Corporations can generate large volumes of data through the products and services they offer to their customers. Analyzing these data appropriately can provide very useful information for optimizing and improving the services offered and even generating new products and services. In short, good data analysis can give a corporation clear competitive advantages.
However, a problem arises when the data to be analyzed increases exponentially, as is currently the case in technology companies where you have products and services that let you measure an enormous number of parameters constantly churning out vast amounts of data. In addition to this, these data usually come from heterogeneous sources – with varying structures or even without any structure at all – and are generated very quickly. When this happens, the conventional technologies for processing, storing and visualizing data are no longer valid, either due to limitations in processing capacity or processing time, or due to data storage limits.
This is where Big Data technologies come into play. These technologies provide data processing and storage tools specifically designed to solve the types of problems faced in this type of environment.
Big Data processing technologies
Big Data technologies use a different approach to solve the processing problems mentioned above. By applying techniques like MapReduce, they allow the distributed processing of large data sets across clusters of computers.
The clusters can be very large; indeed, some corporations have over 1000 computers participating in a cluster, giving them several thousand microprocessors for data processing tasks.
When used in combination with cloud service provider platforms, these technologies also allow the cluster size to vary in real-time according to the specific processing needs. This makes it possible to adapt systems to variable data flows, allowing the cluster size to be increased to absorb high data peaks at specific times and reduced when processing needs decrease. The flexibility is enormous and it allows optimizing the costs of using these technologies to make them accessible to any corporation.
Some of the most commonly used data processing technologies are Hadoop MapReduce, Apache Spark or Apache Lynx.
Big DATA storage technologies
Traditional relational databases weren’t designed for this type of massive data environment, so NoSQL (not only SQL) databases were created to provide a solution. There are actually many different types, with each one designed to resolve a specific scenario optimally. The most common are the so-called columnar, key-value and documentary databases.
Some of the most used Big Data database technologies are Apache Cassandra, Hbase, Amazon DynamoDB or Google BigTable.
At Teldat we use Big Data technologies to provide certain services that we offer to our clients.