It is a mistake to think of Data and Databases as just one thing, and an especially big mistake to think of them as just one proprietary vendor, but like many proprietary systems licensing and support add up to large recurring costs.
Instead consider that there are three kinds of approach to data: traditional relational databases, unstructured databases, and the new 'big data' databases. Choosing the right type for what you want to do is critical to looking after the data in your organisation.
Relational database management systems (RDBMSs) like MySQL and PostgreSQL are ideal for web publishing, and can often be found powering web applications like Wordpress and Drupal.
RDBMSes contain a set of tables containing data fitted into predefined categories, often with links between them. For example, a company database might contain a list of employees in one table, and payroll records in another table, using the employee ID as a link between them.
PostgreSQL development began in 1986 and it is today recognised as one of the most robust and complete open source implementations of an RDBMS.
MySQL is one of the most popular RDBMSs, so much so that the acronym 'LAMP' was coined to describe typical web application deployments - Linux, Apache, MySQL, PHP. MySQL has been around since 1995, and following acquisitions is now owned by Oracle.
Structured Query Language, or SQL, is the language used to define, work with and manage relational databases. NoSQL databases are so-called because they do not follow the same structure as RDBMSes - they do not require rigid definitions of an underlying structure before you store your data, and they do not use SQL to access the data. They do not provide much functionality beyond storing information, but in return they offer signficant benefits in scalability and performance.
Big Data databases
'Big Data' refers to the trend toward extremely large collections of information that cannot be managed or worked with using traditional database tools. Examples of big data include internet search engine data, social network data, biological, meteorological and other scientific data sets.
Migration towards a new environment should deliver more flexiblility, more sustainable underlying infrastructure, avoid data in silos and assure ease of exit. Our job is to help you understand your data and guide you towards making it work better for you. EDM arose to address circumstances where users within organizations independently source, model, manage and store data. These unco-ordinated approaches by various segments of the organization can result in data conflicts and quality inconsistencies – making it difficult for users to trust the data as it is used for operations and reporting.