使用 Great Expectations 和 Databricks 确保数据质量

发布于 3 月 26 日

Data Quality Importance: Critical for production pipelines, and the Great Expectations library is popular.
Great Expectations Overview: A powerful tool for maintaining data quality by defining, managing, and validating expectations.
Integrating with Databricks: Allows automating data quality checks within Databricks workflows.
Supported Data Platforms: Includes relational databases (PostgreSQL, MySQL, etc.), data warehouses (Snowflake, Redshift, etc.), data lakes (Amazon S3, etc.), file systems (local, HDFS), and big data platforms (Apache Spark, Databricks).
Step-by-Step Guide:
- Prerequisites: Have a Databricks workspace and install Great Expectations.
- Install: Use %pip install great_expectations in a Databricks notebook.
- Initialize: import great_expectations as ge; context = ge.data_context.DataContext().
- Create and Validate Expectations: Load data into a Spark DataFrame, convert to Great Expectations DataFrame, create expectations like column existence, null values, value sets, and column means, and validate the data.
- Save and Load Expectations: Save to a JSON file using df_ge.save_expectation_suite(), and load from a JSON file using df_ge.load_expectation_suite().
- Generate Data Docs: Use context.build_data_docs() and context.open_data_docs() to generate and open data documentation.
Example Use Case: For validating a sales dataset, follow similar steps as above including loading data, creating expectations, validating, saving and loading expectations, and generating data docs.
Conclusion: This integration provides a powerful way to ensure data quality and reliability in data pipelines.

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