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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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Here is a Quick Way to Know Current Working Directory in R

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If R is not finding the file you are trying to read then it may be looking in the wrong folder/directory. If you are using the graphical interface you can change the working directory from the file menu.

List of Files and Current Working Directory

Related: JOBS in R Language

If you are not sure what files are in the current working directory you can use the dir() command to list the files and the getwd() command to determine the current working directory:
> dir()
[1] "fixedWidth.dat" "simple.csv"     "trees91.csv"    "trees91.wk1"[5] "w1.dat"
> getwd()
[1] "/home/black/write/class/stat/stat383-13F/dat"

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