Cloudera Enterprise with IBM the Modern Platform for Machine Learning and Analytics, Optimized for the Cloud One Platform
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Netapp Solutions for Hadoop Reference Architecture: Cloudera Faiz Abidi (Netapp) and Udai Potluri (Cloudera) June 2018 | WP-7217
White Paper NetApp Solutions for Hadoop Reference Architecture: Cloudera Faiz Abidi (NetApp) and Udai Potluri (Cloudera) June 2018 | WP-7217 In partnership with Abstract There has been an exponential growth in data over the past decade and analyzing huge amounts of data in a reasonable time can be a challenge. Apache Hadoop is an open- source tool that can help your organization quickly mine big data and extract meaningful patterns from it. However, enterprises face several technical challenges when deploying Hadoop, specifically in the areas of cluster availability, operations, and scaling. NetApp® has developed a reference architecture with Cloudera to deliver a solution that overcomes some of these challenges so that businesses can ingest, store, and manage big data with greater reliability and scalability and with less time spent on operations and maintenance. This white paper discusses a flexible, validated, enterprise-class Hadoop architecture that is based on NetApp E-Series storage using Cloudera’s Hadoop distribution. TABLE OF CONTENTS 1 Introduction ........................................................................................................................................... 4 1.1 Big Data ..........................................................................................................................................................4 1.2 Hadoop Overview ...........................................................................................................................................4 2 NetApp E-Series -
Lenovo Big Data Reference Design for Cloudera Data Platform on Thinksystem Servers
Lenovo Big Data Reference Design for Cloudera Data Platform on ThinkSystem Servers Last update: 30 June 2021 Version 2.0 Reference architecture for Solution based on the Cloudera Data Platform with ThinkSystem servers Apache Hadoop and Apache Spark Deployment considerations for Solution design matched to scalable racks including detailed Cloudera Data Platform architecture validated bills of material Xiaotong Jiang Xifa Chen Ajay Dholakia Lenovo Big Data Reference Design for Cloudera Data Platform on ThinkSystem Servers 1 Table of Contents 1 Introduction ............................................................................................... 4 2 Business problem and business value ................................................... 5 3 Requirements ............................................................................................ 7 Functional Requirements ......................................................................................... 7 Non-functional Requirements................................................................................... 7 4 Architectural Overview ............................................................................. 8 Cloudera Data Platform ........................................................................................... 8 CDP Private Cloud ................................................................................................... 8 5 Component Model .................................................................................. 10 Cloudera Components .......................................................................................... -
Vulnerability Summary for the Week of July 10, 2017
Vulnerability Summary for the Week of July 10, 2017 The vulnerabilities are based on the CVE vulnerability naming standard and are organized according to severity, determined by the Common Vulnerability Scoring System (CVSS) standard. The division of high, medium, and low severities correspond to the following scores: High - Vulnerabilities will be labeled High severity if they have a CVSS base score of 7.0 - 10.0 Medium - Vulnerabilities will be labeled Medium severity if they have a CVSS base score of 4.0 - 6.9 Low - Vulnerabilities will be labeled Low severity if they have a CVSS base score of 0.0 - 3.9 High Vulnerabilities Primary CVSS Source & Patch Vendor -- Product Description Published Score Info The Struts 1 plugin in Apache CVE-2017-9791 Struts 2.3.x might allow CONFIRM remote code execution via a BID(link is malicious field value passed external) in a raw message to the 2017-07- SECTRACK(link apache -- struts ActionMessage. 10 7.5 is external) A vulnerability in the backup and restore functionality of Cisco FireSIGHT System Software could allow an CVE-2017-6735 authenticated, local attacker to BID(link is execute arbitrary code on a external) targeted system. More SECTRACK(link Information: CSCvc91092. is external) cisco -- Known Affected Releases: 2017-07- CONFIRM(link firesight_system_software 6.2.0 6.2.1. 10 7.2 is external) A vulnerability in the installation procedure for Cisco Prime Network Software could allow an authenticated, local attacker to elevate their privileges to root privileges. More Information: CSCvd47343. Known Affected Releases: CVE-2017-6732 4.2(2.1)PP1 4.2(3.0)PP6 BID(link is 4.3(0.0)PP4 4.3(1.0)PP2. -
