What are the most common causes of slow data migration?

Network bandwidth is akin to a highway's capacity; the more lanes (bandwidth), the more traffic (data) it can handle simultaneously. Limited bandwidth, like a congested road, slows down data migration. Factors like network congestion, configuration, latency, and security protocols can bottleneck the flow. To enhance migration speed, consider optimizing network protocols, reducing extraneous traffic, compressing data, or employing parallel or incremental transfer methods, much like adding express lanes or optimizing traffic flow on a highway.

Lack of optimization at extraction from source and insertion at target. If migration is more than a one time event, work should be done at the source so that selection of the data can be done in a reasonable amount of time. Depending on use case, it is often more reasonable to transform the data after it has been landed into target systems, rather than slow the actual migration of the data for intense processing.

Slow data migration can be attributed to various factors, but The absolute first thing you should consider is Data Complexity, that what is the volume of data or If data needs to be transformed or modified during the migration process. If you have large volume of data then it naturally takes more time. The sheer size of the dataset can contribute to slow migration. If data needs to be transformed or modified during the migration process, it can introduce additional processing time.

As with real estate (location, location, location), it's the network,network,network. That was just for fun, but often the network is the culprit. Use tools like ping and tracert for rudimentary testing. Other causes are operations that need to take place on the destination enviroment. Things like index rebuilds, constraint enablement, sometimes row-by-row processing, etc. If you know your infrastructure and your technology, you can usually head these things off before you start. My teams use a checklist that has been built over many migrations that covers both logical (row inserts) and physical (block) database migrations. Without capturing this experience in documented form, each migration can be like skydiving for the first time.

Network bandwidth is for sure one important thing for database migration. Not only data bandwidth but also thinking about what protocol should we use for transfering data. For example customer have 1Gbps link to internet but we decide to use scp or sftp for transfering data for sure you will not get full database link. Also smb is notorious for weird performance over large latency for example on 20ms latency perfromance is much better than on 40ms latency. Another thing is to check with customer when we use full bandwith for migration. Usually during business hours customers does not allow full use of link to not interrupt business processes. For sure do couple of tests before planned migration to find suitable protocols and time.

In my experience, both Data Quality and Dependencies played major role. There are many ways to perform Data Migration and it all depends on why, where, how, when and what? When you make a proper data migration strategy and early plan for resources most of the hurdles can be mitigated. However, if you are trying to migrate transactional data and if the data has dependencies on both target and source systems, planning on Data extraction, cleansing and validations prior to migration is quite important as well. It is very hard to talk about all the pit falls to be aware of. There is no one direct approach for a successful data migration and is dependent on Organization and specific use case.

Poor data quality in migration is like packing for a move without sorting or labeling your items. Issues like inaccuracies, inconsistencies, or incomplete data lead to errors, duplication, or even data loss, slowing the process. Prioritizing data cleansing, validation, mapping, and transformation is crucial. Employing data quality tools ensures that the data is well-organized, accurate, and ready for a smooth transition, much like having everything neatly boxed and labeled before a move.

Yes, I agree with Data Quality as one of the cause of slow data migration. In order to have a smoother experience with data migration, you will need to implement first if does not exist a very strong data governance model to ensure that you have a consistent data quality process by using DQ tools, techniques, business rules and involving all the stakeholders.

If we are talking about data quality we can link in loose therms with data size. In one of migrations database size was 2TB and migration time was estimated to one week due to complex migration procedure. After analysis of data was done we only migrated 5% of inital size the rest was duplicated data, temp data and data that could be easily replaced. So check your data before migration.

Having a good data quality framework is key for data transitions, data mapping, ingestions and other data related tasks. The framework for the data follows the specification which allows the data to meet that specification and abide by the types, context and relationship of data to process. Data validations, data quality firewalls will improve the quality of the data and most importantly its reliability for a smooth transformation and transition. Without a framework, measuring the quality and accuracy of data against a specification will no doubt cause mis-alignment and mis-value with the end result.

Data complexity is the intricacy involved in handling diverse data types and structures. Imagine trying to assemble a complex jigsaw puzzle with pieces from different sets - the more complex the puzzle, the longer it takes. Large, varied, or poorly structured data sets increase processing, conversion, and integration times. Simplifying data models, standardizing formats, and eliminating redundant data can streamline the migration process, much like sorting and organizing puzzle pieces before assembly.

La segmentación de los datos es otra de las causas. A veces los sistemas de información se construyen utilizando diferentes productos , cada uno con su propia BBDD, esto hace que la consolidación de los datos sea mas compleja.

"Im looking at it from my own POV, and thinking of migration similar to the migration project we completed earlier in HR." One of the important points when migrating two databases : -Always remember that each line should have a unique ID. -Avoid considering duplicate data. -Start with that making a dominant database structure, and ensure that the dominant databases can absorb all data migrated by converting, merging and standardizing the migrated data as per the standardized dominant database. -Each and every element converted, merged and standardized should not lose its authenticity (e.g. translating names)

If the data is complex or not well-structured, it can take longer to migrate. This includes data that is in different formats, data that is not properly labeled or categorized, or data that has a lot of dependencies.

Data complexity is for me the point of inflection in data migration, correlate the data with the actual purpose of it is what consumes most of the time, if you do not know where the data is going or what the data is used for you can loose perspective. The way a system translates data into valuable information is key to migrate this to new paradigms, is not enough to see it in a revolutionary new way but for certain is needed to understand how the data was treated in the past to make the migration a success; it is simple, we cannot improve something if we do not understand it.

