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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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Best Practices for Handling Duplicate Elements in Python Lists

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Here are three awesome ways that you can use to remove duplicates in a list. These are helpful in resolving your data analytics solutions.  01. Using a Set Convert the list into a set , which automatically removes duplicates due to its unique element nature, and then convert the set back to a list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = list(set(original_list)) 02. Using a Loop Iterate through the original list and append elements to a new list only if they haven't been added before. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] for item in original_list:     if item not in unique_list:         unique_list.append(item) 03. Using List Comprehension Create a new list using a list comprehension that includes only the elements not already present in the new list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] [unique_list.append(item) for item in original_list if item not in unique_list] All th...
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Numpy Array Vs. List: What's the Difference

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Here are the differences between List and NumPy Array. Both store data, but technically these are not the same. You'll find here where they differ from each other. Python Lists Here is all about Python lists: Lists can have data of different data types. For instance, data = [3, 3.2, 4.6, 6, 6.8, 9, “hello”, ‘a’] Operations such as subtraction, multiplying, and division allow doing through loops Storage space required is more, as each element is considered an object in Python Execution time is high for large datasets Lists are inbuilt data types How to create array types in Python NumPy Arrays Here is all about NumPy Arrays: Numpy arrays are containers for storing only homogeneous data types. For example: data= [3.2, 4.6, 6.8]; data=[3, 6, 9]; data=[‘hello’, ‘a’] Numpy is designed to do all mathematical operations in parallel and is also simpler than Python Numpy storage space is very much less compared to the list due to the practice of homogeneous data type Execution time is ...
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Sets Vs Lists Python Programmer Tips

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Sets are only useful when trying to ensure unique items are preserved. Before sets were available, it was common to process items and check if they exist in a list (or dictionary) before adding them. List example Here unique is an empty list. Every time I compare with this list, and if it is not duplicated then the input item will append to the unique list.  >>> unique = []  >>> for name in ['srini', 'srini', 'rao', 'srini']:  ... if name not in unique:  ... unique.append(name)  ... >>> unique ['srini', 'rao'] There is no need to do this when using sets. Instead of appending you add to a set: Set example >>> for name in ['srini', 'srini', 'rao', 'srini']: ... unique.add(name)  ...  >>> unique {'srini', 'rao'} Just like tuples and lists, interacting with sets have some differences on how to access their items. You can't index them like lists an...
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