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Published By: Microsoft     Published Date: Jul 20, 2018
At its Build conference in May, Microsoft took the wraps off Cosmos DB, the new incarnation of its existing cloud-based Azure DocumentDB NoSQL database. With a nod to the dramatic, Microsoft terms Cosmos DB as its biggest database bet since SQL Server; it is positioning it as its flagship cloud database, suited for use cases ranging from security and fraud detection, to IoT (consumer and industrial), personalization, e-commerce, gaming, social networks, chats, messaging, bots, oil and gas recovery and refining, and smart utility grids. Cosmos DB is a good example of how cloud platform providers are rethinking databases for scalable, elastic environments and commodity infrastructure. The platform that is most comparable is Google Cloud Spanner, but each of these databases is engineered for different purposes: Cosmos DB as a globally distributed operational database and Spanner as a globally distributed SQL-supporting OLTP database. The highlights of Cosmos DB include its flexibility in
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Microsoft
Published By: Oracle     Published Date: Apr 16, 2018
O gerenciamento de banco de dados é caro e complicado. Conforme aumenta o número de aplicativos e bancos de dados, os custos e complicações podem multiplicar-se. Uma solução, seria um sistema de hardware e software projetado especificamente para que o software de banco de dados otimize as operações de banco de dados, tanto para o desempenho quanto para a simplificação administrativa. O Oracle Exadata é a única plataforma que proporciona desempenho ideal do banco de dados e eficiência para dados mistos, análises e cargas de trabalho OLTP. Com uma gama completa de opções de implantação, ele permite que você execute seu banco de dados Oracle e cargas de trabalho de dados onde e como desejar — on-premise, na nuvem da Oracle, na nuvem do Cliente em seu data center ou em qualquer combinação desses modelos.
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executar, banco, dados, oracle, exadata
    
Oracle
Published By: Oracle     Published Date: Apr 16, 2018
La gestión de bases de datos resulta costosa y complicada. A medida que aumenta la cantidad de aplicaciones y de bases de datos, se pueden multiplicar los costos y las complicaciones. Una solución sería un sistema hardware y software diseñado específicamente para que el software de la base de datos optimice las operaciones, tanto para simplificar el rendimiento como el aspecto administrativo. Exadata de Oracle es la única plataforma que ofrece un rendimiento óptimo de la base de datos y eficacia para la combinación de datos, análisis y cargas de trabajo para el procesamiento de transacciones en línea (OLTP). Con una amplia variedad de opciones de implementación, puede ejecutar sus bases de datos y cargas de trabajo de datos de Oracle en el lugar que quiera y de la manera que quiera, en la Nube de Oracle, en Cloud at Customer, en su data center o cualquier combinación de estos modelos.
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ejecutar, base, datos, oracle, exadata
    
