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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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Storage Node Vs Compute Node

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Here are the differences between compute node vs storage node Nodes are two types. Those are compute and storage. The compute node process business logic whereas the storage node stores the data.

Compute node Vs. Storage node



compute node vs storage node


Compute Node

  • A computer (machine) where you can execute actual business logic.
  • The two parameters it might have are RAM and CPU.


Compute node


Storage Node

  • Stores the processing-data where your file system resides
  • Compute and storage nodes you can find at one location.
  • It designates block storage.

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