Data collections for varied source and forms means that traditional relational databases and structures cannot be used to interpret and store this information. This poses a challenge because many organizations still cling to SQL and the relational world as they have for decades. NoSQL technologies are the solution to move us forward because of the flexible approach they bring to storing and reading data without imposing strict relational bindings. NoSQL systems such as Document Stores and Column Stores already provide a good replacement to OLTP/relational database technologies as well as read/write speeds that are much faster.
Organizations are often overwhelmed in embracing the amount of information that is generated and available for them. Managing the amount of data that is generated on a daily basis is becoming a serious challenge. With the speed in which data is generated, it demands equally, if not quicker, tools and technology to be able to extract, process and analyze the data. Traditional technologies of extracting, transforming and storing data can no longer handle the vast loads of data. This limitation has lead to the emergence of Big Data architectures and technologies. NoSQL, Distributed and Service Oriented Systems.
- NoSQL systems replace traditional OLTP/relational database technologies because they place less importance on ACID (Atomicity, Consistency, Isolation, Durability) principles and are able to read/write records at much faster speeds.
- Distributed and Load Balancing systems have now become a standard in all organizations to split and distribute the load of extracting, processing and analyzing data across a series of servers. This allows for large amounts of data to be processed in high speeds which eliminate bottle necks.
- Enterprise Service Bus (ESB) systems replace traditional integration frameworks written in custom code. These distributed and easily scalable systems allow for serialization across large workloads and applications to process large amounts of data to a variety of different applications and systems.
Large collections of data coupled with the challenges of Variety (different formats) and Velocity (near real time generation) pose significant managing costs to organizations. Despite the pace of Moore's Law, the challenge to store large data sets can no longer be met with traditional databases or data stores. The strengths of distributed storage systems like SAN (Storage Area Network) as well as NoSQL data stores that are able effectively divide, compress and store large amounts of data with improved read/write performances.