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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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Data Analytics Tutorial for COBOL Programmers

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Mainframe developers look for an alternative IT course to grow in their careers.  I have explained in this post how can they use their business knowledge. Data analytics tutorial  is a top an alternative for COBOL programmers . What is Data Analytics The field of data science is evolving into one of the fastest-growing and most in-demand fields in the world.  Organizations across industries are looking to make sense of the data they can now collect from new technologies – from predicting the next hot product to determining the risk of an infectious disease outbreak. Demand and Opportunity According to The New York Times, data science “promises to revolutionize industries from business to government, health care to academia.” As data accumulates, organizations are hiring individuals with the expertise to find meaning in the numbers and drive positive business decisions based on what they learn. It is estimated that by 2018, 4 million to 5 million jo...
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