Chainsys-Platform-Technical Architecture-Bots
Technical Architecture Objectives ChainSys’ Smart Data Platform enables the business to achieve these critical needs. 1. Empower the organization to be data-driven 2. All your data management problems solved 3. World class innovation at an accessible price Subash Chandar Elango Chief Product Officer ChainSys Corporation Subash's expertise in the data management sphere is unparalleled. As the creative & technical brain behind ChainSys' products, no problem is too big for Subash, and he has been part of hundreds of data projects worldwide. Introduction This document describes the Technical Architecture of the Chainsys Platform Purpose The purpose of this Technical Architecture is to define the technologies, products, and techniques necessary to develop and support the system and to ensure that the system components are compatible and comply with the enterprise-wide standards and direction defined by the Agency. Scope The document's scope is to identify and explain the advantages and risks inherent in this Technical Architecture. This document is not intended to address the installation and configuration details of the actual implementation. Installation and configuration details are provided in technology guides produced during the project. Audience The intended audience for this document is Project Stakeholders, technical architects, and deployment architects The system's overall architecture goals are to provide a highly available, scalable, & flexible data management platform Architecture Goals A key Architectural goal is to leverage industry best practices to design and develop a scalable, enterprise-wide J2EE application and follow the industry-standard development guidelines. All aspects of Security must be developed and built within the application and be based on Best Practices. -
Groups and Activities Report 2017
Groups and Activities Report 2017 ISBN 978-92-9083-491-5 This report is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 2 | Page CERN IT Department Groups and Activities Report 2017 CONTENTS GROUPS REPORTS 2017 Collaborations, Devices & Applications (CDA) Group ............................................................................. 6 Communication Systems (CS) Group .................................................................................................... 11 Compute & Monitoring (CM) Group ..................................................................................................... 16 Computing Facilities (CF) Group ........................................................................................................... 20 Databases (DB) Group ........................................................................................................................... 23 Departmental Infrastructure (DI) Group ............................................................................................... 27 Storage (ST) Group ................................................................................................................................ 28 ACTIVITIES AND PROJECTS REPORTS 2017 CERN openlab ........................................................................................................................................ 34 CERN School of Computing (CSC) ......................................................................................................... -
Sphinx: Empowering Impala for Efficient Execution of SQL Queries
Sphinx: Empowering Impala for Efficient Execution of SQL Queries on Big Spatial Data Ahmed Eldawy1, Ibrahim Sabek2, Mostafa Elganainy3, Ammar Bakeer3, Ahmed Abdelmotaleb3, and Mohamed F. Mokbel2 1 University of California, Riverside [email protected] 2 University of Minnesota, Twin Cities {sabek,mokbel}@cs.umn.edu 3 KACST GIS Technology Innovation Center, Saudi Arabia {melganainy,abakeer,aothman}@gistic.org Abstract. This paper presents Sphinx, a full-fledged open-source sys- tem for big spatial data which overcomes the limitations of existing sys- tems by adopting a standard SQL interface, and by providing a high efficient core built inside the core of the Apache Impala system. Sphinx is composed of four main layers, namely, query parser, indexer, query planner, and query executor. The query parser injects spatial data types and functions in the SQL interface of Sphinx. The indexer creates spa- tial indexes in Sphinx by adopting a two-layered index design. The query planner utilizes these indexes to construct efficient query plans for range query and spatial join operations. Finally, the query executor carries out these plans on big spatial datasets in a distributed cluster. A system prototype of Sphinx running on real datasets shows up-to three orders of magnitude performance improvement over plain-vanilla Impala, Spa- tialHadoop, and PostGIS. 1 Introduction There has been a recent marked increase in the amount of spatial data produced by several devices including smart phones, space telescopes, medical devices, among others. For example, space telescopes generate up to 150 GB weekly spatial data, medical devices produce spatial images (X-rays) at 50 PB per year, NASA satellite data has more than 1 PB, while there are 10 Million geo- tagged tweets issued from Twitter every day as 2% of the whole Twitter firehose. -
