Supercharging Microstrategy
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Optimisation of Ad-Hoc Analysis of an OLAP Cube Using Sparksql
UPTEC X 17 007 Examensarbete 30 hp September 2017 Optimisation of Ad-hoc analysis of an OLAP cube using SparkSQL Milja Aho Abstract Optimisation of Ad-hoc analysis of an OLAP cube using SparkSQL Milja Aho Teknisk- naturvetenskaplig fakultet UTH-enheten An Online Analytical Processing (OLAP) cube is a way to represent a multidimensional database. The multidimensional database often uses a star Besöksadress: schema and populates it with the data from a relational database. The purpose of Ångströmlaboratoriet Lägerhyddsvägen 1 using an OLAP cube is usually to find valuable insights in the data like trends or Hus 4, Plan 0 unexpected data and is therefore often used within Business intelligence (BI). Mondrian is a tool that handles OLAP cubes that uses the query language Postadress: MultiDimensional eXpressions (MDX) and translates it to SQL queries. Box 536 751 21 Uppsala Apache Kylin is an engine that can be used with Apache Hadoop to create and query OLAP cubes with an SQL interface. This thesis investigates whether the Telefon: engine Apache Spark running on a Hadoop cluster is suitable for analysing OLAP 018 – 471 30 03 cubes and what performance that can be expected. The Star Schema Benchmark Telefax: (SSB) has been used to provide Ad-Hoc queries and to create a large database 018 – 471 30 00 containing over 1.2 billion rows. This database was created in a cluster in the Omicron office consisting of five worker nodes and one master node. Queries were Hemsida: then sent to the database using Mondrian integrated into the BI platform Pentaho. http://www.teknat.uu.se/student Amazon Web Services (AWS) has also been used to create clusters with 3, 6 and 15 slaves to see how the performance scales. -
Sushil Thomas & Steve Wooledge
Sushil Thomas & Steve Wooledge Sushil Thomas & Steve Modern Business Intelligence: Leading the Way for Big Data Success Sushil Thomas & Steve Wooledge Modern Business Intelligence: Leading the Way to Big Data Success Sushil Thomas & Steve Wooledge No part of this book’s contents may be used for any other purpose, or reproduced by any means, electronic or mechanical, without the express prior written permission of Arcadia Data Inc. 999 Baker Way, Suite 120 San Mateo, CA 94404 Visit us on the web: www.arcadiadata.com The text, graphics and examples included herein are for the purpose of illustration and reference only. The specifications on which they are based are subject to change without notice. No legal or accounting advice is provided hereunder. Arcadia Data reserves the right to revise or withdraw this document or any part there- of at any time. Copyright © 2017 Arcadia Data Inc. All rights reserved. Other company and brand products and service names are trademarks or registered trademarks of their respective holders. ISBN: 978-0-692-94721-0 Contents Introduction ...........................................................................................ix Chapter 1: A Brief Overview of the Big Data Ecosystem ..............................1 The Big Data Ecosystem Starts with Apache Hadoop 2 In with the New — and the Old, Too 4 Make Way for Spark 6 Other Platforms: NoSQL, NewSQL, Object Stores 9 Chapter 2: BI and Analytics Meet Business Transformation ...................... 14 What is the Difference between BI and Analytics? 15 A Brief History of BI 17 Wither the RDBMS? Not So Fast… 21 The Present and Future of Enterprise Reporting 22 Self-Service BI 23 Hadoop Analytics Case Study 24 Chapter 3: Rise of the Citizen Data Scientist ...........................................27 The Imperative of User-Friendly Analytics 29 Hadoop as the Platform Game-Changer 31 Data Visualization Comes to the Fore 32 Chapter 4: Democratizing Big Data ....................................................... -
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. -
Microstrategy Readme
