Big Data Solutions
Data engineering is a foundational element for modern businesses. The current market landscape demands agility, detail and innovation, you need to keep your finger on the pulse of your clients and customers whilst monitoring operations to make sure that you’re delivering in areas crucial to your success.
A good data infrastructure allows you to easily extract insights that can deliver progress and transformation for your business.
Our Roles as Data Engineers
Data engineering secures the groundwork for your data science and analysis, it builds and consolidates your system architecture to ensure that the data you collect is accurate, useful, complete and error-free.
Data must be primed and accessible for analysis and action, data architecture plays a leading role here. Your data architecture must be holistic, resilient and future-proof, able to comprehensively deal with different data across business operations via high-quality data lakes and data pipelines.
Data Infrastructure & Engineering As A Service
We can develop new data architecture for you using a single source, or by integrating new and existing data sources together to create more effective data lakes that are easier to pipe to their endpoints.
We do not only focus on structuring your databases and testing their operation efficiency, but we also work on finding new opportunities for data acquisition, helping your business gather actionable information that can be implemented in your strategies.
The Value of Good Data Engineering
To us, data collection and analysis is not just a box-ticking exercise or an unnecessary status-quo for a modern business to adhere to, it’s a means to create value for businesses where it does not previously exist. Any and every business can grow stronger with the power of data, it may seem elusive, or unattainable, but the truth is, your business is likely already collecting highly useful data that you could use to augment your strategies, increasing revenue and enhancing value and ROI.
Design, develop and test data architectures
Well-versed in the languages of data, we will make sure your systems are singing from the same hymn sheet. We align data collection methods across databases & APIs to bring your systems together.
Optimise Data Architecture for Your and Your Business
Your data exists to serve your business. We will make sure your data architecture is optimised for its purpose.
Discover and Implement New Opportunities
We will make your databases as strong as they can be, if there are other opportunities for data collection then we will hunt them down and implement them in our architecture.
Scalability and Modernisation
We engineer future-proof systems that can scale to your business’ growth. We can migrate legacy DWHs into state-of-the-art data lakes with serverless pipeless for real-time data analysis.
Need Help With Your Data Engineering?
We’d love to help with your IT infrastructure or data pipeline problems.
Data Transformation, Preparation and Maintenance
The data engineering process transforms your data into the best possible format for use downstream.
We’ll help you translate your business problems into relevant data pipelines.
How We Respond To Data Engineering Challenges
Data may be siloed in different departments within your organisation, combatting the fragmentation of data within your existing systems is the first step towards proper data management. Engineering systems that work holistically cross-department can unify data and create more complete, efficient and active data lakes. This also greatly enhances the efficiency of moving data downstream as the architecture is no longer disparate or dispersed across different systems.
Data Structure And Format
Formatting is often overlooked in the data preparation stage. It is especially relevant to data collected and entered by hand, as this data is prone to human error and small differences in format can have massive impacts on algorithms. Dates, times, addresses and monetary values are the worst culprits and must be unified in format. Existing compound data will also be decomposed into its constituent parts. For example, a date contains 4 parts, the day (Monday to Sunday), the day of the month, the month and the year. This allows us to create actions that depend on any of these variables, e.g. what day of the week specific sales increase or decrease.
Data Collection Practices
Collecting more data is not a useful approach without prioritising key variables and segregating them from their dependencies. Whilst machine learning algorithms may revolve around core data, e.g. age, gender, shop history, etc, (in the case of customers), fringe/niche data like their geographical position and the time of year can add nuance. We will build a nuanced picture of your data needs that goes above and beyond superficial values.
Data Engineering As A Service
Legacy and SQL
We can transition data from legacy systems into modern systems. We are adept in SQL; even if legacy systems don’t use RDMS databases, modern systems still often use SQL interfaces.
Huge volumes of data has to be coupled with quality system architecture and well-planned use strategies. We can engage your business with the power of big data strategies that drive value and ROI.
Data lakes are centralised pools of data. We can streamline the ingestion of data into data lakes for piping downstream to applications and other endpoints.
We construct both ETL pipelines to move and transform data from system to database and ELT pipelines that transform data at its destination. Data can be moved and transformed via either batch processing techniques or real-time streaming.
We can construct data systems that pipe data into machine learning applications.
We can use your existing data and newly collected data to construct foundational predictive models that can be actioned across your products and services.
Why Choose Our Data Engineering Solutions?
Experienced With Google Cloud Platform & Amazon Web Services
The future of data lies in the cloud. Google Cloud Platform (GCP) and Amazon Web Services (AWS) represent two tech giants and both offer supremely effective means to scale architectures whilst benefiting from centralised, fast and secure databases, managed services and rapid product deployment.
Test-Driven Development (TDD)
We utilise Test-Driven Development (TDD) which is an agile programming style that has been proven to reduce defects and bugs whilst simultaneously tightening and optimising program features. Essentially, TDD uses short cycles of repetitive testing and tweaking to finely optimise databases and pipelines. TDD results in quicker development times and lower maintenance demands.
Object Orientated Code
In order to engineer data systems that make effective use of highly complex real-world datasets, object orientated coding is employed to define data structures together with constituent classes and functions. We can create code blueprints for real-world objects and accurately define object variables via the processes of encapsulation, abstraction, polymorphism and inheritance. The result are modular data systems that can be easily extended, manipulated by newly ingested data and dynamic to changes in attribute or behaviour.
We can run serverless pipeline architecture from AWS or GCS to increase scalability and reduce per instance running costs. Serverless pipelines experience greater uptime, are faster and result in net savings vs their traditional server counterparts.
Data Engineering Services Frequently Asked Questions
What is The Difference Between Data Engineering and Data Science?
Data engineering works in close proximity to the data source, though it does take into account the entire data pipeline from end to end. Generally, though, data engineering is about securing the bottom line architectures on which a data system is built. Data engineering is more orientated towards coding and programming, the aim is to glue systems together to create more consistent data. The data engineer is like an architect or system designer, they build the tools which extract data and make it available for modelling and analysis.
What is Big Data?
Big data has become a buzzword that encapsulates huge volumes of digital data that is analysed intensely to reveal many associations, patterns, trends and correlations. Big data techniques aim to convert macro datasets into micro information on everything from human behaviour to sensitive weather events – it is not strictly a commercial term. It has become synonymous with the big tech companies who are able to enact wide-scale data extraction and analysis but the truth is, big data is not exclusive to these businesses anymore and can be accessed by startups and SMEs.
Do I Need Data Engineering?
If you had a goldmine in your back garden, would you just leave it untouched? Data is all around us and the likelihood is, your business is already inducting it through its systems maybe without you even knowing. You can use this resource in so many ways ranging from customer service to market research and of course, sales. Building modern data systems for businesses is indeed becoming a necessity, the data is there, you just need to hire an engineer to structure your system and utilise that data to enhance your business.