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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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Cloud Storage as a Service Basics(1 of 3)

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Cloud storage is a model of networked enterprise storage where data is stored in virtualized pools of storage which are generally hosted by third parties. Hosting companies operate large data centers, and customers that require their data to be hosted buy or lease storage capacity from these hosting companies.

The data center operators virtualize the resources according to customer requirements and expose them as storage pools, which the customers can use to store data. Physically, the resource may span multiple servers and multiple locations. The safety of the data depends upon the hosting companies and on the applications that leverage the cloud storage.

Cloud storage is based on highly virtualized infrastructure and has the same characteristics as cloud computing in terms of agility, scalability, elasticity, and multi-tenancy. It is available both off-premises and on-premises. 

While it is difficult to declare a canonical definition of cloud storage architecture, object storage is reasonably analogous. Cloud storage software such as OpenStack Cinder, cloud storage products like EMC Atmos® and Hitachi Content Platform, and distributed storage research projects like OceanStore or VISION Cloud are examples of object storage and infer the following guidelines.

Cloud storage is:

  • Made up of many distributed resources but still acts as one; often referred to as federated storage clouds. 
  • Highly fault tolerant through redundancy and distribution of data. 
  • Highly durable through the creation of versioned copies. 
  • Typically eventually consistent with regard to data replicas. 


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