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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 Testing Practices You need for DevOps Projects

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Testing is the critical phase in DevOps. The process of DevOps is to speed up the deployment process. That means there are no shortcuts in testing. Covering most relevant test cases is the main thing the tester has to focus.

DevOps Testing

Requirements 

  1. Good maintainable code
  2. Exhaustive coverage of cases
  3. Training documents to Operations team
  4. Fewer bugs in the bug tracker
  5. Less complex and no redundant code

Testing Activities  

    • The team to use Tools to check the quality of code
    • Style checker helps to correct code style
    • Good design avoids bugs in production
    • Code performance depends on the code-quality
    • Bugs in production say poor testing 

    Tester Roles 

    1. Good quality means zero bugs in production.
    2. Design requirements a base to validate testing results.
    3. Automated test scripts give quick feedback on the quality of code.
    4. Right test cases cover all the functional changes.

    The Bottom Line

    • The DevOps approach is seamless integration between Development and Operations without compromising the Quality.
    • Use test cases in a way to test all possible scenarios.
    • Automate the tasks wherever possible to get the quick reply on quality of source code.

    References

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