Database Software Market: Billy Fitzsimmons +1 312 364 5112
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
(AWS) Security Workshop - Pre-Read Material
Amazon Web Services (AWS) Security Workshop - Pre-read material It is highly recommended to go through the pre-read before attending the AWS Security workshop. Shared Responsibility Model Security and Compliance is a shared responsibility between AWS and the customer. This shared model can help relieve the customer’s operational burden as AWS operates, manages and controls the components from the host operating system and virtualization layer down to the physical security of the facilities in which the service operates. The customer assumes responsibility and management of the guest operating system (including updates and security patches), other associated application software as well as the configuration of the AWS provided security group firewall. Customers should carefully consider the services they choose as their responsibilities vary depending on the services used, the integration of those services into their IT environment, and applicable laws and regulations. The nature of this shared responsibility also provides the flexibility and customer control that permits the deployment. As shown in the chart below, this differentiation of responsibility is commonly referred to as Security “of” the Cloud versus Security “in” the Cloud. AWS responsibility “Security of the Cloud” - AWS is responsible for protecting the infrastructure that runs all of the services offered in the AWS Cloud. This infrastructure is composed of the hardware, software, networking, and facilities that run AWS Cloud services. Customer responsibility “Security in the Cloud” – Customer responsibility will be determined by the AWS Cloud services that a customer selects. This determines the amount of configuration work the customer must perform as part of their security responsibilities. -
Graph Database for Collaborative Communities Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui
Graph Database for Collaborative Communities Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui To cite this version: Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui. Graph Database for Collaborative Communities. Community-Built Databases, Springer, pp.205-234, 2011. hal-00708222 HAL Id: hal-00708222 https://hal.archives-ouvertes.fr/hal-00708222 Submitted on 14 Jun 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Graph Database For collaborative Communities 1, 2 1 Rania Soussi , Marie-Aude Aufaure , Hajer Baazaoui2 1Ecole Centrale Paris, Applied Mathematics & Systems Laboratory (MAS), SAP Business Objects Academic Chair in Business Intelligence 2Riadi-GDL Laboratory, ENSI – Manouba University, Tunis Abstract Data manipulated in an enterprise context are structured data as well as un- structured data such as emails, documents, social networks, etc. Graphs are a natural way of representing and modeling such data in a unified manner (Structured, semi-structured and unstructured ones). The main advantage of such a structure relies in the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of handling data appearing. -
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-# -
The Business Case for In-Memory Databases
THE BUSINESS CASE FOR IN-MEMORY DATABASES By Elliot King, PhD Research Fellow, Lattanze Center for Information Value Loyola University Maryland Abstract Creating a true real-time enterprise has long been a goal for many organizations. The efficient use of appropriate enterprise information is always a central element of that vision. Enabling organizations to operate in real-time requires the ability to access data without delay and process transactions immediately and efficiently. In-memory databases, (IMDB) which offer much faster I/O than on-disk database technology deliver on the promise of real-time access to data. Case studies demonstrate the value of real-time access to data provided by in-memory database systems. Organizations are increasingly recognizing the value of incorporating real- time data access with appropriate applications. In-memory databases, an established technology, have traditionally been used in telecommunications and financial applications. Now they are being successfully deployed in other applications. The overall increases in data volumes which can slow down on-disk database management systems have driven this shift. Additionally, increased computer processing power and main memory capacities have facilitated more ubiquitous in-memory databases which can either standalone or serve as a cache for on-disk databases—thus creating a hybrid infrastructure. Introduction: The Real-Time Enterprise For the last decade, the real-time enterprise has been a strategic objective for many organizations and has been the stimulus for significant investment in IT. Building a real-time enterprise entails implementing access to the most timely and up-to-date data, reducing or eliminating delays in transaction processing and accelerating decision- making at all levels of an organization. -
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 -
ACID Compliant Distributed Key-Value Store
ACID Compliant Distributed Key-Value Store #1 #2 #3 Lakshmi Narasimhan Seshan , Rajesh Jalisatgi , Vijaeendra Simha G A # 1l [email protected] # 2r [email protected] #3v [email protected] Abstract thought that would be easier to implement. All Built a fault-tolerant and strongly-consistent key/value storage components run http and grpc endpoints. Http endpoints service using the existing RAFT implementation. Add ACID are for debugging/configuration. Grpc Endpoints are transaction capability to the service using a 2PC variant. Build used for communication between components. on the raftexample[1] (available as part of etcd) to add atomic Replica Manager (RM): RM is the service discovery transactions. This supports sharding of keys and concurrent transactions on sharded KV Store. By Implementing a part of the system. RM is initiated with each of the transactional distributed KV store, we gain working knowledge Replica Servers available in the system. Users have to of different protocols and complexities to make it work together. instantiate the RM with the number of shards that the user wants the key to be split into. Currently we cannot Keywords— ACID, Key-Value, 2PC, sharding, 2PL Transaction dynamically change while RM is running. New Replica Servers cannot be added or removed from once RM is I. INTRODUCTION up and running. However Replica Servers can go down Distributed KV stores have become a norm with the and come back and RM can detect this. Each Shard advent of microservices architecture and the NoSql Leader updates the information to RM, while the DTM revolution. Initially KV Store discarded ACID properties leader updates its information to the RM. -
JMP Securities Elite 80 Report (Formerly Super 70)
