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Python Regex: The 5 Exclusive Examples

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 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 = re.search(pattern, text) if result:     print("Pattern found:", result.group()) 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 re.search(pattern, 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

The story Hadoop data value less in cost than ETL

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Traditional data warehouse That isn’t to say that Hadoop can’t be used for structured data that is readily available in a raw format; because it can.In addition, when you consider where data should be stored, you need to understand how data is stored today and what features characterize your persistence options.  Consider your experience with storing data in a traditional data warehouse. Typically, this data goes through a lot of rigor to make it into the warehouse.  Builders and consumers of warehouses have it etched in their minds that the data they are looking at in their warehouses must shine with respect to quality; subsequently, it’s cleaned up via cleansing, enrichment, matching, glossary, metadata, master data management, modeling, and other services before it’s ready for analysis.  Obviously, this can be an expensive process. Because of that expense, it’s clear that the data that lands in the warehouse is deemed not just of high value, but it has a broad purpose: it