What is Data Ingestion?
Data ingestion refers to the process of importing, transferring, or loading data from various external sources into a system or storage infrastructure where it can be stored, processed, and analyzed. It’s a foundational step in the data pipeline, especially in data-driven organizations where large volumes of data are generated and collected from different sources.
Data ingestion is a critical process in modern data architectures, especially in big data and data analytics environments, as it lays the foundation for subsequent data processing, analysis, and decision-making. Efficient data ingestion ensures that organizations can leverage their data assets effectively to gain insights, drive innovation, and make data-driven decisions.
What is Data Ingestion?
The process of gathering, managing, and utilizing data efficiently is important for organizations aiming to thrive in a competitive landscape. Data ingestion plays a foundational step in the data processing pipeline. It involves the seamless importation, transfer, or loading of raw data from diverse external sources into a centralized system or storage infrastructure, where it awaits further processing and analysis.
In this guide, we will discuss the process of data ingestion, its significance in modern data architectures, the steps involved in its execution, and the challenges it poses to businesses.
Table of Content
- What is Data Ingestion?
- Why Data Ingestion is Important?
- Type of Data Ingestion
- 1. Real-Time Data Ingestion
- 2. Batch-Based data ingestion
- 3. Micro batching
- The Complete Process of Data Ingestion
- Step 1: Data Collection
- Step 2: Data Transformation
- Step 3: Data Loading
- The Data Ingestion Workflow
- Challenges in Data Ingestion
- Benefits of Data Ingestion
- Data Ingestion vs ETL
- Conclusion