Oracle
Published By: Hewlett Packard Enterprise     Published Date: Mar 26, 2018
Modern storage arrays can’t compete on price without a range of data reduction technologies that help reduce the overall total cost of ownership of external storage. Unfortunately, there is no one single data reduction technology that fits all data types and we see savings being made with both data deduplication and compression, depending on the workload. Typically, OLTP-type data (databases) work well with compression and can achieve between 2:1 and 3:1 reduction, depending on the data itself. Deduplication works well with large volumes of repeated data like virtual machines or virtual desktops, where many instances or images are based off a similar “gold” master.
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Hewlett Packard Enterprise
Published By: Oracle     Published Date: Mar 22, 2018
s your information technology (IT) organization pressured to get more work done with fewer people or on a constricted budget? Do you need to make IT a competitive asset rather than a cost center? Does your business struggle with slow software applications or data that's too often unavailable? If you answered "yes" to any of these questions, it's time to take a close look at Oracle Exadata, the world's fastest database machine exclusively designed to run Oracle Database. It is the first database machine optimized for data warehousing, online transaction processing (OLTP), and database consolidation workloads as well as in-memory databases and database as a service (DBaaS).
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Oracle
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
Compression algorithms reduce the number of bits needed to represent a set of data—the higher the compression ratio, the more space this particular data reduction technique saves. During our OLTP test, the Unity array achieved a compression ratio of 3.2-to-1 on the database volumes, whereas the 3PAR array averaged a 1.3-to-1 ratio. In our data mart loading test, the 3PAR achieved a ratio of 1.4-to-1 on the database volumes, whereas the Unity array got 1.3 to 1.
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Dell PC Lifecycle
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
When your company’s work demands a new storage array, you have the opportunity to invest in a solution that can support demanding workloads simultaneously—such as online transaction processing (OLTP) and data mart loading. At Principled Technologies, we compared Dell EMC™ PowerEdge™ R930 servers1 with the Intel® Xeon® Processor Dell EMC Unity 400F All Flash storage array to HPE ProLiant DL580 Gen9 servers with the HPE 3PAR 8400 array in three hands-on tests to determine how well each solution could serve a company during these database-intensive tasks. Intel Inside®. New Possibilities Outside.
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Dell PC Lifecycle
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
Les opérations de bases de données sont cruciales, tant par l’importance qu’elles revêtent pour votre entreprise que par leur ampleur même. Les tests que nous avons réalisés avec le serveur Dell EMC PowerEdge R930 et la baie de stockage Unity 400F All Flash montrent que cette solution offre des performances comparables à celles d’un serveur HPE ProLiant DL380 Gen9 et d’une baie 3PAR sur les charges applicatives OLTP, avec un taux de compression supérieur (3,2 pour 1 contre 1,3 pour 1). Pour le chargement de vastes ensembles de données, la baie Dell EMC Unity s’est montrée 22 pour cent plus rapide que la baie HPE 3PAR, ce qui peut faciliter la tâche de l’administrateur chargé des DataMarts. Lors de l’exécution simultanée des charges applicatives OLTP et DataMart, la baie Unity a surpassé la baie HPE 3PAR, avec 29 pour cent de commandes traitées en plus par minute.
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Dell PC Lifecycle
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
Les algorithmes de compression réduisent le nombre de bits nécessaires pour représenter un ensemble de données. Plus le taux de compression est élevé, plus cette technique de réduction des données permet d’économiser de l’espace. Lors de notre test OLTP, la baie Unity a atteint un taux de compression de 3,2 pour 1 sur les volumes de base de données. De son côté, la baie 3PAR affichait en moyenne un taux de 1,3 pour 1. Sur le test de chargement DataMart, la baie 3PAR a atteint un taux de 1,4 pour 1 sur les volumes de bases de données, tandis que la baie Unity enregistrait un taux de 1,3 pour 1.
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Dell PC Lifecycle
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
Datenbankverarbeitung ist ein wichtiges Thema – in Bezug auf Ihr Unternehmen und in Bezug auf den schieren Umfang der Vorgänge. Unsere Tests mit dem Dell EMC PowerEdge R930 Server und dem Unity 400F All-Flash-Speicherarray führten zu dem Ergebnis, dass die Leistung des Systems bei OLTP Workloads mit einem HPE ProLiant DL380 Gen9-Server mit 3PAR-Array vergleichbar ist, sich aber durch ein besseres Komprimierungsverhältnis auszeichnet (3,2:1 gegenüber 1,3:1). Ladevorgänge mit großen Datenmengen beendete das Dell EMC Unity 22 Prozent schneller als das HPE 3PAR. Dies kann weniger Aufwand für einen mit Data Marts betrauten Administrator bedeuten. Bei paralleler Ausführung von OLTP- und Data-Mart-Workloads übertraf das Unity-Array das HPE 3PAR im Hinblick auf verarbeitete Aufträge pro Minute um 29 Prozent.
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Dell PC Lifecycle
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
Komprimierungsalgorithmen sorgen dafür, dass weniger Bit benötigt werden, um einen bestimmten Datensatz zu repräsentieren. Je höher das Komprimierungsverhältnis, desto mehr Speicherplatz wird durch dieses spezielle Datenreduzierungsverfahren eingespart. Während unseres OLTP-Tests erreichte das Unity-Array bei den Datenbank-Volumes ein Komprimierungsverhältnis von 3,2:1, während das 3PAR-Array im Schnitt nur ein Verhältnis von 1,3:1 erreichte. In unserem Data Mart-Ladetest erzielte das 3PAR bei den Datenbank-Volumes ein Verhältnis von 1,4:1, das Unity-Array nur 1,3:1.
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Dell PC Lifecycle
Published By: Group M_IBM Q1'18     Published Date: Feb 28, 2018
This paper presents a cost benefit case for IBM Db2 11.1 for LUW and Oracle Database 12c.
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ibm, db2, oracle database, oltp deployments
    
