DOCSLIB.ORG

Best Practices for Big Data : Visualizing Billions of Rows with Rapid Response Times “Big Data Analytics at the Speed of Thought”

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Best Practices for Big Data : Visualizing Billions of Rows with Rapid Response Times “Big Data Analytics at the Speed of Thought” Best Practices for Big Data : Visualizing billions of rows with rapid response times “Big Data Analytics at the Speed of Thought” Anthony Maresco 1 Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved . Safe Harbor Notice This presentation describes features that are under development by MicroStrategy. The objective of this presentation is to provide insight into MicroStrategy’s technology direction. The functionalities described herein may or may not be released as shown. This presentation contains statements that may constitute “forward-looking statements” for purposes of the safe harbor provisions under the Private Securities Litigation Reform Act of 1995, including descriptions of technology and product features that are under development and estimates of future business prospects. Forward-looking statements inherently involve risks and uncertainties that could cause actual results of MicroStrategy Incorporated and its subsidiaries (collectively, the “Company”) to differ materially from the forward-looking statements. Factors that could contribute to such differences include: the Company’s ability to meet product development goals while aligning costs with anticipated revenues; the Company’s ability to develop, market, and deliver on a timely and cost-effective basis new or enhanced offerings that respond to technological change or new customer requirements; the extent and timing of market acceptance of the Company’s new offerings; continued acceptance of the Company’s other products in the marketplace; the timing of significant orders; competitive factors; general economic conditions; and other risks detailed in the Company’s Form 10-Q for the three months ended September 30, 2018 and other periodic reports filed with the Securities and Exchange Commission. By making these forward-looking statements, the Company undertakes no obligation to update these statements for revisions or changes after the date of this presentation. Copyright © 2017Copyright MicroStrategy © 2019 MicroStrategyIncorporated. Incorporated. All All Rights Rights Reserved Reserved. Topics • Our Performance Scenario and Objective • First Order Basic Practices • A Note on Benchmarks • Hadoop OLAP Type Tools • Intelligent Cubes for Concurrency • Last Things • Summary • Q&A Special Thanks To…. HF Chadeisson Principal Solutions Architect 4 Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved . Our Performance Scenario and Objectives Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved . Objective • Compare performance impact of changes with a simple drill-down scenario using TPC-H SSB benchmark data • Progress from slow to fast using a combination of Hadoop features and MicroStrategy features • “Speed of Thought” Big Data Analytics with billions of rows • Get an introduction of new developments that improve the picture • Look at how some of these capabilities optimize Dossier interactive performance Minimize this…. Johannes Kepler Johannes Kepler writing to Galileo Galilei in 1609 “Ships and sails proper for the heavenly air should be fashioned. Then there will also be people, who do not shrink from the dreary vastness of space.” Giordano Bruno was burned at the stake in 1600 for declaring the universe was infinite with countless suns and earths. Performance – Concurrency - Structure • “Initially, having any way to use SQL against the Hadoop data was the goal, now there is an increasing requirement to connect business users … and give them the performance they expect with high levels of concurrency.” • “Note that to meet this requirement, it is likely that users will need to have structured data stored in Hadoop (along with the original unstructured data), as good performance is more likely if a transformation is done once rather than per-query” https://dzone.com/articles/sql-and-hadoop déjà vu All Over Again • ROLAP vs. MOLAP • In-memory • Columnar • Indexing • Pre-Aggregation • MPP Features added to the data stores • And more… • All taken to the next level to deal with Big Data Live Access to Data Sources with Unparalleled Performance Quickly generate multi-pass SQL and leverage push down functionality for optimized performance MicroStrategy Analytics Platform Dynamic Sourcing Generate Multipass SQL Pushdown architecture MicroStrategy lets users seamlessly run queries MicroStrategy is able to easily and quickly generate multi-pass MicroStrategy leverages the database to its full extent by pushing and dynamically drill across multiple sources. The SQL queries to provide greater analytical power and minimize data joins and analytic calculations to the database when possible. server is able to auto-recognize tasks and the amount of data that is pulled back to the mid-tier. The Every connector is optimized for high performance by pushing intelligently direct queries against in-memory sophisticated SQL engine can deliver high performance for the down functions to leverage the power of the database in cubes when possible. most complex SQL computations. conjunction with the server. Cube Cube Database 11 Deliver a single version of the truth on top of your big data Leverage the unified metadata repository to design reusable objects for rapid development, governance and scale Clients