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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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Career in analytics these 3 are top tips you need

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There is a big question to be successful in analytics career. I am giving most popular ideas to be successful in this analytics career. Gettyimages.in There are absolutely three... The tip number one is you need to be familiar with different types of analytics. Web Analytics Financial Analytics Marketing Analytics Education Analytics Retail Analytics Health Care Analytics The second one is you should have right kind of training at less expensive way. In the booming Analytics Industry, one needs right course of training and experience, and working experience of case studies on selected analytics area, really boost your career. I am sure Udemy is offering lot of courses . The third rule and idea is correct mindset Assertive Communication   BE PROACTIVE - Be Proactive is about taking responsibility for your life. You can't keep blaming everything on your parents or grandparents. Proactive people recognize that they are "response-able." They don...
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