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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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5 Python File Modes You Need

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Here're top five Python file modes explained. The purpose is to open, read, write the files. There are occasions you need to deal with data, which is present in the files. You need to give correct file-modes to handle the files in Python. Python file open modes 5 Python File Modes You Need Here's an example code how to you can use file mode:   filename = input ( 'Enter a filename : ' ) f1 = open (filename, 'mode' ) 1- Python File mode w   To open the file for writing, you need 'w' mode. The beauty of this is If the file does not exist, it creates one. This mode's purpose is to write the file. If you try to read, you will get an error. 2- Python File mode w+ To Open the file for Reading and Writing, you need 'w+' mode. For instance, you used w+, you have tried to read the file - after writing, it displays blank. The reason is after writing cursor position will point at the end of the file. 3- Python File mode a It appends the records at the...
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