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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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Here is Sample Logic to get Random numbers in Bash

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Here's a bash script to generate a random number. You can use this logic to generate a random number, and it is useful for AWS engineers. Random number Script - Here's sample logic to get a random number RANDOM=$$ # Set the seed to the PID of the script UPPER_LIMIT=$1 RANDOM_NUMBER=$(($RANDOM % $UPPER_LIMIT + 1)) echo "$RANDOM_NUMBER" If you select UPPER_LIMIT as 100, then the result would be a pseudo-random number between 1 and 100. Her is the output after executing the script Related posts Structured Vs. Un-structured data
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