Desktop Web iPhone iPad Android Applications SCALABLE EXTENSIBLE Documents Reusable Objects Data Model CENTRALIZED CENTRALIZED ADMINISTRATION SECURITY Relational Hadoop & Cloud- Enterprise Personal or Databases Big Data Based Data Applications Departmental Enterprise Data & Business Applications What is “Speed of Thought” Analytics ? • 3 seconds ? • 7 seconds ? • 10 seconds ? • 15 seconds ? • 30 seconds ? • More ??? • All have been quoted! Sometimes you have to wait….but you can set and manage SLA’s for X% of your workload…and evolve… Data – Hardware – Software - Facts • TPC-H SSB at various scales up to 1Terabyte • ~6 Billion rows in the largest fact table – also 1.2 and 3.3 billion tests • Add in semi-structured and data mining • 17 Billion Row Demo by Indexima • 30 B Row demo provided by Kyvos during the conference • 1.2 B Row Taxi used to show Dossier performance with several techniques and products referenced • 10 Node clusters with 8 data/worker nodes • Worker nodes have 8 vCPU and 61 GB • Use ORC file types • Hortonworks HDP 2.6.3 • 10 Node cluster with HDP 3.1.0 provided preliminary information Considerations • Plenty of new turf and it’s continually changing • SQL is still key as NoSQL or NewSQL • Add in semi-structured and data mining • SQL on Hadoop products are evolving quickly with rapid increases in functionality and performance. • Performance can double, triple or more in some cases in 6 month periods. • Ultimate decisions depend on testing with your data • Schema on read is key for iteration and flexibility in the back end • Schemas are still important to operationalize analytics to large number of users • Use of aggregation, agg-aware sql generation, caching, cubes, and dynamic sourcing are keys to performance and scalability • Memory Caching is a key infrastructure component • Agility means “Everything is finished…..Nothing is finished…” Prescription • Use the tools in the platform • Use MicroStrategy to make them better • Add additional tools & components where warranted • Use MicroStrategy to make them better • Use of aggregation, aggregation-aware sql generation, caching, cubes, and dynamic sourcing are tools for performance and scalability • Ensure you have the resources, knowledge, and time allocation for initial and ongoing tuning – sizing – monitoring - benchmarking • With MicroStrategy you get performance, scalability, governance, and concurrency to securely distribute insight with Big Data Analytics to 10’s of 1000’s of people • Additional Big Data Techniques and Tools are required when volume – velocity – variety get to a tipping point First-Order Best Practices Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved . Preliminaries : Minimize Joins TPC-H Schema SSB Schema LINENUMBER • SSB Version of TPC-H eliminates joins but is missing lookup tables • Snowflake is preferred with both dimension and lookup tables Preliminaries : Partitioning • Hive and Impala recommend partitioning to minimize scanned rows • This requires including a filter based on the partition in every query • It is important to pick partition columns so there are the right number of partitions • Partitioning can have other side effects • In an ad-hoc environment, you may not always be using the partitioning filter • In this study, partitioning was not used to see the relative impact of various actions • Bucketing can also be used in conjunction with Partitioning Preliminaries : Statistics • Always collect statistics on tables and columns in order to leverage cost based optimization • Example for Table ANALYZE TABLE table1 COMPUTE STATISTICS; • Example for Columns ANALYZE TABLE table1 COMPUTE STATISTICS FOR COLUMNS; Preliminaries : Optimized File Types • Parquet • ORC • AVRO Use the Latest Versions and Features • Example: move from HiveServer2 to HiveServer2 Interactive LLAP • At 550 Scale – 3.3 Billion Rows • Times are mm:ss Hive Server 2 Hive Server 2 Interactive LLAP 7:58 0:25 6:01 0:25 5:41 0:26 SQL and Row Counts select a14.d_year d_year, Table Rows a13.c_region c_region, a12.p_category p_category, lineorders 5,999,989,709 a12.p_brand1 p_brand1, dates 2556 a12.p_mfgr p_mfgr, sum(a11.lo_revenue) WJXBFS1 customers 30,000,000 from lineorder a11 parts 200,000,000 join part a12 on (a11.lo_partkey = a12.p_partkey) Supplier 2,000,000 join
Load more

Recommended publications
  • Java Linksammlung
    JAVA LINKSAMMLUNG LerneProgrammieren.de - 2020 Java einfach lernen (klicke hier) JAVA LINKSAMMLUNG INHALTSVERZEICHNIS Build ........................................................................................................................................................... 4 Caching ....................................................................................................................................................... 4 CLI ............................................................................................................................................................... 4 Cluster-Verwaltung .................................................................................................................................... 5 Code-Analyse ............................................................................................................................................. 5 Code-Generators ........................................................................................................................................ 5 Compiler ..................................................................................................................................................... 6 Konfiguration ............................................................................................................................................. 6 CSV ............................................................................................................................................................. 6 Daten-Strukturen
