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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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Industrial IoT what GE says to improve Productivity

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GE is once a top company in Heavy Engineering. This is to say items related to Thermal Power plants, Turbines, and maintenance. GE had always believed that since it knew the materials and the physics of its jet engines and medical scanners, no one could best it in understanding those machines. GE Industrial Internet  The aim is it should not share its data to third parties.    GE sets up its own IoT center.    GE is in IoT mood.    GE can improve operational efficiency by studying data from its machines like situated India and Russia. This is just an example.  GE is Targetting for Predictive Maintenace Improves industrial productivity Based on criticality productivity will zoom if maintenance carried in-time.
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