The Forrester Wave™: In-Memory Database Platforms, Q3 2015 by Noel Yuhanna August 3, 2015
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Changing the Game: Monthly Technology Briefs
the way we see it Changing the Game: Monthly Technology Briefs April 2011 Tablets and Smartphones: Levers of Disruptive Change Read the Capgemini Chief Technology Officers’ Blog at www.capgemini.com/ctoblog Public the way we see it Tablets and Smartphones: Levers of Disruptive Change All 2010 shipment reports tell the same story - of an incredible increase in the shipments of both Smartphones and Tablets, and of a corresponding slowdown in the conventional PC business. Smartphone sales exceeded even the most optimis- tic forecasts of experts, with a 74 percent increase from the previous year – around a battle between Apple and Google Android for supremacy at the expense of traditional leaders Nokia and RIM BlackBerry. It was the same story for Tablets with 17.4 million units sold in 2010 led by Apple, but once again with Google Android in hot pursuit. Analyst predictions for shipments suggest that the tablet market will continue its exponential growth curve to the extent that even the usually cautious Gartner think that by 2013 there will be as many Tablets in use in an enterprise as PCs with a profound impact on the IT environment. On February 7, as part of the Gartner ‘First Thing Monday’ series under the title ‘The Digital Natives are Restless, The impending Revolt against the IT Nanny State’ Gartner analyst Jim Shepherd stated; “I am regularly hearing middle managers and even senior executives complaining bit- terly about IT departments that are so focussed on the global rollout of some monolith- ic solution that they have no time for new and innovative technologies that could have an immediate impact on the business. -
Synopsys: Large Graph Analytics in the SAP HANA Database Through Summarization
SynopSys: Large Graph Analytics in the SAP HANA Database Through Summarization Michael Rudolf1 Marcus Paradies1 Christof Bornhövd2 Wolfgang Lehner3 1SAP AG 2SAP Labs, LLC 3Database Technology Group Walldorf, Germany Palo Alto, CA 94304, USA TU Dresden, Germany [email protected] [email protected] [email protected] ABSTRACT “Cell Phones “Computers & & Accessories” Graph-structured data is ubiquitous and with the advent of social Accessories” 4 networking platforms has recently seen a significant increase in 6 popularity amongst researchers. However, also many business appli- part of part of “Freddy” cations deal with this kind of data and can therefore benefit greatly 10 “Mike” from graph processing functionality offered directly by the underly- 8 5 “Phones” “Tablets” 7 ing database. This paper summarizes the current state of graph data in rates 4/5 processing capabilities in the SAP HANA database and describes our efforts to enable large graph analytics in the context of our research in 11 “Steve” rates 5/5 white 3 “Apple in project SynopSys. With powerful graph pattern matching support at iPhone 4” 16 GB the core, we envision OLAP-like evaluation functionality exposed to rates 5/5 the user in the form of easy-to-apply graph summarization templates. black rates 3/5 black 64 GB 1 32 GB 2 By combining them, the user is able to produce concise summaries 9 of large graph-structured datasets. We also point out open questions “Apple iPad “Apple and challenges that we plan to tackle in the future developments on MC707LL/A” “Carl” iPhone 5” our way towards large graph analytics. -
Beyond Relational Databases
EXPERT ANALYSIS BY MARCOS ALBE, SUPPORT ENGINEER, PERCONA Beyond Relational Databases: A Focus on Redis, MongoDB, and ClickHouse Many of us use and love relational databases… until we try and use them for purposes which aren’t their strong point. Queues, caches, catalogs, unstructured data, counters, and many other use cases, can be solved with relational databases, but are better served by alternative options. In this expert analysis, we examine the goals, pros and cons, and the good and bad use cases of the most popular alternatives on the market, and look into some modern open source implementations. Beyond Relational Databases Developers frequently choose the backend store for the applications they produce. Amidst dozens of options, buzzwords, industry preferences, and vendor offers, it’s not always easy to make the right choice… Even with a map! !# O# d# "# a# `# @R*7-# @94FA6)6 =F(*I-76#A4+)74/*2(:# ( JA$:+49>)# &-)6+16F-# (M#@E61>-#W6e6# &6EH#;)7-6<+# &6EH# J(7)(:X(78+# !"#$%&'( S-76I6)6#'4+)-:-7# A((E-N# ##@E61>-#;E678# ;)762(# .01.%2%+'.('.$%,3( @E61>-#;(F7# D((9F-#=F(*I## =(:c*-:)U@E61>-#W6e6# @F2+16F-# G*/(F-# @Q;# $%&## @R*7-## A6)6S(77-:)U@E61>-#@E-N# K4E-F4:-A%# A6)6E7(1# %49$:+49>)+# @E61>-#'*1-:-# @E61>-#;6<R6# L&H# A6)6#'68-# $%&#@:6F521+#M(7#@E61>-#;E678# .761F-#;)7-6<#LNEF(7-7# S-76I6)6#=F(*I# A6)6/7418+# @ !"#$%&'( ;H=JO# ;(\X67-#@D# M(7#J6I((E# .761F-#%49#A6)6#=F(*I# @ )*&+',"-.%/( S$%=.#;)7-6<%6+-# =F(*I-76# LF6+21+-671># ;G';)7-6<# LF6+21#[(*:I# @E61>-#;"# @E61>-#;)(7<# H618+E61-# *&'+,"#$%&'$#( .761F-#%49#A6)6#@EEF46:1-# -
