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.

How does data ingestion work?

Data ingestion begins by extracting data from various sources where it was created or stored, transforming individual files, and bringing them to the appropriate destination location (data store or message queue). For an effective data ingestion process, it’s important to understand the various steps involved:

• Data collection: Collecting or extracting data from various sources, such as relational databases (RDBMS), sensors, logs, and APIs. This is the first step of data ingestion.

• Data transformation: Converting the raw ingested data into a standard format, such as JSON or CSV, and ...