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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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Tokenization: Vault based Vs Vault-less

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Tokenization means generating a random number as an alternative for a Credit card (PAN) number. The random number can be Numeric/Non-numeric. It is in such a format that others cannot decode it.    *** The popular tokenization server is the Vault server. It generates a random Token for each transaction customer made at Merchant outlets. You can find how card numbers are masked with junk characters in the pdf provided. Here is the  Visa Security Tokenization Pdf. Two Top Tokenization Servers. Vault based server Vault-less server What is Tokenization? 1. Vault based server It stores a  Card number (PAN) and a Token  in a Table of the Teradata database as Key and value pairs. Due to the growing volume of transactions, the handling of the database became a big challenge for acquirers.  For each transaction, the vault server stores a record. That row contains both the Credit card number and its Token. When you use the same card multiple times, it gener...
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