Letter Soup #1 – Big Data Edition

In the first edition of Xpand IT’s Letter Soup, we explore the Big Data field. Familiarise yourself with the most important words in one of our key activity areas and identify them below in the Letter Soup puzzle. You’ve taken the first step to becoming an Expert in Big Data!

A – Analyse

We talk about Big Data when we want to analyse significant volumes of data, allowing us to obtain essential positioning for our business. The added value for your business will be higher, the more assertive you can be when analysing the available numbers.

H – Hadoop

Developed under the Apache “hat”, this is open-source software oriented to processing big sets of data. This technology allows the distributed processing of data sets in computer clusters, using simple programming models.

I – Ingestion

Data ingestion is the first step, where all gathered data initiates its journey. This is the layer where data gets categorised and prioritised, so it can then pass to subsequent layers.

I – IoT

The Internet of Things (IoT) is essentially a set of objects/devices connected through the Internet to allow gathering and exchanging data. IoT appears as an enhancing mechanism for Big Data architectures, as it presents itself as the primary source for multiple data items.

P – Processing

Of all the stages of your Data Processing Cycle, the processing stage is probably the most important. It is during this stage that the conversion of raw data into relevant information occurs.

S – Scale

Scale is one of the strengths of a Big Data architecture. This architecture allows data to be distributed across various nodes without any loss of performance.

S – Storage

Big Data inevitably implies a considerable volume of data. Thus, the data storage method is one of the most critical factors in the whole system. Above all, your storage structure must ensure speed and redundancy, and be easily scalable.

S – Streaming

Streaming is the name given to near real-time data processing mechanisms that aim to transform data and extract relevant information. The effectiveness of this process is directly proportional to the speed at which it occurs because the value of this type of data tends to decrease with time.

V – Visualization

Generating a visualisation for a Big Data architecture can be a complicated task if we think about the speed and multiplicity of relationships between the several data sets involved. The value you can extract for your business is directly related to the ease with which you can analyse your data and generate insights.

V – Volume

One of the most relevant premises behind the need for a Big Data System – the volume of data. If we think that every day, more than 3 billion emails are sent, or that about 5 billion people own a cell phone, we can easily extrapolate a set of needs for Big Data systems.

Ana PaneiroLetter Soup #1 – Big Data Edition

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