Selecting the Right Database for the Job

Most businesses work with data in various formats as part of their operations.  Inherent to this are the challenges involved in deriving value from their data to benefit the organization.  Many solutions exist to help firms cope with these challenges-among them a collection of tools known as databases.  These prove to be especially useful in scenarios involving large volumes, multiple users, and concerns for security.  Monticello Consulting Group can help you discover the solution that best fits your organization, as well as partner with you to make that solution a reality.

A relational database houses data in multiple tables joined by unique keys.  The ability to create relations eliminates the need to duplicate data, making relational databases organized and efficient.  Two popular examples of relational databases are SQL Server and ORACLE.

A relational database houses data in multiple tables joined by unique keys.  The ability to create relations eliminates the need to duplicate data, making relational databases organized and efficient.  Two popular examples of relational databases are SQL Server and ORACLE.


 

 

 

A Hybrid Solution

Oftentimes one application does not satisfy your data needs.  You may find yourself gravitating toward several different solutions which best address your specific requirements.  This is a perfectly logical approach, and can be made even better by linking your tools.  The following are just two ways to create a hybrid software solution that optimizes your strengths while maintaining a fluid exchange of data:

Excel + Access

The transition from Excel to Access is not always an easy one.  And while the popular database solution may fare better with complex data manipulation, Excel remains the go-to option for intuitive graphing and ad-hoc analysis.  Linking the two (via an ODBC connection) can provide you with the benefits of both tools in one harmonious package.

Access +SQL Server

A pairing of Access and SQL Server is a widely used technique for building tactical tools.  While not as flashy as a purpose-built software interface, the forms in Access provide users with an adequate medium to run the tool.  And thanks to its ubiquity on business PCs, the Access front-end can be deployed to multiple users without much fuss.  The back-end, meanwhile, runs on a central SQL Server farm-performing intensive calculations server-side and allowing modifications to be done in one centralized location.

Did You Know?

Structured Query Language (SQL) is used to manipulate data in relational databases.  While minor syntax variations exist between popular systems, SQL is a largely universal language.


A Note on Big Data

“There was five exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.” - Eric Schmidt, Google (2010)

“There was five exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.” - Eric Schmidt, Google (2010)

Our clients often ask us for our thoughts on Big Data, or ask us when Big Data is going to impact their organizations.  The reality is, if you are working for a Fortune 500 company today, your organization is likely being impacted by the massive proliferation of data already.  With that said, it is difficult to provide a rule of thumb qualifying an enterprise’s data needs as falling into the realm of ‘Big Data,’ as what is considered ‘big’ today will surely be surpassed in just a few short years.  However, the following determinants play important roles:

Data Collection Sources

With the coming of the Internet of Things (IoT), supported by the proliferation of inexpensive sensors streaming data to the Internet, along with many existing channels for gathering data such as social media and large transactional databases, the proliferation of new data sources will continue to accelerate and create the need for enterprises to employ Big Data technologies to capitalize on this information.

Data Extraction & Organization

Extracting actionable intelligence from disparate data sources, as well as storing this data, requires technologies such as Hadoop, using distributed and scalable processing of large data sets across many different servers.

Since the early 1980s, Relational Database Management Systems (RDMS) had dominated the data storage market.  However, with the arrival of the volume and velocity demands of Big Data, the need to leverage massively parallel hardware and software architectures to store and process data has challenged RDMS players such as Oracle and Microsoft SQL Server to keep up with the tide of available data.  As a result, Big Data technologies such as Hadoop, which leverage distributed storage and large-scale processing, are quickly being adopted within the corporate IT landscape.

One area Big Data is being deployed in financial markets is by regulators such as the CFTC, SEC, and FINRA.  Regulators are deploying sophisticated surveillance technology to mine the terabytes of trade data they are receiving from market participants as part of the complex regulatory reform requirements stemming from the 2008 financial crisis.  With these tools, regulators have the ability to identify patterns of troublesome trading and drill down on trading activity for further investigation.  This is a new capability in the hands of regulators that will aid in the smooth operation of financial markets, and was not available only a few short years ago-prior to the implementation of Big Data technologies.