Database Migration - CDC resources for Oracle, Postgresql, MSQL, Bigquery, Redshift

At databasemigration.dev, our mission is to provide comprehensive information and resources on database data migration, data movement, CDC change data capture, and WAL log exporting. We aim to empower businesses and individuals with the knowledge and tools they need to successfully manage and optimize their database systems. Our goal is to be the go-to source for reliable, up-to-date information on all aspects of database migration and management, and to foster a community of like-minded professionals who can share their expertise and insights.

Introduction

Database migration is the process of moving data from one database to another. It is a complex process that requires careful planning and execution. This cheatsheet is designed to provide a comprehensive overview of the concepts, topics, and categories related to database migration, data movement, CDC change data capture, and WAL log exporting. It is intended to be a quick reference guide for anyone who is getting started with database migration.

Database Migration

Database migration is the process of moving data from one database to another. There are several reasons why organizations may need to migrate their databases. Some of the common reasons include:

• Upgrading to a new version of the database software
• Moving to a new database platform
• Consolidating databases
• Merging databases from different sources

Regardless of the reason for the migration, there are several key steps that need to be followed to ensure a successful migration:

1. Planning: The first step in any database migration project is to plan the migration. This involves identifying the data that needs to be migrated, the source and target databases, and the migration method.

2. Testing: Before migrating the data, it is important to test the migration process to ensure that it works as expected. This involves testing the migration process on a small subset of the data to identify any issues that may arise.

3. Migration: Once the testing is complete, the actual migration can begin. This involves moving the data from the source database to the target database using the chosen migration method.

4. Validation: After the migration is complete, it is important to validate the data to ensure that it has been migrated correctly. This involves comparing the data in the source and target databases to ensure that they match.

Data Movement

Data movement is the process of moving data from one location to another. This can include moving data between databases, moving data between servers, or moving data between different storage systems. There are several methods that can be used for data movement, including:

1. Bulk data movement: This involves moving large amounts of data in a single operation. This can be done using tools like SQL Server Integration Services (SSIS) or Oracle Data Integrator (ODI).

2. Incremental data movement: This involves moving only the changes that have been made to the data since the last migration. This can be done using tools like Change Data Capture (CDC) or log shipping.

3. Real-time data movement: This involves moving data in real-time as it is being generated. This can be done using tools like Apache Kafka or AWS Kinesis.

CDC Change Data Capture

CDC (Change Data Capture) is a method of capturing changes made to a database and replicating them to another database. This can be used for data movement, as well as for other purposes like data warehousing and business intelligence. CDC works by capturing the changes made to the database at the transaction level and storing them in a separate log. This log can then be used to replicate the changes to another database.

There are several benefits to using CDC for database migration:

1. Faster migration: CDC allows for incremental data movement, which means that only the changes made since the last migration need to be moved. This can significantly reduce the time required for the migration.

2. Minimal downtime: Because CDC allows for incremental data movement, it is possible to perform the migration with minimal downtime. This can be especially important for organizations that need to keep their systems up and running 24/7.

3. Data consistency: CDC ensures that the data in the target database is consistent with the data in the source database. This can help to prevent data loss or corruption during the migration process.

WAL Log Exporting

WAL (Write-Ahead Logging) is a method of logging changes made to a database. This log can be used for a variety of purposes, including database recovery and replication. WAL log exporting involves exporting the WAL log from one database and importing it into another database. This can be used for data movement, as well as for other purposes like database replication and disaster recovery.

There are several benefits to using WAL log exporting for database migration:

1. Faster migration: Like CDC, WAL log exporting allows for incremental data movement, which means that only the changes made since the last migration need to be moved. This can significantly reduce the time required for the migration.

2. Minimal downtime: Because WAL log exporting allows for incremental data movement, it is possible to perform the migration with minimal downtime. This can be especially important for organizations that need to keep their systems up and running 24/7.

3. Data consistency: WAL log exporting ensures that the data in the target database is consistent with the data in the source database. This can help to prevent data loss or corruption during the migration process.

Conclusion

Database migration is a complex process that requires careful planning and execution. This cheatsheet provides a comprehensive overview of the concepts, topics, and categories related to database migration, data movement, CDC change data capture, and WAL log exporting. By following the best practices outlined in this cheatsheet, organizations can ensure a successful migration that minimizes downtime, reduces the risk of data loss or corruption, and ensures data consistency between the source and target databases.

Common Terms, Definitions and Jargon

1. Database: A collection of data that is organized and stored in a structured way.
2. Data Migration: The process of moving data from one database to another.
3. Data Movement: The process of transferring data from one location to another.
4. CDC (Change Data Capture): A technique used to capture changes made to a database.
5. WAL (Write-Ahead Logging): A technique used to record changes made to a database.
6. Exporting: The process of extracting data from a database and saving it in a different format.
7. Importing: The process of loading data into a database from an external source.
8. ETL (Extract, Transform, Load): A process used to move data from one database to another while transforming it along the way.
9. Schema: The structure of a database that defines the tables, fields, and relationships between them.
10. Table: A collection of related data organized into rows and columns.
11. Field: A single piece of data within a table.
12. Primary Key: A unique identifier for a record in a table.
13. Foreign Key: A field in one table that refers to the primary key of another table.
14. Index: A data structure used to improve the performance of database queries.
15. Query: A request for data from a database.
16. SQL (Structured Query Language): A programming language used to manage and manipulate data in a database.
17. NoSQL: A type of database that does not use a traditional relational model.
18. Relational Database: A database that organizes data into tables with relationships between them.
19. Document Database: A type of NoSQL database that stores data in documents.
20. Key-Value Database: A type of NoSQL database that stores data as key-value pairs.

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