Getting Big Data Where It Needs To Go

Getting Big Data Where It Needs To Go

Information is a critical, if not the, critical business asset in a fast-changing world. The more information an organization has, the better it can make decisions about which products and services to offer, how to price and distribute them, where it can reduce costs and where it can find new customers.

Big Data is the process of creating insight from the increasing masses of raw data every organization generates.  Most Big Data is made up of loosely structured or unstructured data such as text, photos, genomic or other scientific data. But some of it also originates in structured relational databases. In addition, many of the results of a Big Data analysis are distributed in the form of a relational database.

Whenever Big Data relies on relational data as either a source of information or a way to distribute Big Data insights, DataPortal can share those relational databases quickly, easily, cost-effectively and securely.

“Point and Click” Database Sharing

DataPortal is a patented tool for moving, storing, and sharing database data across platforms and over the Web. It was designed to be used by people at all technical levels. It requires no coding, entering command lines, opening ports or installation of software.

Using DataPortal, each database is published to a DataPortal server. From there it is available over the internal LAN or the Internet to any authorized DataPortal client. This server-to-client communication avoids the security risks of exposing the database directly to a WAN or the Internet. Any user can transfer a full relational database or a subset of that database, in relational form, with the push of a button. All they need is a Web browser and authority to share the database. Using DataPortal, subject matter experts can stay focused on their critical, value-added analytic work without wasting time on the mechanics of sending and receiving data.

DataPortal for Data Gathering

DataPortal is an efficient way to gather structured data from various database sources at a central location for Big Data integration, analysis, conversion and storage.

DataPortal complements Big Data frameworks such as Hadoop, which allow users to access massive quantities of data spread over hundreds or even thousands of processors. These frameworks allow queries to be performed using the familiar SQL used in relational databases.  The results of such queries can also take the form of structured data. Sharing the result set returned by Hadoop with end users can be problematic, but Data Portal makes it easy to deliver them to the user’s database platform of choice.

Whenever Big Data takes the form of a relational database, DataPortal makes it as easy to share as pointing and clicking.

DataPortal: Sharing Big Data Insights

The results of Big Data analysis can only help the business when they can be shared among the employees and managers who will act on that analysis.

Here, again, DataPortal can speed the results of Big Data analysis by making it easy to distribute and share SQL query results (often in the form of a relational database) among users, partners and customers. Using DataPortal, any user can easily access that database without, again, standardizing on a particular database platform, opening additional network ports, exposing the databases directly to the internet or performing any scripting or programming.

It Takes All Kinds of Big Data

When most organizations think of Big Data, they think unstructured data – and much of the time, they are right. But there’s a lot of important enterprise data in their “traditional” relational databases, and a lot of users who are most comfortable using SQL queries to analyze that data.

Whenever your Big Data effort requires or produces relational rather than unstructured data, DataPortal can ease the headache of sharing it so your users can move on to generating business insights. Learn more about how DataPortal is the easiest, fastest, least expensive and most secure way of sharing relational databases.

*image courtesy of Marc_Smith on

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