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Azure Data Factory
An Azure service for ingesting, preparing, and transforming data at scale.
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Testing data consistency of Parquet files with business users requires a readable and user-friendly approach that doesn't involve inefficient workarounds like converting to CSV or Excel repeatedly. Here are efficient methods to address the problem:
- Leverage BI Tools for Visualization
- Tools: Power BI, Tableau, or Excel Power Query.
- Approach:
- Use these tools to connect directly to the Parquet file.
- Visualize the data in tabular or graphical formats that business users can validate.
- These tools allow direct inspection without requiring conversion.
- Visualize the data in tabular or graphical formats that business users can validate.
- Use these tools to connect directly to the Parquet file.
- Query Parquet Files Using SQL Interfaces
- Tools: Azure Synapse Analytics, Azure Databricks, or Apache Spark.
- Approach:
- Load Parquet files into a temporary external table in Synapse, Databricks, or other SQL-compatible environments.
- Provide business users access to query the data using SQL, which is often more comfortable for users familiar with relational databases.
- Users can validate the data consistency through pre-defined queries or ad hoc analysis.
- Provide business users access to query the data using SQL, which is often more comfortable for users familiar with relational databases.
- Load Parquet files into a temporary external table in Synapse, Databricks, or other SQL-compatible environments.
- Use Lightweight Web-Based Data Explorers
- Tools: Data Explorer (in Azure or open-source), Jupyter Notebooks, or streamlit-based apps.
- Approach:
- Build or use existing tools to enable web-based exploration of Parquet data.
- The explorer can provide search, filter, and export options for users to review data without needing specialized tools.
- Build or use existing tools to enable web-based exploration of Parquet data.
- Create Temporary Readable Outputs
- Format: JSON (preferred over CSV for structure preservation).
- Approach:
- If necessary, convert the Parquet files to JSON format with a small, representative sample of the data.
- Share JSON samples with business users using an easy-to-use viewer like an online JSON editor or reader.
- If necessary, convert the Parquet files to JSON format with a small, representative sample of the data.
- Use Azure Data Explorer or Synapse Notebooks
- Tools: Synapse Studio Notebooks, Databricks Notebooks.
- Approach:
- Use these notebooks to load Parquet files and display the data in tabular format.
- Share access or render the output as HTML/PDF for users to review.
- Use these notebooks to load Parquet files and display the data in tabular format.
- Leverage Custom Apps for Validation
- Tools: Develop simple dashboards using Python (streamlit) or Power Apps.
- Approach:
- Build an interactive interface that reads Parquet data and lets users validate it in real-time.
- Add search and filtering capabilities tailored to business requirements.
- Build an interactive interface that reads Parquet data and lets users validate it in real-time.
Recommendations
The most scalable and user-friendly method would be to use a BI tool like PBI or Synapse SQL with a direct connection to the Parquet data. This avoids manual data transformations and empowers users to query and validate data on demand. For more technical users, Jupyter Notebooks or SQL-based exploration would be optimal.
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