Introduction
This is Abe from the Lakehouse Department of the GLB Division. I wrote an article summarizing the content of the session based on the report by Mr. Gibo, who is participating in Data + AI SUMMIT2023 (DAIS) on site.
Articles about the session at DAIS are summarized on the special site below. I would appreciate it if you could see this too.
Schema drift protection to protect data accuracy and consistency
This time, I would like to talk about the talk "Streaming Schema Drift Discovery and Controlled Mitigation" which emphasizes the importance of maintaining data accuracy and consistency and provides tools and strategies to solve the problem of schema drift. I think. The speaker is Mr. Alexander Vanadio, Principal Consultant (Optiv). This talk is full of useful information for data engineers, data analysts, and other technical experts involved in data processing, as well as business managers and data operations personnel who are interested in data accuracy and consistency.
Let's get straight to the point!
The Importance of Data Accuracy and Consistency and the Issue of Schema Drift
Data accuracy and consistency are critical factors in business and research. However, as the data grows, the problem of schema drift can arise. Schema drift is a phenomenon in which the structure of data changes unexpectedly due to changes in the schema of the data source. This may compromise data accuracy and consistency.
The Importance of a Backup Plan
A backup plan is important when adding new columns to a dataframe or Delta table. This is a necessary measure to maintain data accuracy and consistency in the event of schema drift. Having a backup plan allows you to respond quickly in the event of schema drift and maintain data quality.
Schema Drift Fighting Tools and Strategies
Tools and strategies have been proposed to discover and control schema drift. The main ones are introduced below.
- Schema monitor tool: A tool that periodically monitors the schema of your data source and notifies you when it changes. This allows you to detect schema drift early and take action.
- Data Validation: The process of validating that data conforms to the correct schema. Data validation helps maintain data quality in the face of schema drift.
- Schema Evolution: A method of designing a data processing system to be flexible to schema changes. This allows the system to automatically react to schema drift and maintain data accuracy and consistency.
About the latest concepts, features and services
Recently, countermeasures against schema drift using machine learning and AI have been attracting attention. With these techniques, schema drift can be detected and remedied more efficiently. In addition, cloud services and data platforms also provide schema drift countermeasure functions, and support for maintaining data accuracy and consistency is substantial.
Data accuracy and consistency are important factors in business and research, and schema drift countermeasures are essential. It requires the use of modern tools and strategies to maintain data quality.
Simplify data processing and schema inference with Auto Loader
He explained how to use Databricks Auto Loader to simplify data processing and how to infer schemas for JSON formatted data. It emphasized the importance of maintaining data accuracy and consistency, and provided tools and strategies to solve the problem of schema drift.
What is Auto Loader in Databricks
Databricks' Auto Loader is a feature that streamlines data loading and processing. It has the following features.
- Autoload data: Automatically load new data as it is added.
- Schema Inference: Automatically infer the schema of your data to streamline data processing.
- Cooperation with cloud storage: Data can be read in cooperation with cloud storage such as AWS S3 and Azure Blob Storage.
Advantages of schema inference Schema inference has the following advantages:
- Data correctness: Schema inference ensures that the type and structure of your data is accurate.
- Data Consistency: Even if the schema changes, the data is processed according to the inferred schema and remains consistent.
- Improved development efficiency: Eliminates the need to manually define schemas, improving development efficiency.
Summary
Schema drift countermeasures are essential to maintain data accuracy and consistency. This talk demonstrated how to leverage the latest tools and strategies to maintain data quality. By leveraging these methods, you can reduce the risk of schema drift and maintain data accuracy and consistency.
Translated by Johann
