What are the most effective ways to migrate data in your data model?

Data analysis of the data that you are about to migrate into your database model is very necessary. Secondly, transformation of the data that is to be migrated into your table structure is also a very important aspect of Data MIgration.

The more familiar you are with your data's format and possible values, the easier it is to know what you can cache, store, or reformat to serve your customers' needs. Knowing the data formats used in one of the APIs I built allowed me to know how to partition and cache the data to serve 18 billion daily requests. One of the biggest challenges in migrating data from one place to another is knowing what parts of the data are more important than others. When migrating from one storage format to another, knowing which data is most searched for, what data is best for partitioning/indexing, and which data is least needed helps define how you migrate and store the data. The least used data would be a good choice for slower storage.

It is essential to know the data, volume of the database to be migrated, data types and relationships, especially the need for conversions and transformations, which can make the process more complex. Working with stages and data lakes helps a lot, both in not burdening the source database and in removing complexity at the time of migration. Whenever possible, we should use native tools for migration, but if this is not possible, we should choose a good ETL to help us with this challenge.

In case we are not talking about stand-alone independent data sets, it is better to understand the data: DB design and high-level business model. Also, we need to understand how the data sets are integrated with each other and the constraints. It is easier if the source data is perfect with all integrity checks passed and the migration is just transfer of data. If the target data needs any transformations, cleansing or conversions, we should use the right ETL tools to do that processing. Then questions like how frequently data gets added/modified at source, how much real-time the target data should reflect source etc., will help us determine whether change data capture (CDC), incremental, nightly batch or big bang.

First of all, must evaluate the data sources and comprehend their properties, such as size, type, format, quality, and complexity, before you begin data migration. It will help to detect potential problems, plan your migration strategy accordingly, and calculate the time and resources needed. Furthermore, to analyze the data sources and record their metadata, schemas, linkages, and dependencies, some tools like data modelling, data mapping, and data profiling can help you a lot.

There's no such perfect approach, approach should be based on type of data processed. Data with no dependencies should be just done with big bang, some data with daily transaction should done in parallel (or in some cases when Delta is required), incremental for some complex derived data processing. One single approach will always be conflicting with some other scenario, Hence our approach should be dynamic based on various scenarios available in our database migration scope.

The migration process must be optimized for performance, especially when dealing with large datasets. Consider parallel processing, indexing, and performance tuning.

What was learned from your data source assessment should greatly inform your migration approach. From that assessment, you have better insight into the best approach. You should have an understanding of the dependencies and potential pitfalls of the various approach options. Your assessment may shine the light clearly on the best approach or it may close the door on some of the options. The main thing is to make the best choice based on the outcome of the assessment of YOUR data. While there are many potential approaches, ultimately, each data migration has a touch of uniqueness to it since the data, itself, is unique to your environment.

You can select from a variety of migration strategies, such as Single Go, Incremental, or Hybrid, depending on the project objectives, specifications, and limitations. Every strategy has advantages and disadvantages of its own, so you must carefully consider each before choosing one. A Single Go migration, for instance, moves all the data at once and can be quicker and easier but is also more prone to mistakes and disruption. Data is moved in chunks during an incremental migration, which can be more dependable and adaptable but also more difficult and time-consuming. As a data migration expert, one should be more adaptive to the given situation.

There can be a combination of migration approaches in a single project dictated by Release Management. For System tables and module wise rollout, it can be big bang, for transactional it can be co-existence meaning parallel.

It is not that easy, In a live system with active subscribers, everything is complex - You can not touch the main data and action on it - one of the ways to back in the changeable active tables is a snapshot of a specific time - The basic design of the system should allow for migration (you should have separate fields to detect changes in time)

If the source and target are not proper RDM then you are up against a major problem . It will be costly and difficult to do the migration. The poor results will linger for months or years. At least start with a proper target. If they are proper RDMs then the task is almost trivial.

As a best practice perform data profiling on the source data to identify data quality issues, inconsistencies, and anomalies. This step helps in planning for data cleansing and transformation.

First of all, take a backup of the data, which will save you in case of failure. Then make sure your data is ready for migration from one source to another by ensuring these operations Data Cleansing, Data Transformation, and Data Validation. A brief overview of above concepts are given below: Data Cleansing: Removing or fixing erroneous, insufficient, or duplicate data is known as data cleansing. Data Transformation: The process of translating or mapping data from one format to another is known as data transformation. Data Validation: Checking the data for mistakes, inconsistencies, and abnormalities is known as data validation.

As a best practice always have backup plans in place in case the migration encounters unexpected issues. This includes having rollback procedures to revert to the previous state if needed.

It not that simple in live system. we need to migrate the data in phases as the data will keep growing. we need to take first cut of data and migrate it Once the first cut of data migrated then we need downtime for the live system and take the differential data post first cut and then migrate the same.

Once data is ready to migrate, follow the procedures that you have finalized in the migration strategy. You can improvise the approach as per the given scenario, but you should be more adaptive to this. You can use any technique to move data from one source to another by being fully aware of their pros and cons. Start from a single chunk keep an eye and evaluate whether the migrated data has the desired level of correctness and quality.

It's complicated and needs cycles of dry runs before moving data into production. The target should be to try to migrate raw data as it is in the first go and minimize the downtime window. There would be parallel execution for a few days/week to make sure the system works well on the newly migrated database.

After the migration is complete, we must verify that the target system is functioning as expected and that data quality remains high. Address any post-migration issues promptly. Continuously monitor the migrated data and the performance of the target system. Implement regular data maintenance tasks to keep data clean and optimized.

While it is important to perform these tasks, clean data should also be a focus as these contribute to creating proper reports, dashboards etc. In some cases the data might be used as a reference to other functionalities such as an external application(s) using the data.

After the successful execution of the data migration plan, you must examine the health of migrated data by data auditing and by generating a list of reports. If your data starts giving you valuable information in terms of reports and dashboards, then it means you have achieved your purpose of data migration.

Mock-runs and dress-rehearsals should always be part of the data migration plan. Business SME should use automated scripts to verify the data migrated and should be the one to give go-ahead after sanity testing.

To work with data from on-service databases to avoid any issues for the live system, we have two different approaches, - mirroring data with little lack of time (sync each 30min or less) - ETL data in a specific period to query and analyze (once a day)

Migrating data in a data model can be a complex and critical task, depending on the size and complexity of the dataset, the source and destination systems, and the specific requirements of the migration. 1. Data Assessment and Planning 2. Data Profiling, Data Cleansing and Transformation 3. Incremental Migration 4. Data Mapping and ETL Tools 5. Data Validation and Testing 6. Backup and Rollback Plan 7. Monitoring, Logging, Scalability and Performance 8. Data Security, Compliance and Documentation 9. Post-Migration Validation Remember that data migration is not a one-time event; it's an ongoing process. Regularly review and refine your data migration strategy to adapt to changing business needs and technology advancements.