of varied sizes from 52 Billion events and have efficient batch jobs without compaction that allow us to process over 40TB per hour. We will go over the challenges and best practices to achieve efficiency in this mixed environment scenarios.

Tachyon meetup San Francisco Oct 2014

This document outlines the agenda for a Tachyon Meetup in San Francisco. The agenda includes discussing the xPatterns architecture, BDAS++, demos of Tachyon internals and APIs, and lessons learned. BDAS++ refers to enhancements made to Tachyon to support Spark SQL and the Spark job server. Lessons learned focus on issues discovered like partial in-memory file storage bugs and best practices for Tachyon usage.

xPatterns ... beyond Hadoop (Spark, Shark, Mesos, Tachyon)

xPatterns is a big data analytics platform as a service that enables a rapid development of enterprise-grade analytical applications. It provides tools, api sets and a management console for building an ELT pipeline with data monitoring and quality gates, a data warehouse for ad-hoc and scheduled querying, analysis, model building and experimentation, tools for exporting data to NoSql and solrCloud cluster for real-time access through low-latency/high-throughput apis as well as dashboard and visualization api/tools leveraging the available data and models. In this presentation we will showcase one of the analytical applications build on top of xPatterns for our largest customer for that runs xPatterns in production on top a data warehouse consisting of several hundreds TB of medical, pharmacy and lab data records consisting of tens of billions of records. We will showcase the xPatterns components in the form of APIs and tools employed throughout the entire lifecycle of this application. The core of the presentation is the evolution of the infrastructure from the Hadoop/Hive stack to the new BDAS Spark, Shark, Mesos and Tachyon, with lessons learned and demos.

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Finding Key Influencers and Viral Topics in Twitter Networks Related to ISIS,...

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Paragon Science used a combination of network analysis, community detection, topic detection, sentiment analysis, and anomaly detection methods to find key influencers and viral topics in two recent Twitter data sets: one of 7.9 M tweets regarding ISIS, a second set consisting of more than 117 M tweets about the 2016 primary elections, and a third set of 7M tweets realted to Brexit. Paragon Science's patented dynamic anomaly detection technology is based on methods drawn from dynamical systems and chaos theory. In particular, we can calculate finite-time Lyapunov exponents from any time-dependent data stream to find the clusters of entities that are behaving most chaotically compared to the rest of the data set. Because we do not have to specify normal vs. abnormal behavior in advance, no machine learning per se is required. In a robust fashion that is tolerant of missing or erroneous data, we can identify the "unknown unknowns" that can represent threats to be mitigate or opportunities to be seized. To date, our technique has been applied successfully to a broad range of industry verticals, including healthcare data (Advisory Board Company), web user behavior data (Vast), mobile phone data (Place IQ), vehicle pricing analytics (Digital Motorworks/CDK Global), online coupon data (RetailMeNot), email monitoring for patent law cases, and social media monitoring.

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This document provides an introduction and overview of Apache NiFi 1.11.4. It discusses new features such as improved support for partitions in Azure Event Hubs, encrypted repositories, class loader isolation, and support for IBM MQ and the Hortonworks Schema Registry. It also summarizes new reporting tasks, controller services, and processors. Additional features include JDK 11 support, encrypted repositories, and parameter improvements to support CI/CD. The document provides examples of using NiFi with Docker, Kubernetes, and in the cloud. It concludes with useful links for additional NiFi resources.

