Hash function optimisation is the process of improving how hash functions work to make them faster and more reliable. A hash function takes input data and transforms it into a fixed-size string of numbers or letters, known as a hash value. Optimising a hash function can help reduce the chances of two different inputs creating…
Privacy-preserving feature engineering refers to methods for creating or transforming data features for machine learning while protecting sensitive information. It ensures that personal or confidential data is not exposed or misused during analysis. Techniques can include data anonymisation, encryption, or using synthetic data so that the original private details are kept secure.
Schema evolution strategies are planned methods for handling changes to the structure of data in databases or data formats over time. These strategies help ensure that as requirements change and new features are added, existing data remains accessible and usable. Good schema evolution strategies allow systems to adapt without losing or corrupting data, making future…
Data quality monitoring is the process of regularly checking and assessing data to ensure it is accurate, complete, consistent, and reliable. This involves setting up rules or standards that data should meet and using tools to automatically detect issues or errors. By monitoring data quality, organisations can fix problems early and maintain trust in their…
Data stream processing is a way of handling and analysing data as it arrives, rather than waiting for all the data to be collected before processing. This approach is useful for situations where information comes in continuously, such as from sensors, websites, or financial markets. It allows for instant reactions and decisions based on the…
ETL pipeline design is the process of planning and building a system that moves data from various sources to a destination, such as a data warehouse. ETL stands for Extract, Transform, Load, which are the three main steps in the process. The design involves deciding how data will be collected, cleaned, changed into the right…
Data warehouse optimisation is the process of improving the speed, efficiency and cost-effectiveness of a data warehouse. This involves tuning how data is stored, retrieved and processed to ensure reports and analytics run smoothly. Techniques can include indexing, partitioning, data compression and removing unnecessary data. Proper optimisation helps businesses make faster decisions by ensuring information…
Log analysis pipelines are systems designed to collect, process and interpret log data from software, servers or devices. They help organisations understand what is happening within their systems by organising raw logs into meaningful information. These pipelines often automate the process of filtering, searching and analysing logs to quickly identify issues or trends.
Automated data validation is the process of using software tools to check that data is accurate, complete, and follows the required format before it is used or stored. This helps catch errors early, such as missing values, wrong data types, or values outside of expected ranges. Automated checks can be set up to run whenever…
Data virtualisation is a technology that allows users to access and interact with data from multiple sources without needing to know where that data is stored or how it is formatted. Instead of physically moving or copying the data, it creates a single, unified view of information, making it easier to analyse and use. This…