Benefits of Data Ingestion
Numerous organizations make substantial use of data intake. Typical instances of data intake include the following:
- Transferring data to cloud services like Azure from a variety of sources. Other cloud and on-premises servers that use data intake pipeline are similar to Azure.
- Data streaming from several databases to the Elasticsearch server. One term for this may be streaming ingestion.
- Handling the log files. Logs include a variety of information that is quite significant, particularly when it comes to online enterprises.
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
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