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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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IoT In Healthcare Top Skills You Need

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IoT and multimedia technologies have made their entrance into the healthcare field thanks to ambient-assisted living and telemedicine. ... Role of Smart Devices Smart devices, mobile Internet, and Cloud services contribute to the continuous and systematic innovation of Healthcare and enable cost-effective, efficient, timely, and high-quality ubiquitous medical services. Skills You  Need  Data Analytics  Cloud Computing  Security  Smart Device "Pervasive healthcare applications generate a vast amount of sensor data that have to be managed properly for further analysis and processing." The adoption of Cloud in this scenario leads to the abstraction of technical details, eliminating the need for expertise in, or control over, the technology infrastructure, and it represents a promising solution for managing healthcare sensor data efficiently. It further makes mobile devices suited for health information delivery, access, and communication, also on the go, enh...
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