Data Platforms Map from 451 Research
1 2 3 4 5 6 Azure AgilData Cloudera Distribu2on HDInsight Metascale of Apache Kaa MapR Streams MapR Hortonworks Towards Teradata Listener Doopex Apache Spark Strao enterprise search Apache Solr Google Cloud Confluent/Apache Kaa Al2scale Qubole AWS IBM Azure DataTorrent/Apache Apex PipelineDB Dataproc BigInsights Apache Lucene Apache Samza EMR Data Lake IBM Analy2cs for Apache Spark Oracle Stream Explorer Teradata Cloud Databricks A Towards SRCH2 So\ware AG for Hadoop Oracle Big Data Cloud A E-discovery TIBCO StreamBase Cloudera Elas2csearch SQLStream Data Elas2c Found Apache S4 Apache Storm Rackspace Non-relaonal Oracle Big Data Appliance ObjectRocket for IBM InfoSphere Streams xPlenty Apache Hadoop HP IDOL Elas2csearch Google Azure Stream Analy2cs Data Ar2sans Apache Flink Azure Cloud EsgnDB/ zone Platforms Oracle Dataflow Endeca Server Search AWS Apache Apache IBM Ac2an Treasure Avio Kinesis LeanXcale Trafodion Splice Machine MammothDB Drill Presto Big SQL Vortex Data SciDB HPCC AsterixDB IBM InfoSphere Towards LucidWorks Starcounter SQLite Apache Teradata Map Data Explorer Firebird Apache Apache JethroData Pivotal HD/ Apache Cazena CitusDB SIEM Big Data Tajo Hive Impala Apache HAWQ Kudu Aster Loggly Ac2an Ingres Sumo Cloudera SAP Sybase ASE IBM PureData January 2016 Logic Search for Analy2cs/dashDB Logentries SAP Sybase SQL Anywhere Key: B TIBCO Splunk Maana Rela%onal zone B LogLogic EnterpriseDB SQream General purpose Postgres-XL Microso\ Ry\ X15 So\ware Oracle IBM SAP SQL Server Oracle Teradata Specialist analy2c PostgreSQL Exadata -
SAP HANA Client Interface Programming Reference for SAP HANA Platform Company
PUBLIC SAP HANA Platform 2.0 SPS 04 Document Version: 1.1 – 2019-10-31 SAP HANA Client Interface Programming Reference for SAP HANA Platform company. All rights reserved. All rights company. affiliate THE BEST RUN 2019 SAP SE or an SAP SE or an SAP SAP 2019 © Content 1 SAP HANA Client Interface Programming Reference.................................17 2 Configuring Clients for Secure Connections.......................................19 2.1 Server Certificate Authentication.................................................19 2.2 Mutual Authentication........................................................ 20 Implement Mutual Authentication..............................................20 2.3 Configuring the Client for Client-Side Encryption and LDAP.............................. 26 3 Connecting to SAP HANA Databases and Servers...................................27 3.1 Setting Session-Specific Client Information..........................................29 3.2 Use the User Store (hdbuserstore)................................................32 4 Client Support for Active/Active (Read Enabled)...................................34 4.1 Connecting Using Active/Active (Read Enabled)...................................... 34 Client Requirements For A Takeover.............................................35 4.2 Hint-Based Statement Routing for Active/Active (Read Enabled)...........................36 4.3 Forced Statement Routing to a Site for Active/Active (Read Enabled)........................37 Implement Forced Statement Routing to a Site for Active/Active -
Compact and Efficient Representation of General Graph Databases
This is a post-peer-review, pre-copyedit version of an article pub- lished in Knowledge and Information Systems. The final authenti- cated version is available online at: http://dx.doi.org/10.1007/ s10115-018-1275-x Compact and Efficient Representation of General Graph Databases1 Sandra Alvarez-Garc´ıa´ 1, Borja Freire2, Susana Ladra3 and Oscar´ Pedreira3 1Indra, A Coru~na,Spain; 2 Enxenio S.L., A Coru~na,Spain; 3 Universidade da Coru~na, CITIC, Database Laboratory, A Coru~na,Spain Abstract. In this paper, we propose a compact data structure to store labeled at- tributed graphs based on the k2-tree, which is a very compact data structure designed to represent a simple directed graph. The idea we propose can be seen as an extension of the k2-tree to support property graphs. In addition to the static