Showing posts with the label datalake

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Python Regex: The 5 Exclusive Examples

 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations: 01 Matching a Simple Pattern import re text = "Hello, World!" pattern = r"Hello" result =, text) if result:     print("Pattern found:", Output: Output: Pattern found: Hello This example searches for the pattern "Hello" in the text and prints it when found. 02 Matching Multiple Patterns import re text = "The quick brown fox jumps over the lazy dog." patterns = [r"fox", r"dog"] for pattern in patterns:     if, text):         print(f"Pattern '{pattern}' found.") Output: Pattern 'fox' found. Pattern 'dog' found. It searches for both "fox" and "dog" patterns in the text and prints when they are found. 03 Matching Any Digit   import re text = "The price of the

Here's to Know Data lake Vs Database

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.