What is Data Ingestion?

Data Ingestion refers to the process of collecting and importing data from various sources into a system or storage infrastructure for further processing. It involves capturing data from diverse origins, such as databases, logs, sensors, or external applications, and bringing it into a centralised location or data pipeline.

There are several methods of data ingestion, each with its own characteristics and use cases. Here are some additional methods apart from batch processing and real-time streaming:

1. Batch Processing: Collects and processes data in large volumes at scheduled intervals. Suitable for scenarios where immediate processing is not required. Commonly used for data warehousing, data transformation, and generating reports.

2. Real-time Streaming: Processes and analyzes data as it is ingested in real-time. Enables immediate insights and actions. Commonly used for real-time monitoring, anomaly detection, and instant decision-making.

3.Change Data Capture (CDC): CDC is a technique that captures and tracks changes made to data in source systems. It identifies and extracts only the modified data, reducing the amount of data transferred during ingestion. CDC is commonly used in scenarios where capturing and processing real-time data updates is critical.

4.File-based Ingestion: This method involves ingesting data from files, such as CSV, JSON, or XML files. The files are processed, and the data is extracted for further analysis. File-based ingestion is useful when data is stored in static files or when data needs to be transferred from one system to another.

5.API-based Ingestion: involves collecting and importing data using APIs for direct integration with external systems or services. It is commonly used to retrieve data from web services or cloud applications.

6.Database Replication: It involves copying data from one database to another in near real-time. This is commonly used to replicate data across multiple databases or for disaster recovery. Database replication ensures data consistency and availability across different systems.

7.Log-based Ingestion: Log-based ingestion involves capturing and processing data from log files generated by applications, systems, or devices. It is often used for monitoring, troubleshooting, and analysis purposes. Log-based ingestion provides insights into system behavior, performance, and errors.

8.Message Queueing: Message queueing is a method where data is ingested by sending messages to a message queue, which acts as an intermediary between the data source and the processing system. This method ensures reliable and scalable ingestion, especially in distributed systems.

9.Direct Database Connection: In this method, data is ingested by establishing a direct connection to a database and extracting the required data. It is commonly used when data is stored in databases and needs to be processed or transformed.

10.Data Replication: Data replication involves copying data from one storage system to another, often in real-time. It is commonly used for data backup, data synchronization, and ensuring data availability across multiple systems.

These methods offer different approaches to handle data ingestion based on the specific requirements and time sensitivity of the data processing tasks.

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