Concepts
Schema evolution is an important concept for data engineers to understand, particularly those preparing for the AWS Certified Data Engineer – Associate (DEA-C01) exam. A well-designed data architecture needs to account for changes over time, and schema evolution techniques provide a way to manage and adapt the structure of databases without the need to redesign the entire system.
Schema evolution in the context of AWS can involve several services, including Amazon RDS, Amazon Redshift, AWS Glue, and Amazon DynamoDB, among others. Let’s explore some techniques and considerations for schema evolution within AWS services.
AWS RDS
AWS RDS (Relational Database Service) supports several database engines including MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and MariaDB, each of which has some technique for managing schema changes:
- Direct Alteration of Tables: You can use SQL
ALTER TABLEstatements to add, drop, or modify columns. However, depending on the size of the table and the type of change, this can cause downtime because the table may be locked while the change is being made. - Online Schema Change Tools: Tools such as pt-online-schema-change for MySQL or the Zero Downtime Patching feature for Oracle can make schema changes without locking the table.
- Versioned Schema Management: Tools like Flyway or Liquibase allow you to apply version-controlled schema updates.
Amazon Redshift
In Amazon Redshift, the approach to schema evolution is different because of its columnar storage and massively parallel processing (MPP) architecture:
- Adding Columns: You can easily add new columns to existing tables using the
ALTER TABLEcommand without a significant impact on performance. - Dropping Columns: To drop a column, you first create a new table without the unwanted column, then migrate the data to the new table and swap the names.
- Changing Data Types: Similar to dropping columns, you create a new table with the desired column data types, migrate the data, perform a data type transformation if required, and then swap the table names.
AWS Glue
AWS Glue’s Data Catalog is a centralized metadata repository that can store schema versions. Schema evolution in AWS Glue can be managed by:
- Crawlers: AWS Glue Crawlers automatically detect schema changes and make the necessary updates to the Data Catalog.
- Schema Versioning: AWS Glue supports schema versioning which can keep track of incremental changes to schema over time.
- Compatibility Settings: You can set the compatibility mode (NONE, DISABLED, BACKWARD, FORWARD, FULL) for schema changes to ensure that existing ETL jobs continue to function even when schemas change.
Amazon DynamoDB
Schema design in Amazon DynamoDB is flexible, allowing for schema evolution with ease:
- Adding and Removing Attributes: DynamoDB is a NoSQL database that does not require predefined schemas, so you can add or remove non-key attributes without any impact on database operations.
- Secondary Indexes: You can create or delete Global Secondary Indexes or Local Secondary Indexes to change query patterns or improve access to data as requirements evolve.
- Data Type Changes: While you cannot directly change the data type of an attribute, you can work around this by adding a new attribute with the desired data type, populating it with data and migrating read/write operations to the new attribute.
Schema Changes in Practice
To illustrate how schema changes might be managed, consider a scenario in AWS RDS using MySQL where you need to add a column to a large production table. Using a tool like pt-online-schema-change, you could avoid downtime:
pt-online-schema-change --alter "ADD COLUMN new_column INT DEFAULT 0" D=mydb,t=mytable
This tool will create a copy of the table with the schema change, copy the data to the new table, and then rename the tables without locking the original table.
Comparison Chart
| Feature/Service | AWS RDS | Amazon Redshift | AWS Glue | Amazon DynamoDB |
|---|---|---|---|---|
| Adding Columns | Possible with downtime or tools | Low impact | Managed by Crawlers | Flexibly add attributes |
| Dropping Columns | Requires careful management | Create new table and migrate | Managed by Crawlers | Not applicable (flexible schema) |
| Data Type Changes | Usually requires a new table & data migration | Create new table and migrate | Schema versioning | Add new attribute with new data type |
| Indexing Changes | ALTER TABLE or Online Schema Change Tools | Re-index or sort keys | Not applicable | Add/Delete Secondary Indexes |
| Tool Support | Flyway, Liquibase | In-built ALTER TABLE | Crawlers, Compatibility Settings | Managed in application code |
| Downtime | Depends on technique and table size | Minimal for certain operations | Managed by AWS Glue | Zero downtime |
In conclusion, AWS offers several services each with their unique ways of handling schema evolution. Data engineers preparing for the AWS Certified Data Engineer – Associate exam should understand the nuances of schema evolution in various AWS services, as well as best practices to reduce or eliminate system downtime during schema changes.
