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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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2 Tricky Examples Python List Comprehension

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Here are the tricky examples of list comprehension. These examples useful for your project and interviews as well. 1. Printing odd numbers Reminder checking logic used here. If it is not equal to zero when you divide the number with 2, it treats the input number as odd and prints it. lst1 = [x for x in range(40) if x % 2 != 0] print('First 20 Odd Numbers:') print(lst1) 2. Printing even numbers Reminder checking logic used here. If it is equal to zero when you divide the number with 2, it treats the input number as even and prints it. lst2 = [x for x in range(40) if x % 2 == 0] print('First 20 Even Numbers:') print(lst2) Output from the Python scripts First 20 Odd Numbers:  [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39] First 20 Even Numbers:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38]  ** Process exited - Return Code: 0 ** Press Enter to exit terminal Related The real use of Git in Dev Ops
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