Your organization is facing a major database migration. What tools will help you make the transition?

An important question during the migration is - how much of the database should the organization be responsible for? If it considers that it should control the infrastructure top to bottom (e.g. For very sensitive data) some kind of on-prem solution is likely the best, or a highly secure cloud solution with appropriate SLAs and NDAs. Otherwise, cloud-based solutions can be considered quite elastic, but even then, if a lot of control is needed over the running of the DN, and other applications are desired to run alongside it, IaaS may be useful. PaaS databases, like CosmosDB, can be a good solution when the focus is more on the DB schema, configuration and code written for the DB, and handling the underlying infrastructure is not an aim.

Database migration could happen from one type to another, and also it could be *major version upgrade*. Such version upgrade could be due to License renewal / Security / Hygiene etc. While migrating just versions looks staright but needs regression since support of many default features toggle changes, runtime performance has impact based on configuration before and after. So Configuration comparison And Performance comparison on version upgrade need to be considered.

For a major database migration, consider tools like AWS Database Migration Service for cloud-based migrations, offering compatibility with various database platforms. Oracle GoldenGate facilitates real-time data integration and replication. Microsoft SQL Server Migration Assistant aids in migrating to SQL Server from other databases. Informatica's Data Migration tool is great for complex, large-scale migrations, ensuring data quality and integrity. Additionally, Talend Data Integration provides a unified platform for data integration tasks. Remember, successful migration also depends on thorough planning, testing, and ensuring data security throughout the process.

Previously I used AWS Data Migration Service so I will brief about it: Numerous source and target databases, are supported by AWS DMS. DMS lets you maintain source and target database synchronization throughout the transfer by providing continuous data replication with little downtime. Schema mapping and data transformation are also possible. Changes in the source database are captured by AWS DMS using CDC. We can use Amazon S3 as an intermediate storage for data during the migration process. DMS and AWS CloudWatch combine to provide logging and monitoring. We can setup alerts to stay informed about the migration. It supports encrypted connections between source and target databases, ensuring the security of data in transit.

In the migration, the initial step involves assessing both source and target platforms. Tools like Microsoft Data Migration Assistant, AWS Schema Conversion Tool, Oracle SQL Developer, and IBM Data Movement Tool aid in analyzing structure, data types, dependencies, compatibility, performance, and security. This comprehensive approach ensures proactive issue resolution. Also, relying solely on automated tools may oversimplify the database environment's complexity. Human expertise, understanding unique business contexts, provides insights tools might miss. Balancing automated assessments with human insight ensures a nuanced evaluation, reducing the risk of overlooking critical considerations during migration.

In My opinion, Backup and Restore tools would be the best while planning and executing DB Migration. It totally depend on the factors like how big the DB is , what is the network capacity and also if the source DB is highly transactional and we cannot afford any downtime during switchover the. Probably we need to look for some mirroring replication to ensure secondary DB will always be updated

Depending on type of data and the scope or size of data an extraction tool can be utilized for to get data from source database to intermediate format. For SQL data one can utilize SQL Server studio, MYSQL Workbench, Oracle SQL . For CSV or Excel one can utilize SSIS, Oracle Sql Server

No need to divide the migration into three steps (Extraction/Transformation/Loading) and use separate tools for each phase, if you can do it in just two steps or just one, if your migration tools are flexible enough. This will definitely speed up the data migration itself and require much less intermediate storage - esp. if you're migrating lots of databases with similar structure. Oh, and while some filtering and (re)formatting is usually required, sorting data prior to loading them into new DB is rather pointless. Unless you don't want to use indexes for some reason *and* the sorted data will either be read-only or you can assure that inserts and updates will preserve the order.

dbt (data build tool): Open-source data transformation tool designed for analytics. Offers modularity, dependency management, version control, testing, and documentation. Streamlines SQL-based transformations on top of your data warehouse. User-friendly and promotes collaboration within data teams. Even helps with data lineage with dbt docs

Transformation tools helps formatting the data for complex transformations, such as mapping, cleansing, validating, or enriching data.It can also handle data quality, integrity, and consistency issues that may arise during migration. I have utilized Pentaho Data Integration and MS SSIS for data transformation and mapping.

@Gautam: Copying sentences almost verbatim from what's already stated into your comment doesn't contribute anything of value to the article. If you don't have anything meaningful to add, why are you posting at all? Just to satisfy the quota to earn a badge? Please don't do that...

LINQ, Excel, SProc, CSV, C#, pypy, IronPython, F#, Elixir, EF, .net Framework, SSIS, data compare tools in SSMS, Visual Studio Database project

The loading tools can majorly be divided into two categories Streaming incremental load – better for loading small data volumes. Batch incremental load – better for loading large data volumes. For streaming incremental load tools or services such as Apache Nifi, Talend Pentaho, Apache kafka can be used. Batch incremental load tools such as Azure Data Factory, Kafka, pentaho Data integration, AWS Glue can be used

- Testing of the database after migration on load and performance is very crucial. There are various levels of server level variables that need to be migrated as well like “Max of parallelism” etc. - usually lots of performance related issues pops up after migrating db.

Data migration testing ensures that data is transferred accurately, formatted properly, and functions as intended in the new system. For testing tools such as ETL Validator , IBM Informix, Talend open studio .

-Having monitoring tools like NewRelic ARM or Mulesoft tools is critical for productions servers to detect any outages at the earliest to minimize the data impact.

Implementation becomes easy if strategy, planning and testing are done the right way at every step, right from dev, QA, UAT to production. Tools will only act as a bridge but your pre and post migration steps like validation testing, performance testing, server config will play a major role. Also, collaboration between the teams is of utmost importance. Query store in SQL server can be used to collect data and do performance testing post migration.

There is not one silver bullet here. Tools and migrations strategy completely depends on specific database going for migration. Downtime, size and cost is the major factor contributing to decision on right database tool for migrations.

We managed migrating over thousand Oracle DBs, spanning many terabytes of data into PostgreSQL, using nothing else but the free ora2pg tool and some bash scripting. Plus we wrote some Java programs for testing the results. We tried different migration tools (some commercial, others free), but ora2pg turned out the most flexible and capable of handling nearly anything we threw at it. (Though it wasn't the fastest one performance-wise.) It is also rather complex and requires thorough understanding of how to configure it. But allows two-phased migration (Extraction/Transformation as first step) and direct load with pqsl. And since it's a command/line tool, it can be easily scripted/automated, saving lots of human interaction and working hours.

In the face of a significant database migration, employing robust tools is crucial for a seamless transition. Start with a comprehensive assessment using tools like AWS Database Migration Service or Microsoft Data Migration Assistant, which provide insights into compatibility and potential issues. For the actual data transfer, leverage tools such as Oracle Data Pump or Microsoft's SQL Server Integration Services, ensuring efficient and secure movement of data between systems. Database administrators can benefit from tools like Flyway or Liquibase for version control and schema management, ensuring consistency throughout the migration.

In my past experience , regardless how solid the tool is , prepare validation scripts is crucial for database migration that check consistency and integrity of data once migration done to destination . Other thing is the post migration activities must be defined to avoid at the moment coordination for issues might take place after the migration