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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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The In-and-Out of Nodes in Blockchain

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Blockchain is a decentralized technology or distributed ledger on which transactions are anonymously recorded. Which means the transaction ledger is maintained simultaneously across a network of unrelated computers or servers called “nodes”, like a spreadsheet that is duplicated thousands of times across a network of computers.


Blockchain


The ledger contains a continuous and complete record (the “chain”) of all transactions performed which are grouped into blocks

A block is only added to the chain if the nodes, which are members in the blockchain network with high levels of computing power, reach consensus on the next ‘valid’ block to be added to the chain. A transaction can only be verified and form part of a candidate block if all the nodes on the network confirm that the transaction is valid.

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