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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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Infosys Looking for New Opportunities in Cutting Edge Software

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Analytical skills
Analytical skills
"I want us to be there in the great problems that are emerging around artificial intelligence techniques, deep data science and big data techniques, analytics and so on.

New Opportunities


Finding new energy sources, digital oil fields. This is the big thing in the minds of people in the oil and gas industry," Sikka said.

Vishal Sikka also told that Infosys could build a computer like IBM's Watson from scratch. Watson is an artificially intelligent computer system that can answer questions posed in normal language and can be used to help make decisions.

Watson Power


In January this year, IBM announced that it would create a business unit around Watson. IBM CEO Virginia Rometty has said she wants Watson to be a $10 billion business.

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