A feature engineering pipeline is a step-by-step process used to transform raw data into a format that can be effectively used by machine learning models. It involves selecting, creating, and modifying data features to improve model accuracy and performance. This process is often automated to ensure consistency and efficiency when handling large datasets.
Category: Data Engineering
Feature Store Implementation
Feature store implementation refers to the process of building or setting up a system where machine learning features are stored, managed, and shared. This system helps data scientists and engineers organise, reuse, and serve data features consistently for training and deploying models. It ensures that features are up-to-date, reliable, and easily accessible across different projects…
Analytics Sandbox
An analytics sandbox is a secure, isolated environment where users can analyse data, test models, and explore insights without affecting live systems or production data. It allows data analysts and scientists to experiment with new ideas and approaches in a safe space. The sandbox can be configured with sample or anonymised data to ensure privacy…
Data Reconciliation
Data reconciliation is the process of comparing and adjusting data from different sources to ensure consistency and accuracy. It helps identify and correct any differences or mistakes that may occur when data is collected, recorded, or transferred. By reconciling data, organisations can trust that their records are reliable and up to date.
Data Deduplication
Data deduplication is a process that identifies and removes duplicate copies of data in storage systems. By keeping just one copy of repeated information, it helps save space and makes data management more efficient. This technique is often used in backup and archiving to reduce the amount of storage required and improve performance.
Data Enrichment
Data enrichment is the process of improving or enhancing raw data by adding relevant information from external sources. This makes the original data more valuable and useful for analysis or decision-making. Enriched data can help organisations gain deeper insights and make more informed choices.
Data Cleansing Strategy
A data cleansing strategy is a planned approach for identifying and correcting errors, inconsistencies, or inaccuracies in data. It involves setting clear rules and processes for removing duplicate records, filling missing values, and standardising information. The goal is to ensure that data is accurate, complete, and reliable for analysis or decision-making.
Data Validation Framework
A data validation framework is a set of tools, rules, or processes that checks data for accuracy, completeness, and format before it is used or stored. It helps make sure that the data being entered or moved between systems meets specific requirements set by the organisation or application. By catching errors early, a data validation…
Data Quality Monitoring
Data quality monitoring is the ongoing process of checking and ensuring that data used within a system is accurate, complete, consistent, and up to date. It involves regularly reviewing data for errors, missing values, duplicates, or inconsistencies. By monitoring data quality, organisations can trust the information they use for decision-making and operations.
Customer Data Integration
Customer Data Integration, or CDI, is the process of bringing together customer information from different sources into a single, unified view. This often involves combining data from sales, support, marketing, and other business systems to ensure that all customer details are consistent and up to date. The goal is to give organisations a clearer understanding…