Introduction to Data Ingestion

What is data ingestion

Data ingestion is the process of collecting, processing, and loading big data from disparate sources to a central location for further processing and analysis. This is a critical step in the big data analytics pipeline because it involves collecting data from various sources and transforming it into a standardized format that can be easily analyzed. Big data platforms rely on the data ingestion process to ensure a smooth flow of data through the various stages of the pipeline.

Data ingestion is a crucial first step in big data analytics, and it is often considered one of the most challenging tasks. According to a report by Appen, as much as 25% of a data team’s time is spent on this step. Given its importance and complexity, it’s critical to understand the benefits of data ingestion for big data analytics.

• Flexibility: The data ingestion process can handle various data formats, including unstructured data.

• Simplicity: When combined with extract, transform, and load (ETL) processes, data ingestion enables the restructuring of enterprise data into predefined formats, making it easy to use.

• Analytics: Data ingestion is widely used to enable valuable business insights from various data sources, which can be leveraged using analytics tools.

• Availability: Data ingestion provides data and data analytics to data scientists and data engineers faster, making it available for further analysis.

• Decision-making: The key benefit of data ingestion is that it enables businesses to use analytics derived from ingested data to make data-informed decisions.