approach, we also pro- pose a dynamic version of the storage representation, which allows flexible schemas and insertion or deletion of data. We provide an implementation of a basic set of operations, which can be combined to form complex queries over these graphs with attributes. We evaluate the performance of our proposal with existing graph database systems and prove that our compact attributed graph representation obtains also competitive time results. Keywords: Compression; Graph Databases; Property Graphs; Attributed Graphs; Compact Data Structures; Dynamic Graphs 1. Introduction Graphs are a natural way for modeling data in such domains where the most arXiv:1812.10977v1 [cs.DS] 28 Dec 2018 relevant information relies on the relationships between the entities. Some rep- resentative examples are Web graphs (Raghavan and Garcia-Molina 2003), so- cial networks (Padrol-Sureda et al. -
SAP on Google Cloud: High Availability
SAP on Google Cloud: High availability Overview © 2020 Google LLC. All rights reserved. Contents About this document 2 Introduction 2 Levels of high availability 3 Level 1: Infrastructure 3 Zones and regions 3 Live migration 4 Host auto restart 4 Level 2: Database setup 5 SAP HANA databases 5 Synchronous SAP HANA System Replication 5 SAP HANA host auto-failover on Google Cloud 7 SAP ASE databases 8 MaxDB databases 8 IBM Db2 databases 9 Microsoft SQL Server databases 9 Level 3: Application servers 10 Summary 12 Further reading 13 1 © 2020 Google LLC. All rights reserved. About this document This document is part of a series about working with SAP on Google Cloud. The series includes the following documents: ● High availability (this document) ● Migration strategies ● Backup strategies and solutions ● Disaster-recovery strategies Introduction The term high availability (HA) is used to describe an architecture that improves a system’s availability. The availability of a system refers to a user’s ability to connect to the system and conduct the required operations. If a user can’t connect, the system is perceived as unavailable, regardless of the underlying issue. For example, a networking issue can prevent users from accessing the service, even though the system is running. A high-availability setup interacts with multiple components of the architecture to minimize the points of failure, typically by using redundancy. To measure a service’s performance throughout the year, the metric of percentage of uptime is used to calculate the ratio of uptime to the aggregate of uptime and downtime. A system that is available for ~8750 hours during the 8760 hours of a year has an uptime of 99.89% (8750/8760) and a downtime of 10 hours. -
New Sap Framework Marries Geospatial Data with Hana Business Data
▲ E-Guide NEW SAP FRAMEWORK MARRIES GEOSPATIAL DATA WITH HANA BUSINESS DATA SearchSAP NEW SAP FRAMEWORK MARRIES GEOSPATIAL DATA WITH HANA BUSINESS DATA Home New SAP framework marries geospatial data with HANA business data ISCOVER THE NEW SAP framework that D marries geospatial data with HANA business data in this expert e-guide. Learn how it can help organizations enrich business applications with geospatial data from geographic information systems (GIS) like Esri ArcGIS. Also, find out about the latest version of SAP Business One. PAGE 2 OF 9 SPONSORED BY NEW SAP FRAMEWORK MARRIES GEOSPATIAL DATA WITH HANA BUSINESS DATA NEW SAP FRAMEWORK MARRIES GEOSPATIAL DATA WITH HANA BUSINESS DATA Jim O’Donnell, News Editor Home New SAP framework The marriage of SAP HANA business data and Esri mapping information marries geospatial data with HANA became more solid with the release of SAP Geographical Enablement Frame- business data work. Esri is a geospatial data company based in Redlands, Calif. Recent releases of SAP HANA have included features that integrated Esri geospatial data into HANA applications, and the SAP Geographic Enablement Framework extends this integration as a standalone product, according to Karsten Hauschild, SAP solution manager. The framework helps organizations to enrich business applications with geospatial data from geographic information systems (GIS) like Esri ArcGIS, Hauschild said. This can help drive applications that go beyond the simple map-related software found on consumer mobile devices. “If you look at what’s going on now, these map-driven user experiences are enabled on these mobile devices with built-in GPS, but that’s only on the con- sumer side, and they are satisfied by locating places or checking directions,” PAGE 3 OF 9 SPONSORED BY NEW SAP FRAMEWORK MARRIES GEOSPATIAL DATA WITH HANA BUSINESS DATA Hauschild said. -
