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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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Hadoop Skills Free Video Training

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Are you interested in the world of Big data technologies, but find it a little cryptic and see the whole thing as a big puzzle. The hadoop free video training really useful to learn quickly. Are you looking to understand how Big Data impact large and small business and people like you and me? Do you feel many people talk about Big Data and Hadoop, and even do not know the basics like history of Hadoop, major players and vendors of Hadoop. Then this is the course just for you! This course builds a essential fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through: Understanding of Big Data problems with easy to understand examples. History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop. What is Hadoop Magic which makes it so unique and powerful. Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role. And mos...
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