After a little more thinking I’m not sure that Big Data is a new thing… rather it is a trend that has “crossed the chasm” and moved into the mainstream. Call Detail records are Big Data and they are hardly new. In the note below I will suggest that, contrary to the long-standing Teradata creed, Big Data is not Enterprise Data Warehouse (EDW) data. It belongs in a new class of warehouse to be defined…
The phrase “Big Data” refers to a class of data that comes in large volumes and is not usually joined directly with your Enterprise Data Warehouse data… even if it is stored on the same platform. It is very detailed data that must be aggregated and summarized and analyzed to meaningfully fit into an EDW. It may sit adjacent to the EDW in a specialized platform tailored to large-scale data processing problems.
Big Data may be data structured in fields or columns, semi-structured data that is de-normalized and un-parsed, or unstructured data such as text, sound, photographs, or video.
The machinery that drives your enterprise, either software or hardware, is the source of big Data. It is operational data at the lowest level.
Your operations staff may require access to the detail, but at this granular level the data has a short shelf life… so it is often a requirement to provide near-real-time access to Big Data.
Because of the volume and low granularity of the data the business usually needs to use it in a summarized form. These summaries can be aggregates or they can be the result statistical summarization. These statistical summaries are the result of Big Data analytics. This is a key concept.
Before this data can be summarized it has to be collected… which requires the ability to load large volumes of data within business service levels. The Big Data requires data quality control at scale.
You may recognize these characteristics as EDW requirements; but where an EDW requires support for a heterogeneous environment with thousands of data subject areas and thousands and thousands of different queries that cut across the data in an ever-increasing number of paths, a Big Data store supports billions of homogeneous records in a single subject area with a finite number of specialized operations. This is the nature of an operational system.
In fact, a Big Data store is really an Operational Data Store (ODS)… with a twist. In order to evaluate changes over time the ODS must store a deep history of the details. The result is a Big Data Warehouse… or an Operational Big Data Store.