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Turning data chaos into the computable

Tuesday 10 March 2020

Jon Whiteley's picture
By Jon Whiteley

Wrangling, sorting and managing data from thousands of disparate sources is all in a day’s work for a data engineer. Here, we look at how that chaos can become the future of tech.

Turning the unintelligible into powerful intelligence is what big data is all about. While it’s easy to get caught up in the headlines of what exciting developments can bring, it’s worth remembering who’s behind it: a data engineer. In this blog, we look at the need to derive real value from data using engineering, as well as being careful to consider the legal and ethical implications it brings. All so engineers can become powerful, principled players in this data-driven world.

From overload to order

CACI IIG have incredibly skilled work to do when it comes to simultaneously working with batched and streaming data from thousands of sources in highly secure, mission critical environments. Our data engineers take great care, using the latest tech, to sort it all out to lay the foundations for processing and enrichment.

Often, thousands of data sources are required to get the right level of insight extracted. These can either be collected in batches or streamed continuously. While the latter is the best way to get a real-time, 360-degree view of data, this isn’t always possible.

When working with batched data at CACI IIG, we use data processors Hadoop and Spark. While Spark has speed on its side, Hadoop is more of an ecosystem for processing large datasets. When it comes to streaming data, we use Kafka, but for the most part we use a mix of batch and streaming data, for example using Lambda architecture.

This initial phase of gathering and wrangling data lays the foundations for the next stages – turning the chaos into something the computers and humans can work with and understand.

This next phase looks at understanding what the data is exactly – namely, whether it’s structured or unstructured, and what data schema it’s in (for example if it’s a date field, phone number or secure encrypted field). Once this is understood, the data can be transformed into a consistent language and format, which enables the computer to ‘read’ the data. At CACI IIG, we also use tools such as search and analytics engine Elasticsearch to effectively query the data and uncover unexpected insights.

Driving insights from data

Once data is all in a consistent format, it can be processed with machine learning. But how can data engineers drive the insight they want from big data?

From training and testing algorithms, to more sophisticated technology, interpreting is a huge part of a data engineer’s responsibilities. So, where do they start? Typically, at CACI IIG, we’re able to apply machine learning processes to big data, training the algorithms and verifying the results until we gain a valuable output such as actionable insight and data visualisation.

However, with data from so many disparate sources, including videos, images and social media messages, technology is becoming more sophisticated in order to keep up.

Image recognition and streaming video analysis are just a couple of the technologies that have developed in order to drive the most value and insight from masses of data. This can be used to interpret data in numerous powerful ways; from wading through security footage to understanding how a car accident occurred.

The interpretation of data is constantly being reshaped with new tech and innovations, and it has a huge impact on how we protect and understand mission critical environments.

The future of data engineering

New tech and innovations are constantly changing how data is being interpreted, but with these developments come ethical and legal responsibilities that need to be considered.

While deriving value from big data comes from engineering rather than tech, the pressure and potential is greater than ever before for data engineers to harness the best approaches for maximum results.

While at CACI IIG, we understand the responsibility that comes from powerful tech, it’s necessary to always consider again and again any legal and ethical factors that might affect the way we are processing or interpreting our data.

With public consciousness wising up to these issues, it’s in everyone’s interests to consider them, while at the same time being excited by the future of big data and what it might bring. From a language-agnostic approach to the power of engineering over tech, a new way of understanding and using data is on the horizon, as is engineering’s ability to enable it.

If you would like to know more about CACI IIG then click on the link below or if you have any questions about any of the above you can contact us here. 

Wrangling, sorting and managing data from thousands of disparate sources is all in a day’s work for a data engineer. Here, we look at how that chaos can become the future of tech.

Turning data chaos into the computable

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