LIST of NOSQL DATABASES [Currently 150]
Your Ultimate Guide to the Non - Relational Universe! [the best selected nosql link Archive in the web] ...never miss a conceptual article again... News Feed covering all changes here! NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above. [based on 7 sources, 14 constructive feedback emails (thanks!) and 1 disliking comment . Agree / Disagree? Tell me so! By the way: this is a strong definition and it is out there here since 2009!] LIST OF NOSQL DATABASES [currently 150] Core NoSQL Systems: [Mostly originated out of a Web 2.0 need] Wide Column Store / Column Families Hadoop / HBase API: Java / any writer, Protocol: any write call, Query Method: MapReduce Java / any exec, Replication: HDFS Replication, Written in: Java, Concurrency: ?, Misc: Links: 3 Books [1, 2, 3] Cassandra massively scalable, partitioned row store, masterless architecture, linear scale performance, no single points of failure, read/write support across multiple data centers & cloud availability zones. API / Query Method: CQL and Thrift, replication: peer-to-peer, written in: Java, Concurrency: tunable consistency, Misc: built-in data compression, MapReduce support, primary/secondary indexes, security features. -
SAP HANA – from Relational OLAP Database to Big Data Infrastructure
SAP HANA – From Relational OLAP Database to Big Data Infrastructure Norman May, Wolfgang Lehner, Shahul Hameed P., Nitesh Maheshwari, Carsten Müller, Sudipto Chowdhuri, Anil Goel SAP SE fi[email protected] ABSTRACT and data formats [5]. Novel application scenarios address primar- SAP HANA started as one of the best-performing database engines ily a mixture of traditional business applications and deep analytics for OLAP workloads strictly pursuing a main-memory centric ar- of gathered data sets to drive business processes not only from a chitecture and exploiting hardware developments like large number strategic perspective but also to optimize the operational behavior. of cores and main memories in the TByte range. Within this pa- With the SAP HANA data platform, SAP has delivered a well- per, we outline the steps from a traditional relational database en- orchestrated, highly tuned, and low-TCO software package for push- gine to a Big Data infrastructure comprising different methods to ing the envelope in Big Data environments. As shown in figure 1, handle data of different volume, coming in with different velocity, the SAP HANA data platform is based in its very core on the SAP and showing a fairly large degree of variety. In order to make the HANA in-memory database system accompanied with many func- presentation of this transformation process more tangible, we dis- tional and non-functional components to take the next step towards cuss two major technical topics–HANA native integration points mature and enterprise ready data management infrastructures. In as well as extension points for collaboration with Hadoop-based order to be aligned with the general “definition” of Big Data, we data management infrastructures. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
What's New in the SAP HANA Client Company
INTERNAL SAP HANA Client 2.4 Document Version: 1.1 – 2020-10-26 What's New in the SAP HANA Client 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 New and Changed Features in the SAP HANA Client..................................3 What's New in the SAP HANA Client 2 INTERNAL Content 1 New and Changed Features in the SAP HANA Client Note Information about earlier versions of the SAP HANA Client (prior to version 2.4) is included in the SAP HANA Platform documentation. Client Version Type Description 2.4 New Direct TCP You can create an HTTP and TLS proxy connection with Connec out using WebSockets, tions Through an allowing direct TCP con HTTP Proxy nections via an HTTP proxy. HTTP Proxy Client Con nections Implement HTTP Proxy Client Connections JDBC Connection Prop erties 2.4 New SAP SAP HANA Cloud supports HANA SNI routing. You must use Cloud version 2.4.167 (2.4.67 for Sup port the JDBC driver) or later of the SAP HANA client inter faces with SAP HANA Cloud. There are also restrictions on the platforms that sup port SAP HANA Cloud. 2.5 New .NET A new environment varia Core En ble, HDBDOTNETCORE, hance and examples have been ments added for .NET Core. Run the .NET Core Exam ples What's New in the SAP HANA Client New and Changed Features in the SAP HANA Client INTERNAL 3 Client Version Type Description 2.5 New .NET You can now develop .NET Core Core applications on Linux Sup and macOS with the SAP ports Li nux and HANA client.