Data pipeline monitoring is the process of tracking and observing the flow of data through automated systems that move, transform, and store information. It helps teams ensure that data is processed correctly, on time, and without errors. By monitoring these pipelines, organisations can quickly detect issues, prevent data loss, and maintain the reliability of their…
Category: Data Engineering
Time Series Decomposition
Time series decomposition is a method used to break down a sequence of data points measured over time into several distinct components. These components typically include the trend, which shows the long-term direction, the seasonality, which reflects repeating patterns, and the residual or noise, which captures random variation. By separating a time series into these…
Data Preprocessing Pipelines
Data preprocessing pipelines are step-by-step procedures used to clean and prepare raw data before it is analysed or used by machine learning models. These pipelines automate tasks such as removing errors, filling in missing values, transforming formats, and scaling data. By organising these steps into a pipeline, data scientists ensure consistency and efficiency, making it…
Data Sampling Strategies
Data sampling strategies are methods used to select a smaller group of data from a larger dataset. This smaller group, or sample, is chosen so that it represents the characteristics of the whole dataset as closely as possible. Proper sampling helps reduce the amount of data to process while still allowing accurate analysis and conclusions.
Hash Function Optimization
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
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
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
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
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
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…