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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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Skills Need Every Employee By 2020

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With all the  predictions about the future of work , we are constantly painting a picture of how the future workplace will look. But, what most of us should focus on is what the workforce will  need   to look like – meaning, what skills employees need to have to ensure continued success into the next decade? The  Top 10 Online Colleges website  has the answer. They’ve done the research and compiled a list of skills that all employees will need by 2020 (which is only six years away!) and the drivers of the new skill set. First, we must understand what is driving change and molding the future of work. The six key drivers of change include: Extreme longevity:  People are living longer. The rise of smart machines and systems:  Tech will augment and extend our own capabilities. Computational world:  There will be an increase in sensors and processing that will make the world a programmable system. New media ecology:  There will...
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