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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's Python Program for List Duplicates

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Here is a program to find the item that occurs most frequently in a data structure. So why to find frequent item? Maybe it is the most purchased item on your shopping site. Perhaps it is the web page that gets hit the most often. If you are a tester, it could easily be the test that has had the most failures over the last year. Whatever it is, you want an easy way to find the data you need, and Python is here to help you. Python List duplicates Here are the two simple lists: list_1 = [1,2,3,2,3,2]  list_2 = ['a', 'b', 'a', 'b', 'c'] We can't do simple math on the individual items  since the second list contains characters. For example, it could contain the words of a book, and you want to find the most commonly used word in the work.  Also, it maybe list of UPC values for commonly purchased items. Whatever it is, all we can guarantee is that the data is probably comparable, in that we can compare one of the items to another. Yet we need to f...
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These Tips Helpful to Remove Python List and Dictionary Duplicates

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In this post, I have shared top ideas to remove duplicates from the list. Those are with Append and Dictionary. 1. How to Remove Duplicates with Append # Here is a list with duplicates list_with_duplicates = [1,2,3,12,1,2,3,4,5,6,1,2,3,7,8,9] It is simple if you follow the first-approach - brute force approach: list_without_duplicates = [] for pd in list_with_duplicates:   if pd not in list_without_duplicates:       list_without_duplicates.append(pd) print(list_without_duplicates) Result: [1, 2, 3, 12, 4, 5, 6, 7, 8, 9] This method has performance issues when the list is bigger in size.  Real-time. 2. How to Remove Duplicates with Dictionary # Here is you can convert a list to a dictionary dict_without_duplicates = dict(zip(list_with_duplicates, list_with_duplicates)) print(dictionary_without_duplicates) Result: {1: 1, 2: 2, 3: 3, 12: 12, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9} Real-time. Once again, this works and has the advantage of taking less space than duplic...
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