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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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SQL Vs NOSQL real differences to read today

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SQL and NoSQL both or two different languages that will be used on different databases. In resolving bigdata analytics NoSQL is most popular. Where as SQL is popular in relational databases.

SQL Vs NOSQL Top Differences

sql vs nosql

SQL

  • SQL is structured query language 
  • It was first commercial language used in RDBMS 
  • SQL language is divided into multiple sub elements

NoSQL

  • Data is not in one machine or even one network. 
  • Data can be any type public data and private data 
  • Huge volume of data so you cannot put it in one place. 
  • It is uncoordinated in time as well as space. 
  • It is not always nice, structured data that SQL was meant to handle.

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