Data ingestion is the process of gathering various types of data from multiple sources into a single storage medium, where it can be accessed and analyzed. This process is crucial for modern organizations to make informed decisions with accurate and up-to-date information. Data ingestion involves several steps, including data discovery, acquisition, validation, transformation, and loading. The benefits of data ingestion include improved data availability, simplified data collection, enhanced data consistency, scalability considerations, cost and time savings, and increased data efficiency. However, challenges such as increased complexity, information security concerns, data integrity issues, and regulatory oversight must be addressed. Data ingestion can be approached in different ways, including batch ingestion, real-time ingestion, and micro-batch ingestion, depending on the needs of the organization. The right data ingestion tools are essential to automate the process, considering factors such as data formatting, movement frequency, scalability, and data privacy and security. Data ingestion is distinct from ETL, which is a type of data ingestion that involves extensive processing methods, including extraction, transformation, and loading.