Apache Spot: a More Effective Approach to Cyber Security
SOLUTION BRIEF Data Center: Big Data and Analytics Security Apache Spot: A More Effective Approach to Cyber Security Apache Spot uses machine learning over billions of network events to discover and speed remediation of attacks to minutes instead of months—streamlining resources and cutting risk exposure Executive Summary This solution brief describes how to solve business challenges No industry is immune to cybercrime. The global “hard” cost of cybercrime has and enable digital transformation climbed to USD 450 billion annually; the median cost of a cyberattack has grown by through investment in innovative approximately 200 percent over the last five years,1 and the reputational damage can technologies. be even greater. Intel is collaborating with cyber security industry leaders such as If you are responsible for … Accenture, eBay, Cloudwick, Dell, HP, Cloudera, McAfee, and many others to develop • Business strategy: You will better big data solutions with advanced machine learning to transform how security threats understand how a cyber security are discovered, analyzed, and corrected. solution will enable you to meet your business outcomes. Apache Spot is a powerful data aggregation tool that departs from deterministic, • Technology decisions: You will learn signature-based threat detection methods by collecting disparate data sources into a how a cyber security solution works data lake where self-learning algorithms use pattern matching from machine learning to deliver IT and business value. and streaming analytics to find patterns of outlier network behavior. Companies use Apache Spot to analyze billions of events per day—an order of magnitude greater than legacy security information and event management (SIEM) systems. -
CDP DATA CENTER 7.1 Laurent Edel : Solution Engineer Jacques Marchand : Solution Engineer Mael Ropars : Principal Solution Engineer
CDP DATA CENTER 7.1 Laurent Edel : Solution Engineer Jacques Marchand : Solution Engineer Mael Ropars : Principal Solution Engineer 30 Juin 2020 SPEAKERS • © 2019 Cloudera, Inc. All rights reserved. AGENDA • CDP DATA CENTER OVERVIEW • DETAILS ABOUT MAJOR COMPONENTS • PATH TO CDP DC && SMART MIGRATION • Q/A © 2019 Cloudera, Inc. All rights reserved. CLOUDERA DATA PLATFORM © 2020 Cloudera, Inc. All rights reserved. 4 ARCHITECTURE CIBLE : ENTERPRISE DATA CLOUD CDP Cloud Public CDP On-Prem (platform-as-a-service) (installable software) © 2020 Cloudera, Inc. All rights reserved. 5 CDP DATA CENTER OVERVIEW CDP Data Center (installable software) NEW CDP Data Center features include: Cloudera Manager • High-performance SQL analytics • Real-time stream processing, analytics, and management • Fine-grained security, enterprise metadata, and scalable data lineage • Support for object storage (tech preview) • Single pane of glass for management - multi-cluster support Enterprise analytics and data management platform, built for hybrid cloud, optimized for bare metal and ready for private cloud Cloudera Runtime © 2020 Cloudera, Inc. All rights reserved. 6 A NEW OPEN SOURCE DISTRIBUTION FOR BETTER CAPABILITY Cloudera Runtime - created from the best of CDH and HDP Deprecate competitive Merge overlapping Keep complementary Upgrade shared technologies technologies technologies technologies © 2019 Cloudera, Inc. All rights reserved. 7 COMPONENT LIST CDP Data Center 7.1(May) 2020 • Cloudera Manager 7.1 • HBase 2.2 • Key HSM 7.1 • Kafka Schema Registry 0.8 -
Kyuubi Release 1.3.0 Kent
Kyuubi Release 1.3.0 Kent Yao Sep 30, 2021 USAGE GUIDE 1 Multi-tenancy 3 2 Ease of Use 5 3 Run Anywhere 7 4 High Performance 9 5 Authentication & Authorization 11 6 High Availability 13 6.1 Quick Start................................................ 13 6.2 Deploying Kyuubi............................................ 47 6.3 Kyuubi Security Overview........................................ 76 6.4 Client Documentation.......................................... 80 6.5 Integrations................................................ 82 6.6 Monitoring................................................ 87 6.7 SQL References............................................. 94 6.8 Tools................................................... 98 6.9 Overview................................................. 101 6.10 Develop Tools.............................................. 113 6.11 Community................................................ 120 6.12 Appendixes................................................ 128 i ii Kyuubi, Release 1.3.0 Kyuubi™ is a unified multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown in the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi provides the following features: USAGE GUIDE 1 Kyuubi, Release 1.3.0 2 USAGE GUIDE CHAPTER ONE MULTI-TENANCY Kyuubi supports the end-to-end multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server already exists. 1. Supports multi-client concurrency and authentication 2. Supports one Spark application per account(SPA). 3. Supports QUEUE/NAMESPACE Access Control Lists (ACL) 4. -
