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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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Top Valuable Sources to Learn Predictive Analytics After Your Degree

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The word predictive analytics is to increase competitive advantage and at the same time to suggest the best value products to end customers. Data analytics Why you need to go for predictive analytics The reasons are Growing data Cheaper computers and servers Easy to use software Tough economic conditions More: Case Study on data analytics The predictive analytics helps in the following areas: Detecting fraud Improve marketing campaigns Reduce risk Improving operations Growing data analytics creating many job opportunities. Where You Need to Learn Do PG or post graduation in data analytics Attend Class Room Coachings
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