"Big Data" is one trend that is taking the tech world by storm, shifting paradigms and creating a higher demand for IT professionals. Whether or not you're ready for it, in the words of one big data expert, "The avalanche has already begun"

However it is important to understand that big data solutions augment the functionality provided by traditional information management solution components. The key big data solution components are as follows:

§    Information sourcing
Big data involves a new variety of data sources and huge volumes of data that need to be sourced. Some big data sources include weblogs (from e-commerce sites) and social media feeds from Twitter and Facebook. Machine data from sensors on an oil rig or manufacturing plant.

§    Information integration and exchange
These new data types need new styles of ingestion and integration. Big data can be integrated using ETL tools with support for big data such as Informatica, DataStage, or Talend or even with tools that work within the Hadoop ecosystem such as Apache Hive or Apache Pig. Hive and Pig are good at loading unstructured, structured, and semi-structured data into the Hadoop distributed file system (HDFS).

§    Information reservoirs/lakes
The concept of information reservoirs and lakes has emerged with the rise of semi-structured and unstructured data in the big data landscape. These include both structured repositories such as traditional data warehouses and data marts and repositories such as HDFS to store unstructured and semi-structured data.

§    Information visualization
As the information lakes have both structured and semi-structured data there is a need for visualization tools that can search and present to users a unified view of both structured and unstructured data.