Yellowbrick Versus Apache Impala
TECHNICAL BRIEF Yellowbrick Versus Apache Impala The Apache Hadoop technology stack is designed Impala were developed to provide this support. The to process massive data sets on commodity problem is that these technologies are a SQL ab- servers, using parallelism of disks, processors, and straction layer and do not operate optimally: while memory across a distributed file system. Apache they allow users to execute familiar SQL statements, Impala (available commercially as Cloudera Data they do not provide high performance. Warehouse) is a SQL-on-Hadoop solution that claims performant SQL operations on large For example, classic database optimizations for Hadoop data sets. storage and data layout that are common in the MPP warehouse world have not been applied in Hadoop achieves optimal rotational (HDD) disk per- the SQL-on-Hadoop world. Although Impala has formance by avoiding random access and processing optimizations to enhance performance and capabil- large blocks of data in batches. This makes it a good ities over Hive, it must read data from flat files on the solution for workloads, such as recurring reports, Hadoop Distributed File System (HDFS), which limits that commonly run in batches and have few or no its effectiveness. runtime updates. However, performance degrades rapidly when organizations start to add modern Architecture comparison: enterprise data warehouse workloads, such as: Impala versus Yellowbrick > Ad hoc, interactive queries for investigation or While Impala is a SQL layer on top of HDFS, the fine-grained insight Yellowbrick hybrid cloud data warehouse is an an- alytic MPP database designed from the ground up > Supporting more concurrent users and more- to support modern enterprise workloads in hybrid diverse job and query types and multi-cloud environments. -
Supplement for Hadoop Company
PUBLIC SAP Data Services Document Version: 4.2 Support Package 12 (14.2.12.0) – 2020-02-06 Supplement for Hadoop company. All rights reserved. All rights company. affiliate THE BEST RUN 2020 SAP SE or an SAP SE or an SAP SAP 2020 © Content 1 About this supplement........................................................4 2 Naming Conventions......................................................... 5 3 Apache Hadoop.............................................................9 3.1 Hadoop in Data Services....................................................... 11 3.2 Hadoop sources and targets.....................................................14 4 Prerequisites to Data Services configuration...................................... 15 5 Verify Linux setup with common commands ...................................... 16 6 Hadoop support for the Windows platform........................................18 7 Configure Hadoop for text data processing........................................19 8 Setting up HDFS and Hive on Windows...........................................20 9 Apache Impala.............................................................22 9.1 Connecting Impala using the Cloudera ODBC driver ................................... 22 9.2 Creating an Apache Impala datastore and DSN for Cloudera driver.........................24 10 Connect to HDFS...........................................................26 10.1 HDFS file location objects......................................................26 HDFS file location object options...............................................27 -
Chapter 2 Introduction to Big Data Technology
Chapter 2 Introduction to Big data Technology Bilal Abu-Salih1, Pornpit Wongthongtham2 Dengya Zhu3 , Kit Yan Chan3 , Amit Rudra3 1The University of Jordan 2 The University of Western Australia 3 Curtin University Abstract: Big data is no more “all just hype” but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V’s to up 10V’s of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference. Keywords Big data, 3V of Big data, Cloud Computing, Data Lake, Enterprise Data Centre, PaaS, IaaS, SaaS, Hadoop, Spark, HBase, Information retrieval, Solr 2.1 Introduction The ability to exploit the ever-growing amounts of business-related data will al- low to comprehend what is emerging in the world. In this context, Big Data is one of the current major buzzwords [1]. Big Data (BD) is the technical term used in reference to the vast quantity of heterogeneous datasets which are created and spread rapidly, and for which the conventional techniques used to process, analyse, retrieve, store and visualise such massive sets of data are now unsuitable and inad- equate. This can be seen in many areas such as sensor-generated data, social media, uploading and downloading of digital media.