Schema evolution represents the process of altering a database schema to match various needs within your applications. This process is crucial in maintaining an adaptable database as your company grows or as requirements change over time.

It’s a topic that a candidate for the AWS Certified Data Engineer – Associate (DEA-C01) exam needs to thoroughly understand, given its application in managing data on AWS cloud systems. Several schema evolution techniques can come in handy depending on your unique needs and operations.

Table of Contents

I. Schema Versioning

Versioning is a common, often-used technique where developers manage different versions of schema. This process allows the making of changes to new versions of the schema without necessarily affecting the older versions. This enables the coexistence of multiple data versions while providing flexibility to keep or discard changes.

AWS Glue is an example where schema versioning technique is used. AWS Glue catalog stores the schema version history and when schema changes are made, it keeps track of these changes over time.

II. Data Migration

Data migration involves transferring data from one system or format to another. It can happen in situations such as when updating a system, transferring data to a different storage medium, or transforming data to fit into a new data model.

During schema evolution, data migration may involve updating existing data to match the newly updated schema. Amazon’s Database Migration Service (DMS) and AWS Glue’s “FindMatches” provide an excellent framework to duplicate data from one source to another, harmonize discrepancies, and ensure that the evolution doesn’t interfere with the existing database.

III. Schema Evolution through Avro

Apache Avro (Avro) is a popular data serialization system that supports rich data structures plus a compact, fast, binary data format. The benefits of using Avro with AWS are its native support for schema evolution and its wide compatibility – it works well with languages such as Python, C, C++, C#, Java, and more.

The Avro schema evolution technique supports differentiating reader’s and writer’s schema. This means that the schema used to write the data is not necessarily the same schema that will be used when reading. This approach is convenient in managing rapidly changing schema, where hardcoded schema becomes difficult.

IV. Nullification and Default Values

Another popular schema evolution technique involves setting fields to nullify or default values. This can be enforced when one adds new columns to data that were not originally there. The rows created before the addition of the new column will be filled with NULL or a set default value.

Amazon Redshift is an example of an AWS service where nullification is utilized during schema evolution.

Technique AWS Service
Schema Versioning AWS Glue
Data Migration AWS DMS, Glue’s “FindMatches”
Schema Evolution through Avro Amazon S3, AWS Glue
Nullification and Default Values Amazon Redshift

In Conclusion

Mastering various schema evolution techniques is a crucial part of preparing for the AWS Certified Data Engineer – Associate (DEA-C01) exam. By succeeding in this, you demonstrate not only your understanding of AWS services such as AWS Glue, Amazon Redshift, and AWS DMS, but also your capacity to handle dynamic data needs in a live business environment.

Practice Test

True or False: Changing the schema of a NoSQL database is simpler than changing the schema of an SQL database.

  • True
  • False

Answer: True

Explanation: NoSQL databases are schema-less, so they can easily adapt to changes. However, SQL databases are rigid in their schema design, which might require a lot more effort to change.

Amazon Redshift supports automatic schema evolution.

  • True
  • False

Answer: False

Explanation: Amazon Redshift does not support automatic schema evolution. Any changes to the schema need to be manually implemented.

What does schema evolution in a database refer to?

  • A. Adding new data
  • B. Deleting old data
  • C. Changes made to a database structure
  • D. Renaming a database

Answer: C. Changes made to a database structure

Explanation: Schema evolution refers to the ability to adapt to changes to a database schema that occur over time which can include adding new columns, deleting existing ones, changing data types, etc.

True or False: Schema evolution can only be performed in downtime.

  • True
  • False

Answer: False

Explanation: Many databases allow for schema evolution to take place without the need for downtime. However, it depends on the system’s capabilities and the magnitude and complexity of the changes.

Which AWS service allows schema evolution at scale?

  • A. Amazon S3
  • B. AWS Glue
  • C. Amazon EC2
  • D. Amazon RDS

Answer: B. AWS Glue

Explanation: AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics. AWS Glue can catalog your Amazon S3 data, making it easy to organize and search for specific datasets for analytics.

True or False: Schema evolution is performed once at the beginning of a database creation process.

  • True
  • False

Answer: False

Explanation: Schema evolution is a continuous process that takes place in the lifecycle of a database when changes need to be made to its structure.

Which technique involves using version numbers to manage schema evolution?

  • A. Versioning
  • B. Compatibility
  • C. Serialization
  • D. Default values

Answer: A. Versioning

Explanation: Versioning is a technique in schema evolution that assigns version numbers to different iterations of the schema to help manage their transitions.

In the context of data lakes, what is one major challenge related to schema evolution?

  • A. Data classification
  • B. Data security
  • C. Schema-on-read
  • D. Data ingestion

Answer: C. Schema-on-read

Explanation: Schema-on-read, a characteristic of data lakes, can be a challenge as it requires to infer the schema only when the data is read. This makes schema evolution more complicated.

True or False: Backward compatibility is crucial to successful schema evolution.

  • True
  • False

Answer: True

Explanation: Backward compatibility means that new versions of the schema will still work with older data. This is vital for successful schema evolution to avoid data loss or corruption.

Which of the following are considerations when planning schema evolution? (Select all that apply)

  • A. System performance
  • B. Cost implications
  • C. Backward and forward compatibility
  • D. Current weather conditions

Answer: A. System performance, B. Cost implications, C. Backward and forward compatibility

Explanation: System performance, cost implications, and compatibilities are all important considerations in schema evolution, as changes can have a direct impact on performance, can introduce additional costs, and need to be both backward and forward compatible. The weather does not affect schema evolution.

Interview Questions

What is schema evolution in AWS Glue?

Schema evolution in AWS Glue is the process of how the schema of your data changes over time and how those changes are handled.

What steps should you take to handle schema evolution in AWS Glue?

You can handle schema evolution in AWS Glue by firstly recognizing the need for schema change and then applying changes to the table schema in the AWS Glue Data Catalog or source data.

How does AWS Glue handle schema changes in tables?

AWS Glue can automatically update table schema in the Data Catalog when it discovers a new schema.

Can schema evolution handle adding new columns to the data?

Yes, schema evolution can handle adding new columns to your data in AWS Glue.

Does AWS Glue automatically update table schemas in the Data Catalog?

Yes, AWS Glue can automatically recognize and implement schema changes and update the table schema in the Data Catalog.

Can you manually handle schema changes in AWS Glue?

Yes, you can manually handle the schema changes in AWS Glue by stopping the job, applying changes to the schema, and restarting the job.

What data formats support schema evolution in AWS Glue?

The data formats that support schema evolution in AWS Glue include JSON, Avro, Apache Parquet, and ORC.

What are the benefits of schema evolution in data engineering?

Schema evolution benefits include: maintaining consistency of data across different versions, reducing the need for system downtime for schema changes, supporting data backfilling and historical data querying.

What will happen if the schema changes while a job is still running in AWS Glue?

If a schema changes while a job is still running in AWS Glue, the job might fail or result in inaccurate data because it uses the schema that was in place at the time the job was started.

Can schema evolution handle data type changes in the columns?

Yes, schema evolution can handle changes in the data types of the columns but it is more complex and needs careful handling to prevent data loss or inaccuracies.

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