    [Show full text]
  • Declarative Languages for Big Streaming Data a Database Perspective
    Tutorial Declarative Languages for Big Streaming Data A database Perspective Riccardo Tommasini Sherif Sakr University of Tartu Unversity of Tartu [email protected] [email protected] Emanuele Della Valle Hojjat Jafarpour Politecnico di Milano Confluent Inc. [email protected] [email protected] ABSTRACT sources and are pushed asynchronously to servers which are The Big Data movement proposes data streaming systems to responsible for processing them [13]. tame velocity and to enable reactive decision making. However, To facilitate the adoption, initially, most of the big stream approaching such systems is still too complex due to the paradigm processing systems provided their users with a set of API for shift they require, i.e., moving from scalable batch processing to implementing their applications. However, recently, the need for continuous data analysis and pattern detection. declarative stream processing languages has emerged to simplify Recently, declarative Languages are playing a crucial role in common coding tasks; making code more readable and main- fostering the adoption of Stream Processing solutions. In partic- tainable, and fostering the development of more complex appli- ular, several key players introduce SQL extensions for stream cations. Thus, Big Data frameworks (e.g., Flink [9], Spark [3], 1 processing. These new languages are currently playing a cen- Kafka Streams , and Storm [19]) are starting to develop their 2 3 4 tral role in fostering the stream processing paradigm shift. In own SQL-like approaches (e.g., Flink SQL , Beam SQL , KSQL ) this tutorial, we give an overview of the various languages for to declaratively tame data velocity. declarative querying interfaces big streaming data.
    [Show full text]
  • Apache Apex: Next Gen Big Data Analytics
    Apache Apex: Next Gen Big Data Analytics Thomas Weise <[email protected]> @thweise PMC Chair Apache Apex, Architect DataTorrent Apache Big Data Europe, Sevilla, Nov 14th 2016 Stream Data Processing Data Delivery Transform / Analytics Real-time visualization, … Declarative SQL API Data Beam Beam SAMOA Operator SAMOA DAG API Sources Library Events Logs Oper1 Oper2 Oper3 Sensor Data Social Databases CDC (roadmap) 2 Industries & Use Cases Financial Services Ad-Tech Telecom Manufacturing Energy IoT Real-time Call detail record customer facing (CDR) & Supply chain Fraud and risk Smart meter Data ingestion dashboards on extended data planning & monitoring analytics and processing key performance record (XDR) optimization indicators analysis Understanding Reduce outages Credit risk Click fraud customer Preventive & improve Predictive assessment detection behavior AND maintenance resource analytics context utilization Packaging and Improve turn around Asset & Billing selling Product quality & time of trade workforce Data governance optimization anonymous defect tracking settlement processes management customer data HORIZONTAL • Large scale ingest and distribution • Enforcing data quality and data governance requirements • Real-time ELTA (Extract Load Transform Analyze) • Real-time data enrichment with reference data • Dimensional computation & aggregation • Real-time machine learning model scoring 3 Apache Apex • In-memory, distributed stream processing • Application logic broken into components (operators) that execute distributed in a cluster •
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Informatica 10.2 Hotfix 2 Release Notes April 2019
    Informatica 10.2 HotFix 2 Release Notes April 2019 © Copyright Informatica LLC 1998, 2020 Contents Installation and Upgrade......................................................................... 3 Informatica Upgrade Paths......................................................... 3 Upgrading from 9.6.1............................................................. 4 Upgrading from Version 10.0, 10.1, 10.1.1, and 10.1.1 HotFix 1.............................. 4 Upgrading from Version 10.1.1 HF2.................................................. 5 Upgrading from 10.2.............................................................. 6 Related Links ................................................................... 7 Verify the Hadoop Distribution Support................................................ 7 Hotfix Installation and Rollback..................................................... 8 10.2 HotFix 2 Fixed Limitations and Closed Enhancements........................................ 17 Analyst Tool Fixed Limitations and Closed Enhancements (10.2 HotFix 2).................... 17 Application Service Fixed Limitations and Closed Enhancements (10.2 HotFix 2)............... 17 Command Line Programs Fixed Limitations and Closed Enhancements (10.2 HotFix 2).......... 17 Developer Tool Fixed Limitations and Closed Enhancements (10.2 HotFix 2).................. 18 Informatica Connector Toolkit Fixed Limitations and Closed Enhancements (10.2 HotFix 2) ...... 18 Mappings and Workflows Fixed Limitations (10.2 HotFix 2)............................... 18 Metadata
    [Show full text]
  • 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.
