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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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How to Work on Re-skill Legacy to Recent

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How to work on your reskill plan to grow your career from any level. Just you need to learn and be ready with proficiency. Someone will absorb you.



Skills You Need To Grow in Your Career



How to work on Re-skill Legacy to Recent



The popular IT skills you need for the digital age are — ML(Machine Learning), AR (Augmented reality), VR (Virtual reality), AI (Artificial intelligence), and IoT. 


 Skills Rank
 Machine Learning  01
 IoT                             03
 Augmented Reality 04
 Artificial Intelligence 02


That's machine learning, augmented reality, virtual reality, artificial intelligence, and the Internet of Things.


How a COBOL Developer Changed His Career


Ramanujan who has been at TCS for 26 years has himself re-skilled several times — from COBOL to client-server to ERP to cloud, before moving into more stable managerial roles. But the reskilling he's witnessing now is like none he's seen before.

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