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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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6 Exclusive Differences Between Structured and Unstructured data

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Here's a basic interview question for Big data engineers. Why it's basic means many Bachelor degrees now offering courses on Big data, as a beginner, understanding of data is a little tricky. So interviewers stress this point.

Don't worry, I made it simplified. So you get a clear concept. I share here a total of six differences between these. In today's world, we have a lot of data. That data is the unstructured format.

Structured Vs Unstructured data - 6 Top Differences
 

Structured Data

  1. The major data format is text, which can be string or numeric. The date is also supported.
  2. The data model is fixed before inserting the data.
  3. Data is stored in the form of a table, making it easy to search.
  4. Not easy to scale.
  5. Version is maintained as a column in the table.
  6. Transaction management and concurrency are easy to support.

Unstructured data

  • The data format can be anything from text to images, audio to videos.
  • The data model cannot be fixed since the nature of the data can change. Consider a tweet message that could be text followed by images and audio.
  • Data is not stored in the form of a table.
  • Very easy to scale.
  • Versioning is at an entire level.
  • Transaction management and concurrency are difficult to support.

References

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