T Level Digital: Data Models, Formats and Big Data

The data content of the Pearson digital T Level cores asks more than how a single table is designed. It asks how data is organised into models, which format it travels in between systems, what the six dimensions of Big Data mean for an organisation, and how data is wrangled into a usable state without introducing new errors. That content is shared by the Digital Software Development, Digital Support and Security and Digital Data Analytics cores, which are assessed through two written papers and an employer-set project.

The quiz has twelve questions set in invented organisations. Two separate the older data models: staff records arranged as a tree, and parts that belong to many products at once. Three work with real data shown in the question: a customer record in JSON with a nested address and a list of tags, a CSV line that splits into five fields because an address contains a comma, and names that turn to garbled symbols in a 128-character set. One asks what the date, camera and size stored with a photo are called. Two name dimensions of Big Data from a scenario: smart meters reporting every few seconds, and social media posts from fake accounts. The last four cover preparing data: which wrangling step adds a region worked out from each postcode, an account number with two digits swapped, the best way to catch a mistyped email address, and which of four facts about an order is continuous. Each explanation says why the key fits and why the nearest wrong option does not.

The flashcards cover the three data models and what decides between them, each data format and character set, metadata and Blobs, the six Vs, the wrangling steps, the two kinds of entry error, the ways to prevent them and the three kinds of quantitative data.

The written work has eight tasks to answer on paper: comparing the three data models for students and courses, choosing between JSON, CSV and XML, ASCII against UTF-8 for international customers, the six Vs with an example of each, wrangling 5,000 addresses step by step, and uses of metadata. Two are longer, evaluative answers of the kind used for the higher-mark questions: check digits against double entry for account numbers, and drop-down menus and pre-filled boxes on an order form. Each has a model answer and the points a marker would look for.

There is also a short oral practice with an examiner, who reads any data aloud exactly, asks one question at a time and gives brief feedback at the end. Relational databases, entity relationship diagrams and SQL queries are practised in a separate material.

• Distinguish hierarchical, network and relational data models and the factors in choosing one
• Recognise JSON, CSV and XML and choose a format for a purpose
• Explain the difference between ASCII and UTF-8
• Explain the role of metadata
• Describe the six Vs of Big Data and apply them to a scenario
• Order and apply the steps of data wrangling
• Identify transcription and transposition errors and evaluate methods to prevent data entry errors
• Classify quantitative data as discrete, continuous or categorical

Practice material written by Zestly, based on the core content of the Pearson T Level Technical Qualifications in Digital Software Development, Digital Support and Security and Digital Data Analytics (first teaching September 2025), content area 'Data': data taxonomy, data formats, storage structures and metadata, the six Vs of Big Data, data wrangling, data entry errors and data models.