Most data ingestion pipelines include the following steps:
1. Data discovery
The purpose of data discovery is to find, understand, and access data from numerous sources. It is the exploratory phase where you identify what data is available, where it is coming from, and how it can be used to benefit your organization. This phase involves asking questions, such as what kind of data do we have? Where is it stored? How can we access it?
Data discovery is crucial for establishing a clear understanding of the data landscape. This step enables us to understand the data’s structure, quality, and potential for usage.
2. Data acquisition
Once the data has been identified, the next step is data acquisition. This involves collecting the data from its various sources and bringing it into your system. The data sources can be numerous and varied, ranging from databases and APIs to spreadsheets and even paper documents.
The data acquisition phase can be quite complex, as it often involves dealing with different data formats, large volumes of data, and potential issues with data quality. Despite these challenges, proper data acquisition is essential to ensure the data’s integrity and usefulness.
3. Data validation
In this phase, the data that has been acquired is checked for accuracy and consistency. This step is crucial to ensure that the data is reliable and can be trusted for further analysis and decision making.
Data validation involves various checks and measures, such as data type validation, range validation, uniqueness validation, and more. This step ensures that the data is clean, correct, and ready for the next steps in the Data Ingestion process.
4. Data transformation
Once the data has been validated, it undergoes a transformation. This is the process of converting the data from its original format into a format that is suitable for further analysis and processing. Data transformation could involve various steps like normalization, aggregation, and standardization, among others.
The goal of data transformation is to make the data more suitable for analysis, easier to understand, and more meaningful. This step is vital as it ensures that the data is usable and can provide valuable insights when analyzed.
5. Data loading
Data loading is where the transformed data is loaded into a data warehouse or any other desired destination for further analysis or reporting. The loading process can be performed in two ways—batch loading or real-time loading, depending on the requirements.
Data loading is the culmination of the data ingestion process. It’s like putting the final piece of the puzzle in place, where the processed data is ready to be utilized for decision-making and generating insights.