Cybersecurity, Data Management & ,7 Infrastructure FEBRUARY 201 ELITE 80 THE HOTTEST PRIVATELY HELD &<%(5SECURITY, '$7$0$1$*(0(17 AND ,7,1)5$6758&785( COMPANIES &RS\ULJKWWLWLSRQJSZO6KXWWHUVWRFNFRP Erik Suppiger Patrick Walravens Michael Berg [email protected] [email protected] [email protected] (415) 835-3918 (415) 835-8943 (415)-835-3914 FOR DISCLOSURE AND FOOTNOTE INFORMATION, REFER TO JMP FACTS AND DISCLOSURES SECTION. Cybersecurity, Data Management & IT Infrastructure TABLE OF CONTENTS Executive Summary ............................................................................................................................ 4 Top Trends and Technological Changes ............................................................................................ 5 Funding Trends ................................................................................................................................ 11 Index by Venture Capital Firm .......................................................................................................... 17 Actifio ................................................................................................................................................ 22 Alert Logic ......................................................................................................................................... 23 AlgoSec ............................................................................................................................................ 24 AnchorFree ...................................................................................................................................... -
AWS Managed Services (AMS)
AWS Managed Services (AMS) Application Developer's Guide AMS Advanced Operations Plan Version September 16, 2020 AWS Managed Services (AMS) Application Developer's Guide AMS Advanced Operations Plan AWS Managed Services (AMS) Application Developer's Guide: AMS Advanced Operations Plan Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. AWS Managed Services (AMS) Application Developer's Guide AMS Advanced Operations Plan Table of Contents Application Onboarding to AMS Introduction ........................................................................................ 1 What is Application Onboarding? ................................................................................................. 1 What we do, what we do not do .................................................................................................. 1 AMS Amazon Machine Images (AMIs) ............................................................................................ 2 Security enhanced AMIs ...................................................................................................... 4 Key terms ................................................................................................................................. -
Property Graph Vs RDF Triple Store: a Comparison on Glycan Substructure Search
RESEARCH ARTICLE Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search Davide Alocci1,2, Julien Mariethoz1, Oliver Horlacher1,2, Jerven T. Bolleman3, Matthew P. Campbell4, Frederique Lisacek1,2* 1 Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 2 Computer Science Department, University of Geneva, Geneva, 1227, Switzerland, 3 Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 4 Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia * [email protected] Abstract Resource description framework (RDF) and Property Graph databases are emerging tech- nologies that are used for storing graph-structured data. We compare these technologies OPEN ACCESS through a molecular biology use case: glycan substructure search. Glycans are branched Citation: Alocci D, Mariethoz J, Horlacher O, tree-like molecules composed of building blocks linked together by chemical bonds. The Bolleman JT, Campbell MP, Lisacek F (2015) molecular structure of a glycan can be encoded into a direct acyclic graph where each node Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search. PLoS ONE 10(12): represents a building block and each edge serves as a chemical linkage between two build- e0144578. doi:10.1371/journal.pone.0144578 ing blocks. In this context, Graph databases are possible software solutions for storing gly- Editor: Manuela Helmer-Citterich, University of can structures and Graph query languages, such as SPARQL and Cypher, can be used to Rome Tor Vergata, ITALY perform a substructure search. Glycan substructure searching is an important feature for Received: July 16, 2015 querying structure and experimental glycan databases and retrieving biologically meaning- ful data. -
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. -
Performance at Scale with Amazon Elasticache
Performance at Scale with Amazon ElastiCache July 2019 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 ElastiCache Overview ......................................................................................................... 2 Alternatives to ElastiCache ................................................................................................. 2 Memcached vs. Redis ......................................................................................................... 3 ElastiCache for Memcached ............................................................................................... 5 Architecture with ElastiCache for Memcached ............................................................... -
High Performance with Distributed Caching
High Performance with Distributed Caching Key Requirements For Choosing The Right Solution High Performance with Distributed Caching: Key Requirements for Choosing the Right Solution Table of Contents Executive summary 3 Companies are choosing Couchbase for their caching layer, and much more 3 Memory-first 4 Persistence 4 Elastic scalability 4 Replication 5 More than caching 5 About this guide 5 Memcached and Oracle Coherence – two popular caching solutions 6 Oracle Coherence 6 Memcached 6 Why cache? Better performance, lower costs 6 Common caching use cases 7 Key requirements for an effective distributed caching solution 8 Problems with Oracle Coherence: cost, complexity, capabilities 8 Memcached: A simple, powerful open source cache 10 Lack of enterprise support, built-in management, and advanced features 10 Couchbase Server as a high-performance distributed cache 10 General-purpose NoSQL database with Memcached roots 10 Meets key requirements for distributed caching 11 Develop with agility 11 Perform at any scale 11 Manage with ease 12 Benchmarks: Couchbase performance under caching workloads 12 Simple migration from Oracle Coherence or Memcached to Couchbase 13 Drop-in replacement for Memcached: No code changes required 14 Migrating from Oracle Coherence to Couchbase Server 14 Beyond caching: Simplify IT infrastructure, reduce costs with Couchbase 14 About Couchbase 14 Caching has become Executive Summary a de facto technology to boost application For many web, mobile, and Internet of Things (IoT) applications that run in clustered performance as well or cloud environments, distributed caching is a key requirement, for reasons of both as reduce costs. performance and cost. By caching frequently accessed data in memory – rather than making round trips to the backend database – applications can deliver highly responsive experiences that today’s users expect.