Developing data pipelines involves several steps that include determining the goal, choosing the data sources, determining the data ingestion strategy, designing the data processing plan, setting up storage for the output of the pipeline, planning the data workflow, implementing a data monitoring and governance framework and planning the data consumption layer. The steps involved in designing a data pipeline can vary depending on the specific use case and requirements of an organization. Some common processing steps include transformation, augmentation, filtering, grouping and aggregation.

Data transformation involves cleaning, filtering, masking, aggregating or enriching raw data to ensure data integration, standardization and quality. It is a process of converting raw data into a format that can be easily analysed and used for business insights.

Data cleansing involves identifying and correcting or removing inaccurate, incomplete, irrelevant or duplicated data from a dataset. It is an important step in ensuring that the data used for analysis is accurate and reliable.

Developing data models involves creating a representation of the underlying structure of a dataset that can be used to analyze and understand the relationships between different variables. Data models can be used to help identify patterns and trends in large datasets, and can be used to make predictions about future events based on historical data. There are several types of data models, including conceptual models, logical models and physical models.

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two methods of moving data from one system to another. ETL involves extracting data from a source system, transforming it into a format that can be used by the target system, and then loading it into the target system. ELT involves extracting data from a source system and loading it into a target system before transforming it into a format that can be used by the target system.

Data enrichment involves adding additional information to a dataset to provide more context and value. This can include adding geographic information, demographic information, or other relevant details that can help to improve the accuracy and usefulness of the dataset.