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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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PostgreSQL is beyond NoSQL database

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The PostegreSQL is a popular database for web applications. You can manage user data in this database conveniently.
#postgreSQL
#postgreSQL

What is PostgreSQL

Web applications started using NoSQL databases. PostgreSQL is updating their database to meet the requirements of web applications. So PostgreSQL is almost equal to NoSQL database.

Java Script

PostgreSQL supports JSON (JavaScript Simple Object Notation) . JSON is portable data format to share data. The MongoDB follows JSON.
PostgreSQL's structured format for saving JSON, called JSONB, eliminates the need for restructuring a document before it is committed to the database.

Benefits 

  1. PostgreSQL is similar to MongoDB to ingest documents 
  2. PostgreSQL follows ACID compatibility 
  3. PostgreSQL have all the features and options to edit JSON data.

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