【旧版】Oracle Cloud Infrastructure：サービス概要のご紹介 [2020年2月版]

※最新資料はこちら https://www.slideshare.net/oracle4engineer/oracle-cloud-infrastructure-20204 ------------------------------ https://blogs.oracle.com/oracle4engineer/column_cloud_oci Oracle Cloud Infrastructureは、オンプレミスからの大規模ワークロード移行に完全対応する次世代インフラ基盤です。高性能と高セキュリティを備えたインフラ基盤で、仮想マシン、ベアメタルマシン、オブジェクトストレージ、Database Cloud Service、Exadata Cloud Service等の各種サービスを提供します。

【旧版】Oracle Exadata Cloud Service：サービス概要のご紹介

※最新資料はこちら https://www.slideshare.net/oracle4engineer/oracle-exadata-cloud-service-20205 ------------------------------ https://blogs.oracle.com/oracle4engineer/entry/column_cloud_exacs Oracle Exadata Cloud Serviceは、世界No1のデータベースとExadataを組み合わせた、世界でもっとも最適なクラウド・データベース・プラットフォームです。もっともパワフルなデータベース・プラットフォームに、パブリック・クラウドの容易性とコスト優位性というメリットを加えています。

Caching and JCache with Greg Luck 18.02.16

Comsysto Reply GmbH

Caching is a frequently used and misused technique for speeding up performance, off-loading non-scalable or expensive infrastructure, scaling systems and coping with large processing peaks. In this talk Greg introduces you to caching and highlights the key caching theory points that you should consider in applying caching. Then we take a comprehensive look at the new JCache standard standardises Java usage of caching.

【旧版】Oracle Exadata Cloud Service：サービス概要のご紹介 [2020年8月版]

※最新資料はこちら https://www.slideshare.net/oracle4engineer/oracle-exadata-cloud-service-20214 ------------------------------ https://blogs.oracle.com/oracle4engineer/entry/column_cloud_exacs Oracle Exadata Cloud Serviceは、世界No1のデータベースとExadataを組み合わせた、世界でもっとも最適なクラウド・データベース・プラットフォームです。もっともパワフルなデータベース・プラットフォームに、パブリック・クラウドの容易性とコスト優位性というメリットを加えています。

Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...

Terraform - Taming Modern Clouds

Nic Jackson

Running your Java EE 6 applications in the Cloud @ Silicon Valley Code Camp 2010

Arun Gupta

Arun Gupta presented on running Java EE 6 applications in the cloud. He discussed Java EE 6 support on various cloud platforms including Amazon, RightScale, Elastra, and Joyent. He also compared features of different cloud vendors and how Java EE can evolve to better support cloud computing. Gupta concluded that Java EE 6 applications can easily be deployed to various clouds and GlassFish provides a feature-rich implementation of Java EE 6.

Healthcare Claim Reimbursement using Apache Spark

The document discusses rewriting a claims reimbursement system using Spark. It describes how Spark provides better performance, scalability and cost savings compared to the previous Oracle-based system. Key points include using Spark for ETL to load data into a Delta Lake data lake, implementing the business logic in a reusable Java library, and seeing significant increases in processing volumes and speeds compared to the prior system. Challenges and tips for adoption are also provided.

Five essential new enhancements in azure HDnsight

Ashish Thapliyal

This document discusses features of Apache Spark on Azure HDInsight including a new Spark IO cache that provides significant performance improvements of up to 9x for Spark queries. It also discusses other HDInsight features like Hive LLAP for interactive querying, data analytics templates, and tools for Spark job debugging and diagnosis. Azure HDInsight is presented as a secure, managed Hadoop and Spark cloud platform for building data lakes on Azure.

How We Used Cassandra/Solr to Build Real-Time Analytics Platform

DataStax Academy

This session will discuss how Cassandra/Solr can be used to create real-time analytics platform – jKool. jKool provides an in-memory analysis of time-series data, automatically performing sequencing, correlation, grouping, enriching, synchronizing, computing, querying and displaying data streams. The session will discuss architecture, challenges and approaches taken to create a real-time analytics platform on top of open source big data analytics platforms: Cassandra, Solr, Kafka & Spark.

Apache Deep Learning 101 - ApacheCon Montreal 2018 v0.31

Timothy Spann

【旧版】Oracle Autonomous Database：サービス概要のご紹介 [2020年8月版]