Group M_IBM Q1'18
Published By: Dell and Nutanix     Published Date: Jan 16, 2018
Because many SQL Server implementations are running on virtual machines already, the use of a hyperconverged appliance is a logical choice. The Dell EMC XC Series with Nutanix software delivers high performance and low Opex for both OLTP and analytical database applications. For those moving from SQL Server 2005 to SQL Server 2016, this hyperconverged solution provides particularly significant benefits.
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data, security, add capacity, infrastructure, networking, virtualization, dell
    
Dell and Nutanix
Published By: Oracle     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p
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Oracle
Published By: Oracle CX     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle CX
Published By: Oracle CX     Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p
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Oracle CX
Published By: IBM     Published Date: Sep 28, 2017
This paper presents a cost/benefit case for two leading enterprise database contenders -- IBM DB2 11.1 for Linux, UNIX, and Windows (DB2 11.1 LUW) and Oracle Database 12c -- with regard to delivering effective security capabilities, high-performance OLTP capacity and throughput, and efficient systems configuration and management automation. Comparisons are of database installations in the telecommunications, healthcare, and consumer banking industries. For OLTP workloads in these environments, three-year costs average 32 percent less for use of DB2 11.1 compared to Oracle 12c.
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ibm, enterprise database, oltp, telecommunications, healthcare, consumer banking
    
IBM
Published By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.
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database usage, database management, server usage, data protection
    
Hewlett Packard Enterprise
Published By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
What if you could reduce the cost of running Oracle databases and improve database performance at the same time? What would it mean to your enterprise and your IT operations? Oracle databases play a critical role in many enterprises. They’re the engines that drive critical online transaction (OLTP) and online analytical (OLAP) processing applications, the lifeblood of the business. These databases also create a unique challenge for IT leaders charged with improving productivity and driving new revenue opportunities while simultaneously reducing costs.
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cost reduction, oracle database, it operation, online transaction, online analytics
    
Hewlett Packard Enterprise
Published By: IBM     Published Date: Jul 26, 2017
This paper presents a cost/benefit case for two leading enterprise database contenders -- IBM DB2 11.1 for Linux, UNIX, and Windows (DB2 11.1 LUW) and Oracle Database 12c -- with regard to delivering effective security capabilities, high-performance OLTP capacity and throughput, and efficient systems configuration and management automation. Comparisons are of database installations in the telecommunications, healthcare, and consumer banking industries. For OLTP workloads in these environments, three-year costs average 32 percent less for use of DB2 11.1 compared to Oracle 12c.
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ibm, enterprise data, windows, linux, telecommunications, healthcare, consumer banking
    
IBM
Published By: IBM     Published Date: Jun 08, 2017
This paper presents a cost/benefit case for two leading enterprise database contenders -- IBM DB2 11.1 for Linux, UNIX, and Windows (DB2 11.1 LUW) and Oracle Database 12c -- with regard to delivering effective security capabilities, high-performance OLTP capacity and throughput, and efficient systems configuration and management automation. Comparisons are of database installations in the telecommunications, healthcare, and consumer banking industries. For OLTP workloads in these environments, three-year costs average 32 percent less for use of DB2 11.1 compared to Oracle 12c.
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ibm, linux, windows, telecommunications, healthcare, oracle database
    
IBM
Published By: Micron     Published Date: Jan 12, 2017
Micron’s 9100MAX delivers on the NVMe promise with 69% better throughput and transaction rates plus much lower latency in PostgreSQL OLTP. Download this technical marketing brief to learn more.
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Micron
Published By: Micron     Published Date: Jan 12, 2017
See how Micron® NVMe SSDs and Microsoft® SQL Server reach impressive OLTP transaction rates while drastically minimizing latency and simplifying configuration. Download this technical marketing brief now.
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Micron
Published By: IBM     Published Date: Oct 13, 2016
Compare IBM DB2 pureScale with any other offering being considered for implementing a clustered, scalable database configuration see how they deliver continuous availability and why they are important. Download now!
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data. queries, database operations, transactional databases, clustering
    
IBM
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