Broadgate Big Data Dictionary Part One


We have been interested in Big Data concepts and technology for a while. There is a great deal of interest and discussion with our clients and associates on the subject of obtaining additional knowledge & value from data.

As with most emerging ideas there are different interpretations and meanings for some of the terms and technologies (including the thinking that ‘big data’ isn’t new at all but just a new name for existing methods and techniques).

With this in mind we thought it would be useful to put together a few terms and definitions that people have asked us about recently to help frame Big Data.

We would really like to get feedback, useful articles & different views on these to help build a more definitive library of Big Data resources.  We’ve started with a few basic terms and next month with cover some of the firms developing solutions – this is just a starting point…


Big Data Analytics is the processing and searching through large volumes of unstructured and structured data to find hidden patterns and value. The results can be used to further scientific or commercial research, identify customer spending habits or find exceptions in financial, telemetric or risk data to indicate hidden issues or fraudulent activity.

Big Data Analytics is often carried out with software tools designed to sift and analyse large amounts of diverse information being produced at enormous velocity. Statistical tools used for predictive analysis and data mining are utilised to search and build algorithms.

Big Data

The term Big Data describes amounts of data that are too big for conventional data management systems to handle. The volume, velocity and variety of data overwhelm databases and storage. The result is that either data is discarded or unable to be analysed and mined for value.

Gartner has coined the term ‘Extreme Information Processing’ to describe Big Data – we think that’s a pretty good term to describe the limits of capability of existing infrastructure.

There has always been Big Data in the sense that data volumes have always exceeded the ability for systems to process it. The tool sets to store & analyse and make sense of the data generally lag behind the quantity and diversity of information sources.

The actual amounts and types of Big Data this relates to is constantly being redefined as database and hardware manufacturers are constantly moving those limits forward.

Several technologies have emerged to manage the Big Data challenge. Hadoop has become a favourite tool to store and manage the data, traditional database manufacturers have extended their products to deal with the volumes, variety and velocity and new database firms such as ParAccel, Sand & Vectorwise have emerged offering ultra-fast columnar data management systems. Some firms, such as Hadapt, have a hybrid solution utilising tools from both the relational and unstructured world with an intelligent query optimiser and loader which places data in the optimum storage engine.

Business Intelligence

The term Business Intelligence(BI) has been around for a long time and the growth of data and then Big Data has focused more attention in this space. The essence of BI is to obtain value from data to help build business benefits. Big Data itself could be seen as BI – it is a set of applications, techniques and technologies that are applied to an entities data to help produce insight and value from it’s data.

There are a multitude of products that help build Business Intelligence solutions – ranging from the humble Excel to sophisticated (aka expensive) solutions requiring complex and extensive infrastructure to support. In the last few years a number of user friendly tools such as Qlikview and Tableau have emerged allowing tech-savvy business people to exploit and re-cut their data without the need for input from the IT department.

Data Science

This is, perhaps, the most exciting area of Big Data. This is where the Big Value is extracted from the data. One Data Scientist partner of ours described as follows: ” Big Data is plumbing and that Data Science is the value driver…”

Data Science is a mixture of scientific research techniques, advance programming and statistical skills (or hacking), philosophical thinking (perhaps previously known as ‘thinking outside the box’) and business insight. Basically it’s being able to think about new/different questions to ask, be technically able to intepret them into a machine based format, process the result, interpret them and then ask new questions based from the results of the previous set…

A diagram by blogger Drew Conway  describes some of the skills needed – maybe explains the lack of skills in this space!

In addition Pete Warden (creator of the Data Science Toolkit) and others have raised caution on the term Data Science “Anything that needs science in the name is not a real science” but confirms the need to have a definition of what Data Scientists do.


Databases can generally be divided into structured and unstructured.

Structured are the traditional relational database management systems such as Oracle, DB2 and SQL-Server which are fantastic at organising large volumes of transactional and other data with the ability to load and query the data at speed with an integrity in the transactional process to ensure data quality.

Unstructured are technologies that can deal with any form of data that is thrown at them and then distribute out to a highly scalable platform. Hadoop is a good example of this product and a number of firms now produce, package and support the open-source product.

Feedback Loops

Feedback loops are systems where the output from the system are fed back into it to adjust or improve the system processing. Feedback loops exist widely in nature and in engineering systems – think of an oven – heat is applied to warm to a specific temperature and is measured by a thermostat – once the correct temperature is reached the thermostat informs the heating element and it shuts down until feedback from the thermostat says it is getting too cold and it turns on again… and so on.

Feedback loops are an essential part of extracting value from Big Data. Building in feedback and then incorporating Machine Learning methods start to allow systems to become semi-autonomous, this allows the Data Scientists to focus on new and more complex questions whilst testing and tweaking the feedback from their previous systems.


Hadoop is one of the key technologies to support the storage and processing of Big Data. Hadoop emerged from Google and its distributed Google File System and Mapreduce processing tools. It is an open source product under the Apache banner but, like Linux, is distributed by a number of commercial vendors that add support, consultancy and advice on top of the products.

Hadoop is a framework for running applications on large clusters of commodity hardware. The Hadoop framework transparently provides applications both reliability and data motion. Hadoop implements a computational paradigm named map/reduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both map/reduce and the distributed file system are designed so that node failures are automatically handled by the framework.

So Hadoop could almost be seen as a (big) bucket where you can throw any form and quantity of data into it and it will organise and know where that data resides and can retrieve and process it. It also accepts that there may be holes in the bucket and can patch them up by using additional resources to patch itself up – all in all very clever bucket!!

Hadoop runs on a scheduling basis so when a question is asked it breaks up the query and shoots them out to different parts of the distributed network in parallel and then waits and collates the answers.


We will continue this theme next month and then start discussing some of the technology organisations involve in more detail, such as covering Hive, Machine Learning, MapReduce, NoSQL and Pig.


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Posted on : 26-07-2012 | By : richard.gale | In : Data

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