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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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R Language: Data types and structures

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To make the best of the R language, you'll need a strong understanding of the basic data types and data structures and how to operate on those. Very Important to understand because these are the things you will manipulate on a day-to-day basis in R. Everything in R is an object.

R language
The basic data types 
  • logical (e.g., TRUE, FALSE)
  • integer (e.g,, 2L, as.integer(3))
  • numeric (real or decimal) (e.g, 2, 2.0, pi)
  • complex (e.g, 1 + 0i, 1 + 4i)
  • character (e.g, "a", "swc")
The basic data structures in R
  • vector
  • list
  • matrix
  • data frame
  • factors
  • tables
Vector in R
A vector is the most common and basic data structure in R and is pretty much the workhorse of R. 
Vectors can be of two types:
  • atomic vectors
  • lists

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