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There are different kinds of data pipelines that can be classified into batch processing pipeline and streaming pipeline. Batch pipelines process data in batches at specific periods. Batch pipelines can be used for processing information which do not require to be available at once, for example, processing of daily reports and large data sets. Stream pipelines process data in an endless flow, constantly processing information as it is generated. Stream pipelines are useful for applications that require low-latency information, for example, transaction monitoring, IoT, and real-time analytics. There are different types of data transformation, for example, ETL and ELT. ETL means Extract, Transform, and Load, whereas ELT means Extract, Load, and Transform. In the former case, the data transformation happens before it gets into the target system, while in the latter one, data is loaded first and transformed later.

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Data pipeline architecture refers to the integration of various components involved in gathering, processing, transporting, and storing data. A basic data pipeline will have data sources, data ingestion layer, processing/transformation system, data storage, and ultimately, the end-point for analysis. Data will first go through application/database, be transformed into usable information, then eventually stored in a data warehouse. Some of the best architectures are designed with scalability, reliability, security, and ease of monitoring in mind. There are different types of architectures that may be selected based on whether the business requires batch processing or real-time processing or both. Some of the available tools in the market can be applied during different phases to manage data extraction, transformation, orchestration, and monitoring. Data pipeline architectures are also important in enabling the automation of data pipeline since recurring tasks can easily be scheduled.