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Data Ingestion Architecture

Data ingestion architecture refers to the framework and processes involved in collecting, processing, and storing data from various sources into a data storage system, such as a data warehouse or data lake. This architecture is crucial for ensuring that data is available, reliable, and ready for analysis.

A well-designed data ingestion architecture typically includes several key components. First, it should support different types of data ingestion methods, such as real-time streaming and batch processing. Real-time ingestion is essential for time-sensitive data, while batch ingestion is suitable for periodic data collection 3.

Additionally, the architecture should incorporate data transformation processes, which can occur either before or after data ingestion, depending on the chosen approach (ETL vs. ELT). The architecture must also address challenges such as data complexity, security, and reliability to ensure that the data remains valuable and accurate throughout the ingestion process 35.

Finally, monitoring and orchestration tools are vital for managing the data ingestion pipeline, ensuring that data flows smoothly from source to destination while maintaining quality and performance 1.

Follow These Best Practices for High-Quality Data Ingestion

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