Answer the Questions in Comment Section
Schema evolution refers to the process of managing changes to a database schema over time.
- (A) True
- (B) False
Answer: A
Explanation: Schema evolution is indeed the process of managing and adapting the changes to a database schema as the system evolves over time.
AWS Glue automatically handles schema evolution.
- (A) True
- (B) False
Answer: A
Explanation: AWS Glue supports schema evolution, allowing automatic schema changes to be accommodated during data loading and query operations.
Which AWS service provides a data warehouse solution that can handle schema evolution?
- (A) AWS RDS
- (B) AWS Redshift
- (C) AWS S3
- (D) AWS DynamoDB
Answer: B
Explanation: AWS Redshift is a data warehouse service that can manage schema evolution through processes such as adding new columns or changing data types.
Schema evolution can be handled manually by a Data Engineer by updating ETL scripts every time the schema changes.
- (A) True
- (B) False
Answer: A
Explanation: Although this is not a recommended practice due to its error-prone and labor-intensive nature, schema evolution can indeed be managed manually by a Data Engineer through updating ETL scripts whenever the schema changes.
In Apache Hive, adding a column with a default value is a type of schema evolution.
- (A) True
- (B) False
Answer: A
Explanation: Adding a column with a default value is a non-destructive change that is one example of schema evolution in Apache Hive.
Which of the following statements is true about schema evolution in Amazon DynamoDB?
- (A) Adding a new column requires the creation of a new table.
- (B) DynamoDB supports adding new attributes to items without affecting existing items.
- (C) Removing an attribute requires table downtime.
- (D) DynamoDB does not support schema evolution.
Answer: B
Explanation: Amazon DynamoDB is schema-less for everything except the primary key, so you can add new attributes to items without affecting existing ones.
When enabling schema evolution on AWS Glue, data partitioning is automatically handled.
- (A) True
- (B) False
Answer: B
Explanation: Data partitioning is related to how data is stored and organized rather than how the schema evolves. While AWS Glue can help with data partitioning, it is not automatically handled as part of schema evolution.
In the context of Apache Avro, which of the following is a schema evolution strategy?
- (A) Full Transitive
- (B) Forward Compatibility
- (C) Backward Compatibility
- (D) Both B and C
Answer: D
Explanation: Apache Avro supports both forward and backward compatibility as part of its schema evolution strategies.
Amazon Athena handles schema changes seamlessly without any interruption in query services.
- (A) True
- (B) False
Answer: B
Explanation: While Amazon Athena works well with changing data, certain schema changes may require updating table definitions or recreating tables depending on the nature of the change.
A common challenge in schema evolution is data consistency during the transition of schemas.
- (A) True
- (B) False
Answer: A
Explanation: One of the key challenges in schema evolution is ensuring data consistency and integrity while changes to the schema are applied.
Amazon Redshift Spectrum requires external tables to be updated manually for schema evolution.
- (A) True
- (B) False
Answer: B
Explanation: Amazon Redshift Spectrum allows querying directly against data in S3, and if the schema evolves, it doesn’t necessarily require manual updates to external tables, as it can often infer schema or use table properties to accommodate changes.
AWS Lake Formation plays a role in schema evolution by providing schema versioning and rollback capabilities.
- (A) True
- (B) False
Answer: A
Explanation: AWS Lake Formation helps with schema versioning by tracking the changes to registered schemas and allowing for rollback if necessary to previous schema versions.
Great post on Schema evolution techniques! Very useful for DEA-C01 preparation.
Could someone explain how AWS Glue handles schema evolution?
Thanks for sharing this post. It clarified many of my doubts.
Is it necessary to use schema evolution in all data engineering projects on AWS?
Can schema evolution impact performance?
Appreciate the detailed explanations on different tools supporting schema evolution.
Found the part about Avro and Parquet formats very informative. Thanks!
Are there any pitfalls to avoid when using schema evolution with AWS Data Pipeline?