MicroStrategy 2021 Readme Version 2021 MicroStrategy2021 September 2021 Copyright © 2021 by MicroStrategy Incorporated. All rights reserved. Trademark Information The following are either trademarks or registered trademarks of MicroStrategy Incorporated or its affiliates in the United States and certain other countries: Dossier, Enterprise Semantic Graph, Expert.Now, HyperIntelligence, HyperMobile, HyperScreen, HyperVision, HyperVoice, HyperWeb, Information Like Water, Intelligent Enterprise, MicroStrategy, MicroStrategy 2019, MicroStrategy 2020, MicroStrategy 2021, MicroStrategy Analyst Pass, MicroStrategy Architect, MicroStrategy Architect Pass, MicroStrategy Badge, MicroStrategy Cloud, MicroStrategy Cloud Intelligence, MicroStrategy Command Manager, MicroStrategy Communicator, MicroStrategy Consulting, MicroStrategy Desktop, MicroStrategy Developer, MicroStrategy Distribution Services, MicroStrategy Education, MicroStrategy Embedded Intelligence, MicroStrategy Enterprise Manager, MicroStrategy Federated Analytics, MicroStrategy Geospatial Services, MicroStrategy Identity, MicroStrategy Identity Manager, MicroStrategy Identity Server, MicroStrategy Integrity Manager, MicroStrategy Intelligence Server, MicroStrategy Library, MicroStrategy Mobile, MicroStrategy Narrowcast Server, MicroStrategy Object Manager, MicroStrategy Office, MicroStrategy OLAP Services, MicroStrategy Parallel Relational In-Memory Engine (MicroStrategy PRIME), MicroStrategy R Integration, MicroStrategy Report Services, MicroStrategy SDK, MicroStrategy System Manager, -
Accelerate Your BI on Trillions of Rows
DATASHEET Accelerate Your BI on Trillions of Rows Semantic Layer Powered by Smart OLAPTM Technology CLOUD | ON-PREMISES BI ACCELERATION LAYER BUSINESS INTELLIGENCE AND ANALYTICS • Smart OLAPTM for unlimited scalability ACCESS • High-performing Universal Semantic Layer MECHANISMS SQL MDX REST API JAVA API • ML-powered Smart Recommendation Engine for smarter aggregates SEMANTIC MODEL • Incremental data refresh BI SERVERS • Support for recursive, unbalanced, ragged, and KYVOS BI HIGH AVAILABILITY | LOAD BALANCER MULTI ACCELERATION LEVEL alternate hierarchies, as well as custom rollups LAYER CACHE QUERY ENGINES • Support for accurate and approximate distinct count KYVOS SMART AGGREGATES • Visual and code-free data preparation MODERN DATA • Support for all BI and analytics tools PLATFORMS • Enterprise-level security UNMATCHED PERFORMANCE SMART OLAP™ TECHNOLOGY Query trillions of rows in sub-seconds.. Interact with Deal with the scale and complexity of today’s data with your data like never before. Roll up, drill down, slice our disruptive OLAP technology. Advanced algorithms and dice in seconds. Enable concurrent access to enable aggregations on huge cardinality and massive thousands of users with high performance for both volumes, and ML-powered Smart Recommendation warm and cold queries to produce a truly Engine brings in the intelligence required to build enterprise-class experience. smarter aggregates on modern data platforms. UNIVERSAL SEMANTIC LAYER USE YOUR FAVORITE BI TOOLS Define all your metadata and business logic in one Visualize data using your existing BI tools with instant place and create a unified data view for users response times. Kyvos supports all major BI tools, across the enterprise. Translate complex business including Tableau, MicroStrategy, Qlik, Excel, Looker, use cases into accurate data models with Business Objects, Cognos, Power BI, Spotfire, and, as advanced data modeling features. -
Migrate Your SSAS Cubes to the Cloud Or an On-Premise Data Platform Unprecedented OLAP with No Compromise on Scale and Speed
SOLUTION BRIEF Migrate your SSAS Cubes to the Cloud or an On-premise Data Platform Unprecedented OLAP with No Compromise on Scale and Speed SSAS OLAP offers a powerful way to aggregate enterprise data, and WHY SHOULD YOU MIGRATE? enable ad-hoc analysis on it. However, it faces several limitations when it encounters massive data. Scalability is a major deterrent as there is a Migrate from SSAS to Kyvos if you limit to the size of data on which SSAS cubes can be built. Besides, with want to: increasing data, the time required to process the cube increases • Build cubes on massive volumes significantly, making it difficult to fit SSAS OLAP in a modern data of data with no limit on size environment. • Store all required information in your cubes and drill-down to Our Smart OLAP™ technology eliminates these limitations, helping you deeper levels of granularity at build aggregates on any size data, while delivering high performance. interactive speeds You can migrate your existing SSAS cubes to Kyvos and scale quickly to • Enable a large number of accommodate your growing data. Built with SSAS backward concurrent users to access the cubes without impacting compatibility in mind, Kyvos enables easy migration. You can work on performance any of the cloud platforms such as Amazon Web Services (AWS), • Leverage modern data platforms Microsoft Azure, Google Cloud Platform or any flavors of Hadoop. You to scale quickly can also build cubes on cloud data warehouses such as Snowflake, Google BigQuery, and Amazon Redshift, as well as other sources like Azure SQL DB and Delta Lake.