Published By: Datastax
Published Date: Apr 04, 2017
Graph databases are changing how we use data. But first, an example – you're (probably) a human working on a project and looking at graph databases as a potential solution. While we're a company that has a graph database that hopefully solves your problem. Now we could store that data in a boring relational database, but how do we do more than that? For instance, using that data, when combined with other data points, to find other people like you and recommend our solution to them? This is where a graph can come in handy.
The friendly graph data model makes it easy to use patterns of relationships within large data sets. By leveraging those relationships we can analyze, or create better real-time experiences. Why Graph explores why this graph database 'thing' is really a thing, how they compare to other database systems, and the use cases they best support.
Published By: Datastax
Published Date: Aug 03, 2018
"Part of the “new normal” where data and cloud applications are concerned is the ability to handle multiple types of data models that exist in the application and persist each in a single datastore. This data management capability is called a “multi-model” database.
Download this free white paper and explore the multi-model concept, its rationale, and how DataStax Enterprise (DSE) is the only database that can help accelerate building and powering distributed, responsive and intelligent cloud applications across multiple data models"
Published By: Vertica
Published Date: Oct 30, 2009
Independent research firm Knowledge Integrity Inc. examine two high performance computing technologies that are transitioning into the mainstream: high performance massively parallel analytical database management systems (ADBMS) and distributed parallel programming paradigms, such as MapReduce, (Hadoop, Pig, and HDFS, etc.). By providing an overview of both concepts and looking at how the two approaches can be used together, they conclude that combining a high performance batch programming and execution model with an high performance analytical database provides significant business benefits for a number of different types of applications.
Published By: VMTurbo
Published Date: Mar 25, 2015
An Intelligent Roadmap for Capacity Planning
Many organizations apply overly simplistic principles to determine requirements for compute capacity in their virtualized data centers. These principles are based on a resource allocation model which takes the total amount of memory and CPU allocated to all virtual machines in a compute cluster, and assumes a defined level of over provisioning (e.g. 2:1, 4:1, 8:1, 12:1) in order to calculate the requirement for physical resources.
Often managed in spreadsheets or simple databases, and augmented by simple alert-based monitoring tools, the resource allocation model does not account for actual resource consumption driven by each application workload running in the operational environment, and inherently corrodes the level of efficiency that can be driven from the underlying infrastructure.
When you extend the global reach of your enterprise, you’ll find new markets for your products and services. That means reaching more potential customers, bigger growth potential, and higher ROI. But to tap into those emerging markets, you need to provide the best, most consistent user experience. Now, it’s possible for you to build, deploy, and manage modern apps at scale with a globally-distributed database—without the hassles associated with hosting in your data center.
Read the e-book Build Modern Apps with Big Data at a Global Scale and learn how Azure Cosmos DB, a globally-distributed turnkey database service, is transforming the world of modern data management.
Keep access to your data available, consistent, and safe—with industry-leading, enterprise-grade security and compliance. Start developing the best app experience for your users based on five well-defined consistency models:
Strong: Favors data consistency. Ideal for banks, e-commerce processing, and online booking.
Developing for and in the cloud has never been more dependent on data. Flexibility, performance, security—your applications need a database architecture that matches the innovation of your ideas.
Industry analyst Ovum explored how Azure Cosmos DB is positioned to be the flagship database of internet-based products and services, and concluded that Azure Cosmos DB “is the first to open up [cloud] architecture to data that is not restricted by any specific schema, and it is among the most flexible when it comes to specifying consistency.”
From security and fraud detection to consumer and industrial IoT, to personalized e-commerce and social and gaming networks, to smart utilities and advanced analytics, Azure Cosmos DB is how Microsoft is structuring the database for the age of cloud.
Read the full report to learn how a globally distributed, multi-model data service can support your business objectives. Fill out the short form above to download the free research paper.
NJ 2-1-1 is a free phone number and online database that connects New Jersey residents quickly and effectively to community resources and emergency information. It is part of a growing national model that provides 190 million Americans in all 50 states.
The database you pick for your next application matters now more than ever. It can be difficult, and oftentimes impossible, to quickly join today's data into the relational model. Learn how a NoSQL database can act as a viable alternative to or compliment an existing relational database.
This whitepaper looks at why companies choose Riak
over a relational database. We focus specifically on
availability, scalability, and the key/value data model. Then
we analyze the decision points that should be considered
when choosing a non-relational solution and review data
modeling, querying, and consistency guarantees. Finally, we
end with simple patterns for building common applications
in Riak using its key/value design, dealing with data conflicts
that emerge in an eventually consistent system, and discuss
By using the Oracle Exadata Database Machine as your data warehouse platform you have a balanced, high performance hardware configuration. This paper focuses on the other two corner stones, data modeling and data loading.
As business users expand to newer types of applications, and grow older ones, their attempts to use RDBMS encounter the "Relational Wall," where RDBMS technology no longer provides the performance and functionality needed. This paper measures the wall, explains what model and architectural differences cause it, how to foresee it, and how to avoid it.
Published By: Datastax
Published Date: Aug 27, 2018
Graph databases have the power to see deeply into real-time data relationships and make it easy to use relationship patterns for instant insight into large data sets. From IoT to networking to customer 360 to solving business problems with multi-model support, the power of graph can never be understated. Read this white paper to learn the uses cases for graph databases and how graph databases work.
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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