Data Ingestion vs ETL
Data ingestion and ETL (Extract, Transform, Load) are related concepts for data management, but they serve different purposes and stages within the data processing pipeline.
Aspect |
Data Ingestion |
ETL |
---|---|---|
Definition |
Moving raw data from its source to a central location for storage is the first stage in the data integration process. |
The process of organizing ingested data into a predetermined structure and storing it in a repository, such as a warehouse, is known as ETL. |
What it is |
Ingestion of data is a process. Data may be ingested into a staging area in a number of ways. |
Once the data reaches the staging area, ETL processes it. Data is standardized using ETL. |
Purpose |
Creating a single, centralized location for all data is its aim. The required parties are then granted access to the repository. |
By standardizing your data, you may make it more accessible. Insights from data can be gained in this way. |
Tools |
Apache Kafka, Matillion, Apache NiFi, Wavefront, Funnel. |
Portable, Xplenty, Informatica, AWS Glue |
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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