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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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Machine Learning Quick Tutorial - Part:1

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The following are the list of languages useful for Machine learning. There's no such thing as one language being "better" than another. It's a case of picking the right tool for the job. Your Resume has value if you put any one of these languages. Python The Python language has increased in usage because it's easy to learn and easy to read. Python has good libraries such as scikit-learn, PyML, Jython and pybrain. R R is an open-source statistical programming language. The syntax is not the easiest to learn, but I do encourage you to have a look at it. It also has a large number of machine learning packages and visualization tools.  The R-Java project allows Java programmers to access R functions from Java code. Matlab The Matlab language is used widely within academia for technical computing and algorithm creation. Like R, it also has a facility for plotting visualizations and graphs. Scala A new breed of languages is emerging that takes advantag...
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