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Today, companies face challenges not only related to the sheer volume of data but also, more significantly, to its extreme heterogeneity in terms of formats and sources.

Traditional structured sources are complemented by vast amounts of unstructured content, multimedia files, IoT data streams, logs, and reports, creating a highly diverse information ecosystem.

The primary goal of enterprises is to leverage their (huge) data set and make it accessible for competitiveness. However, data must be “ready to use” regardless of its nature, origin, or intended practical application by enterprises, ranging from business intelligence to training machine learning models. This encapsulates the essence of data ingestion.

The meaning of data ingestion

Among the various definitions of data ingestion, we find TechTarget’s description particularly fitting. They define it as “the process of acquiring and importing data for immediate use or storage in a database.” Thus, data ingestion represents a structured sequence of activities aimed at capturing, preparing, and transferring data for direct use or storage.

In this context, many use the term data ingestion pipeline to underscore its structured workflow nature. However, distinguishing this concept from a traditional data pipeline can be challenging, as some consider them synonymous. IBM offers a detailed perspective, defining ingestion as the initial step within the data pipeline architecture. This encompasses data extraction, control, and validation activities, followed by data transformation and storage phases.

Data ingestion, a critical process in the data age

Data ingestion is pivotal because the information essential for enterprises (e.g. constructing a 360° view of the customer or making strategic decisions) emanates from diverse sources and often in non-natively compatible formats.

In this sense, data ingestion is the most important layer of any data integration and analysis architecture: it underpins the macrocosm of business intelligence, machine learning applications, generative AI and, more generally, all projects related to data science.

IBM points out data ingestion plays a crucial role in improving data quality. This is due to the many checks and validations involved in the process, designed to ensure accurate information.

The 5 main steps of the process, from discovery to uploading

Practically speaking, what does data ingestion entail, and what are its key steps within the process (or pipeline)? Generally, it encompasses five steps:

  1. Discovery

This is the exploratory phase, during which the enterprise maps its data identifying  sources, types, and formats. It is crucial for understanding key elements such as your data’s structure, quality, and potential.

  1. Acquisition

This phase is inherently complex due to the diverse and heterogeneous nature of the anticipated data sources, spanning from databases and spreadsheets to streaming data and paper documents. Ensuring proper data acquisition is crucial to render the data usable for business objectives. The entire process can be executed through one of three methods: batch, real-time, and lambda, which integrates the benefits of both preceding approaches.

  1. Validation

The data undergoes numerous checks to ensure accuracy and consistency.

  1. Transformation

Data is adapted to suit subsequent processing activities. Different activities are envisioned, depending on the baseline data and the project’s objectives: these include, for example, standardisation and normalisation.

  1. Loading

Ultimately, the data is loaded into the storage structure, commonly (but not exclusively) a data warehouse.


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