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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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How to Access Dictionary Key-Value Data in Python

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Use for-loop to read dictionary data in python. Here's an example of reading dictionary data. It's helpful to use in real projects.


Sample Program to Read Dictionary


Python program to read dictionary data

yearly_revenue = {

   2017 : 1000000,

   2018 : 1200000,

   2019 : 1250000,

   2020 : 1100000,

   2021 : 1300000,

 }

total_income = 0

for year_id in yearly_revenue.keys():

  total_income+=yearly_revenue[year_id]

  print(year_id, yearly_revenue[year_id])


print(total_income)

print(total_income/len(yearly_revenue))


Output


2017 1000000

2018 1200000

2019 1250000

2020 1100000

2021 1300000

5850000

1170000.0



** Process exited - Return Code: 0 **

Press Enter to exit the terminal


Explanation

The input is dictionary data. The total revenue sums up for each year. Notably, the critical point is using the dictionary keys method.


References

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