Enhancing data security is akin to fortifying a suitcase with locks and codes. Sending an unlocked suitcase is quicker, analogous to faster data transfer, while adding layers of security slows down the process, much like the time needed to attach locks and codes to the suitcase.

For security data within database systems, there are several security items to consider that are often overlooked. -How are you securing your database backups at rest? -Are you ensuring a least-privilege model where users are only granted the minimum access needed to perform their job function? -Are you restricting administrators on the server and performing auditing of logs? -Are you ensuring users have a separate login for administration purposes, vs when performing user-level tasks? -If working with sensitive data, are you providing column or row-level access protocols for users or groups? -Are you securing data in transit when exporting data to other servers, systems, storage? Hurdles of security are worth the price of admission!

Quite often security is overlooked during migration. Well just put file on this public bucket and we will remove it after migration or something simmilar. Moving production data means that all measures implemented for protecting data must be implemented during migration. Specially in cloud. Encryption at rest and in traffic is must.

Data Security is basically the layers of safety you set to keep your data safe for unauthorized access. It's also a factor limiting Data Migration especially if the level of authorization is more. It is advised that access and authorization should be in place for data security checks yet limited. Team member can have access but the level of authorization should be limited to high-level data security personnel. It's advised that Data should have multiple layer of security and enable Two-Factor Authentication. Access control should be in place for permission or privileges.

Performing data integrity or validation checks during migration can potentially lead to a slowdown, particularly in the context of data security. The impact on migration speed is contingent upon both the volume and structure of the data being transferred. These checks involve verifying that the data meets predefined criteria or standards to ensure its accuracy and reliability. While essential for maintaining data quality, the thorough validation process introduces an additional layer of scrutiny before allowing data to proceed with migration. The complexity of these checks, coupled with the intricacies of the data's structure and the sheer volume being validated, can contribute to a reduction in the overall speed of the migration process.

Creating a repeatable ( ideally automated) process and is starting on the right path for testing. Use the scripts/use cases throughout each phase of the process. Reset databases back to a point int time snapshot after changes are made in the automated process. Verify totals, check error rates, if new data needs to be tested find the differential data to verify rates. Is there bad data in the source creating false positives, if there is an automated process, mostly you have false positives (someone is not following best practices!). Automated scripting and tools are useful, but there is no substitute for a user that has experience with the data, and system and has a keen eye for detail. They know the data, do not underestimate these users.

Poor data quality contributes to slow migration, the better the labeling and organization the data is, the quicker the task will run.

UAT, UAT, UAT! Universal Acceptance Test I cannot emphasize the importance of going through multiple tests and scenarios to ensure major and obvious issues are weeded out, however, always know that more issues might arise after going live, and that should be considered as part of the migration support level agreement.

I agree. I have reviewed many implementations that initiated Data Migration without any testing and closure criteria. The common argument is that 'the system will ensure that the data is validated'. That is an incorrect approach. One need to have a documented Data Testing and Migration Closure Criteria before loading the first record. Ideally the team should use standard reports wherever possible for Testing, review and closure

I agree. Poor planning and inadequate testing prior to migration can result in unforeseen issues, causing slowdowns. Thorough planning and testing are crucial to identifying and addressing potential bottlenecks proactively. To enhance the efficiency of data migration, addressing these factors and ensuring optimization across network, hardware, software, and processes is imperative.

Data tuning for migration is like fine-tuning a race car before a big race. It involves optimizing data performance and system efficiency to enhance throughput, responsiveness, and reliability. Techniques like indexing, partitioning, and load balancing are akin to adjusting a car's engine, tires, and weight distribution for optimal speed and performance. Analyzing data requirements, identifying bottlenecks, and applying appropriate tuning measures can significantly accelerate the migration process.

Inefficient SQL Queries: Poorly optimized queries, lack of proper indexing, or missing statistics can result in slow data retrieval and insertion.

Losing business funtionalities is often a large challenge. Frustration to slow responding system does not only frustrate the company employees but also the customers

Destination of the data also poses key factor to consider, Limitations on the destination systems, such as read/write speed or connection limitations, can affect migration speed.

One of the reason for time consuming activity during the migration is Data volume. If the Volume of data is huge it is bound to take more time and then all other reasons mentioned above will automatically become the factor for Time increase. Before anyone look at the Data Migration project, First thing to work with respective teams to reduce the data volume for Migration. And then to see if Data Migration can be done in parallel rather than sequentially.

Source and Target System Compatibility Incompatibility between source and target systems can severely hamper data migration, akin to trying to fit square pegs into round holes. Differences in data storage formats, database architectures, or software versions can lead to complex conversion processes, data integrity issues, and increased migration time. Ensuring compatibility or employing middleware solutions to bridge gaps between systems is crucial for a smooth and efficient migration process, much like using adapters to connect different types of electronic devices.

One of the major reason is monolathic vs Microsegmented workflows and lot more customisation which leads to regression UAT /QA which further is timing consuming .. so considering all best way is to have microsegmented application and less customisation and well centralised documentation on overall surround applications .

Usually, insufficient planning and strategy for the migration process can slow things down. A project manager might not have established a clear road map, including timelines, milestones, and resource allocation.

Understanding end-user use of data is crucial to migrating data successfully, but it requires significant time and planning to map out how the data is used on the front end so that a new system makes it just as easy, or perhaps easier to render the data useful.