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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 Convert Dictionary to Dataframe: Pandas from_dict

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 Pandas is a data analysis Python library.  The example shows you to convert a dictionary to a data frame. The point to note here is DataFrame will take only 2D data. So you need to supply 2D data. 


Dictionary to Data frame example


Pandas Dictionary to Dataframe


import pandas as pd

import numpy as np

data_dict = {'item1' : np.random.randn(4), 'item2' : np.random.randn(4)}

df3=pd.DataFrame.from_dict(data_dict, orient='index')

print(df3)


Output


0 1 2 3 item1 -0.109300 -0.483624 0.375838 1.248651 item2 -0.274944 -0.857318 -1.203718 -0.061941


Explanation

Using the NumPy package, created a dictionary with random values. There are two items - item 1 and item 2. The data_dict is input to the data frame. The from_dict method needs two parameters. These are data_dict and index. Here's the syntax you can refer to quickly.


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