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15 Python Tips : How to Write Code Effectively

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 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index and the value in a loop, enumerate is a more Pyth

Here's to Know Data lake Vs Database

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In a data lake, data stored internally in a repository. You can call it a blob. The data in the lake a no-format data, but you need a schema for the database.  Data lake Repository Database In the database, the Schema definition you need before you store data on it. It should follow Codd's rules. Here data is completely formatted. The data stores here in Tables, so you need SQL language to read the records. Poor performance in terms of scalability. Data lake It doesn't have any format - it's just a dump. You can send this dump to the Hadoop repository for data analysis. This repository can be incremental. You can build a database. The data lake is a dump of data with no format. It needs a pre-format before it sends for analytics. Data security and encryption: You need these before you send data to Hadoop. In real-time, you need to pre-process data. This data you need to send to the data warehouse to get insights.