The tech industry has experienced a surge in demand for skilled data engineers. Upskill to become a data engineer and you’ll acquire the skills needed to build and maintain a quality data architecture, which gives your organisation a competitive edge and solves its most complex problems.

Choose to study via an apprenticeship programme, and it combines classroom-based learning with real-life experience, so you can see how the theory works in practice. Study your apprenticeship with Cambridge Spark and we’ll also give you access to our online learning platform, EDUKATE.AI, which allows you to practice your new skills with real datasets in a safe sandbox environment.

At Cambridge Spark, we set the gold standard. We’re always first to market with recognised certifications and qualifications that develop new skills. We have a 99.5% pass rate with 70%+ distinction/merit grades compared to an industry average of just 33%. Enrol on our Data Engineer Apprenticeship (L5) to learn about essential data engineering tools like Python, SQL (Structured Query Language), DevOps, CI/CD (continuous integration and continuous delivery), and Git.

• Python: widely used in data analysis, artificial intelligence, scientific computing, and web development, Python reduces the need for additional code and benefits from an automation capability, which speeds up development time and simplifies data tasks for users.

• SQL: a powerful tool for managing relational databases, SQL allows teams to create, read, update, and delete data within a database, as well as define relationships and set constraints to ensure data integrity. 

• DevOps: the DevOps lifecycle consists of 8 phases, which represent the processes, capabilities, and tools needed for development: discover > plan > build > test > deploy > operate > observe > continuous feedback. In each phase, teams collaborate and communicate to maintain alignment, speed, and quality.

• CI/CD: falls under DevOps and is used to streamline and speed up the software development lifecycle by avoiding bugs and code failures, while maintaining a continuous cycle of software development and updates. 

• Git: a DevOps tool that is used to manage source code, track changes and maintain version control, which allows several developers to work together on non-linear development.

Once familiar with these tools and platforms, a wealth of opportunity opens to you because you’ll possess some of the most sought after data skills.

Understanding the data engineering lifecycle and data modelling

The data engineering lifecycle focuses on how you can collect and transform raw data into usable formats. Key processes include:

Data collection

First the raw data needs to be collated from disparate sources, such as surveys, databases, or sensors. Once in a central repository, the data is cleansed to remove errors, duplicates and inconsistencies, which improves data quality. Finally, the data is stored (either on-premises or in the cloud), so it is easily accessible by data engineers or data scientists.

Data transformation

Next, the data needs to be modified. For example, standardisation places data into the same format/structure, so it’s easily comparable, aggregation summarises detailed data into meaningful insights that allow for better analysis, and enrichment adds complementary information to the data for richer context that aids decision-making.

Where data transformation becomes more complex is when we add logic - a process that is also known as data modelling. Here, we add visualisation to create models that illustrate what data an organisation has, where it is stored, the relationship between different data types, and the data’s attributes. There are several types of data modelling, for example: 

• Conceptual data models: offer a big-picture view of the data estate.

• Logical data models: provide greater detail about data attributes and relationships.

• Physical data models: create a schema for how data is physically stored within a database. 

Data serving

In this final stage, it’s time to communicate and share with stakeholders who will start to use the data. For example, data analysis, which includes published reports or dashboards and could include ‘business intelligence’, machine learning to support forecasting, predicting and decision making, and reverse ETL (extract, load, transform) where the data is fed back to the source for further processing.

Domains that underpin the data engineering lifecycle

Within every stage of the data engineering lifecycle are 6x critical factors:

1. Security
2. Data management
3. DataOps
4. Data architecture
5. Orchestration
6. Software engineering

Creating and maintaining data analytics pipelines 

Organisations often talk about the value in their data, but until that data is transformed, it’s virtually impossible to extract any meaningful, actionable insights. 

One way to achieve this transformation is through a data analytics pipeline, which is similar to CI/CD within DevOps. A data analytics pipeline is concerned with how to operationalise data to improve the flow of information and speed time to insights. It takes place during the transformation stage when data is moved to a data warehouse or data lake, automating the manual steps involved in data transformation.

There are several types of data pipeline, including:

• Batch processing: processing several data sets at the same time during off-peak hours. Typically used when there isn’t an immediate need for the data.

• Streaming data: continuously processing information from ‘events’, such as a user interaction or IoT sensor. Typically used to provide real-time insights.

• Data integration pipelines: merges data from several sources into a single unified view. Typically used when data from different systems is in different formats/structures.

• Cloud-native data pipelines: software products that collect, cleanse, and transform data to  support decision making.

Maximising the value of business data

Today, every business has the potential to be a data business. However, the size and scale of data being generated makes it hard to collect, process and analyse data in a timely manner. Therefore, data engineering isn’t just about having the right data, but also having the business model and internal capabilities to support it.

A report from McKinsey highlights the 4 technology shifts that enable the faster creation of innovative data products: 

• Enhanced data-management efficiency: the ability to process, manage, access, and reuse data in real-time.

• GenAI: structuring data more cost-effectively to enable its broader use, which democratises data, analytics and AI.

• Increased access to real-world data: IoT technologies make capturing data quicker and more affordable, for use across a range of initiatives.

• Growing use of internal data products: leaders are treating data like a product to support different use cases.

Meanwhile, PwC advocates spending less time thinking about how much data you have, where it comes from and how to use it. It too favours the idea of first identifying where you can use data to create more value than the competitors - in other words, to start with the use case rather than the data. For data to be truly valuable to an organisation, it needs to link back to, and support, the overall vision, mission, and business strategy.

Developing soft skills to support data-driven transformation

While technical skills are important for your role as a data engineer, an apprenticeship will also teach you crucial leadership skills.

• Strategic thinking: to see what the business needs today and anticipate what it may need in the future, based on its overall vision and mission.

• Communication: to share clear, concise, compelling messages with key stakeholders and end users. As well as understand the importance of ‘active listening’ to ensure people feel heard and involved. 

• Decision making: the ability to make informed choices based on what the data is telling you - but also to empower others to make their own decisions based on their skills, knowledge and experience.

• People management: you don’t work in a vacuum. Your success relies on the input of those around you, so you need to know how to foster good relationships and pull on the skills of others when appropriate.

Become a data engineer

When you choose to study with Cambridge Spark, we set you up for success. Training is delivered via a blend of live lectures, off-the-job training, and self-paced e-learning - and you’ll be supported by our expert lecturers, technical mentors and professionally trained coaches at every step.

You’ll also be invited to join our community of 4,000+ current learners and alumni, as well as hear from some of the best minds in the business, from leading technology providers like Google Cloud Platform and Databricks. 

Discover more about our Data Engineer Apprenticeship (L5).