    [Show full text]
  • HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
    Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo...........................................................................................................................................................
    [Show full text]
  • Apache Calcite: a Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
    Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources Edmon Begoli Jesús Camacho-Rodríguez Julian Hyde Oak Ridge National Laboratory Hortonworks Inc. Hortonworks Inc. (ORNL) Santa Clara, California, USA Santa Clara, California, USA Oak Ridge, Tennessee, USA [email protected] [email protected] [email protected] Michael J. Mior Daniel Lemire David R. Cheriton School of University of Quebec (TELUQ) Computer Science Montreal, Quebec, Canada University of Waterloo [email protected] Waterloo, Ontario, Canada [email protected] ABSTRACT argued that specialized engines can offer more cost-effective per- Apache Calcite is a foundational software framework that provides formance and that they would bring the end of the “one size fits query processing, optimization, and query language support to all” paradigm. Their vision seems today more relevant than ever. many popular open-source data processing systems such as Apache Indeed, many specialized open-source data systems have since be- Hive, Apache Storm, Apache Flink, Druid, and MapD. Calcite’s ar- come popular such as Storm [50] and Flink [16] (stream processing), chitecture consists of a modular and extensible query optimizer Elasticsearch [15] (text search), Apache Spark [47], Druid [14], etc. with hundreds of built-in optimization rules, a query processor As organizations have invested in data processing systems tai- capable of processing a variety of query languages, an adapter ar- lored towards their specific needs, two overarching problems have chitecture designed for extensibility, and support for heterogeneous arisen: data models and stores (relational, semi-structured, streaming, and • The developers of such specialized systems have encoun- geospatial). This flexible, embeddable, and extensible architecture tered related problems, such as query optimization [4, 25] is what makes Calcite an attractive choice for adoption in big- or the need to support query languages such as SQL and data frameworks.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • Hortonworks Data Platform for Enterprise Data Lakes Delivers Robust, Big Data Analytics That Accelerate Decision Making and Innovation
    IBM Europe Software Announcement ZP18-0220, dated March 20, 2018 Hortonworks Data Platform for Enterprise Data Lakes delivers robust, big data analytics that accelerate decision making and innovation Table of contents 1 Overview 5 Technical information 2 Key prerequisites 6 Ordering information 2 Planned availability date 7 Terms and conditions 2 Description 9 Prices 5 Program number 10 Announcement countries 5 Publications 10 Corrections Overview Hortonworks Data Platform is an enterprise ready open source Apache Hadoop distribution based on a centralized architecture supported by YARN. Hortonworks Data Platform is designed to address the needs of data at rest, power real-time customer applications, and deliver big data analytics that can help accelerate decision making and innovation. The official Apache versions for Hortonworks Data Platform V2.6.4 include: • Apache Accumulo 1.7.0 • Apache Atlas 0.8.0 • Apache Calcite 1.2.0 • Apache DataFu 1.3.0 • Apache Falcon 0.10.0 • Apache Flume 1.5.2 • Apache Hadoop 2.7.3 • Apache HBase 1.1.2 • Apache Hive 1.2.1 • Apache Hive 2.1.0 • Apache Kafka 0.10.1 • Apache Knox 0.12.0 • Apache Mahout 0.9.0 • Apache Oozie 4.2.0 • Apache Phoenix 4.7.0 • Apache Pig 0.16.0 • Apache Ranger 0.7.0 • Apache Slider 0.92.0 • Apache Spark 1.6.3 • Apache Spark 2.2.0 • Apache Sqoop 1.4.6 • Apache Storm 1.1.0 • Apache TEZ 0.7.0 • Apache Zeppelin 0.7.3 IBM Europe Software Announcement ZP18-0220 IBM is a registered trademark of International Business Machines Corporation 1 • Apache ZooKeeper 3.4.6 IBM(R) clients can download this new offering from Passport Advantage(R).
    [Show full text]