What is Data Migration? Guide to Data Migration Solutions and Planning

Luke Smith
Enterprise Solutions Architect
July 1, 2022
Matt Tanner
Developer Relations Lead
July 1, 2022
Matt Tanner
Developer Relations Lead
July 1, 2022
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Table of Contents

What is data migration?

Data migration is the process of moving data from one storage solution to another. Even though the premise is simple, the process is quite complex. When migrating data, you may be required to reformat or transform the data, change the database schema, or redo your database and/or application logic, such as refactoring stored procedures based on schema changes.

The need for data migration often arises when moving data from storage solutions that are no longer supported by the organization or moving to a more modern database to support the latest use cases. This may also entail projects such as moving from an on-prem solution to one that is hosted in the cloud. Another use case in modern enterprises that are embracing the big data movement is migrating data to improve data availability to other applications which may need access.

In the data migration process, it is very important to ensure the security and integrity of the data. Therefore, you should carefully analyze and choose the right data migration solution and have a robust data migration plan. Choosing the right platform can be the difference between a seamless migration and one that is fraught with bugs, potential data leaks, and data integrity issues.

What is the difference between data migration and ETL?

ETL, short for “Extract, Transform, Load”, is the process of extracting data from a source location, transforming it based on a set of requirements, and loading it into a target location. Data migration is the process of transferring data between repositories, systems, or formats.

Unlike ETL, the data migration process also includes data prefiling, cleaning, data validation, and data quality checks in the target system. These actions are necessary, since various exceptions may occur during the data conversion process. These issues could arise from the requirements of operation systems, the way files are created, etc. There are many concerns to keep an eye on when transforming data and moving it into a new solution.

Another important concept is data integration. This is the process of combining data from different sources into one. Doing this can provide users with all the data in a single unified view. This approach can quickly solve data access issues and allow for new use cases that were not possible when data was siloed.

Data migration and integration processes include similar steps:

  • ETL: ETL is still part of the migration and integration process, but with some additional steps listed below.
  • Standardization: The process of introducing certain rules for entering and storing data.
  • Data cleaning: Correcting the data structure and the actual data itself.
  • Reconciliation: Verifying that the data migrated to the target location matches the data in the source systems or the expected output.

Since the data migration process is quite complex and involves a number of important steps, it requires a lot of time and highly-skilled engineers to successfully pull it off. Often, data migration projects lack the investment in planning for all the work and possible outcomes of the migration. This can impact the time, budget, and success of the project. Within digital transformation projects, data migration can be one of the toughest and highest risk pieces of the entire project. 

One of the larger concerns with data migrations is the high risk of data integrity and quality issues. This is a major concern since issues could have downstream effects not easily visible during testing, potentially leading to significant customer impact. Because of these factors, businesses need to find a simple, reliable, and cost-effective data migration solution which minimizes many of these concerns.

An excellent choice is using Arcion’s data migration capabilities as the hub of your data migration solution. With respect to a common digital transformation use case, Arcion allows organizations to move to the cloud by enabling them to transfer data from on-prem systems to modern cloud architectures. Arcion also powers use cases such as migrating data between applications and systems, and many other combinations. All configuration and operations can be performed using a simple intuitive interface or done through an extensive suite of APIs. Because of the availability of the UI, the amount of code required to build a data migration pipeline is none, or extremely minimal for highly-customized migrations. This saves organizations time and money while delivering robust data migration capabilities that are secure and reliable.

As mentioned earlier, the reliability of the data after the migration is complete remains a key factor in the success of the data migration project. Even with poorly structured data, Arcion guarantees data reliability. It also supports the automatic mapping of data between different database types such as moving data from SQL to NoSQL, or vice versa. 

Arcion Cloud is IaaS (Infrastructure as a Service). For processing and temporary storage of data, it provides you with its cloud services with large computing power. Arcion Cloud also doubles as a PaaS (Platform as a Service) solution, since Arcion Cloud enables connectivity to other platforms for storing and processing data that are hosted in its fully-managed cloud environment.

Some of the main benefits of using Arcion’s data migration solution are: 

  • You can get it set up in a few minutes with rapid deployment of streaming pipelines.
  • Arcion securely and accurately moves data of any size and structure from source to target location, ensuring end-to-end data integrity.
  • Achieve rapid digital transformation: With Arcion, you can easily migrate data from legacy databases to modern data platforms that are on-premise or in the cloud. It allows you to map database schemas between SQL and NoSQL providers automatically, removing traditional migration restrictions and giving you the freedom to choose the platforms that suit you and your digital roadmap.
  • Highly parallel architecture: With Arcion, you can load data in parallel at high speeds, allowing you to move workloads of varying sizes and complexity faster.
  • Automatic schema conversion: Arcion automatically convert and map data types from a source to the corresponding target data types based on source system schemas. You can perform these steps regardless of the number of tables, indexes, views, or data types in the source system.
  • Arcion provides zero downtime data migration, supports bi-directional replication out of the box for users to fallback at any time, and no data loss in case of failure or crash.

How do you manage data migration?

The main challenge for executing a successful data migration is the “gravity” of the data. Factors for this include an increase in the volume of data, like adding smaller data sets into a larger data set, as well as the amount of its use. In addition, in the data migration process, there is always a risk of data corruption, long downtimes, application instability, and data loss.

The data migration process also heavily depends on the correct operation and configuration of the target system. If it does not operate correctly, or is configured incorrectly, data may be corrupted or lost. This really underlines the importance of making sure you have a good understanding of how to validate the data once it arrives in the target destination before using the data or taking the source system offline.

In the process of organizing data migration, a number of rather serious challenges can arise.

  • Data formatting: The format of data from older systems may differ from that of modern systems. For example, it may omit information such as a time marker. In addition, new systems can generate unstructured or real-time data that is absent in earlier systems. Therefore, there is a need to adapt the data migration tools.
  • Scalability: The volume of data and the speed of their transfer is constantly growing, which requires choosing a data migration solution that can efficiently and quickly process a large amount of data.
  • Interaction with external and third party data providers: Data obtained from external sources may have a different detail level, which makes it difficult to process them.
  • Collection of detailed information to organize an effective data migration process: There are a lot of factors to consider when implementing data migration. Among them are the types of data that need to be collected and analyzed, the data sources, the systems that will use the data, the ways in which the data will be used, and the frequency with which the data will be updated.
  • Support and continuous updating of the data migration system to meet business requirements: It’s important to keep both legacy systems and modern distributed databases in sync. The use of Change Data Capture technology can help pick up transactions from the source, transform the data to the target system, and apply the transaction on the target. To keep both in sync, it needs to perform continuous data replication from the legacy system to the new database.

How to choose the right data migration solution

Choose a right data migration solution can save you countless time and resource. Before we dive into the criteria in selecting the right tool, below are the important steps a data migration plan should include:

  • Determining the structure and scope of the project: Before planning the migration process, it is very important to conduct a preliminary assessment of the project. You need to determine the critical areas of the project structure, the format of the data to be used, and the scope of the project.
  • Evaluation of available migration tools: When choosing a tool that you will use for data migration, it is important to consider what features it provides, how flexible and scalable it is, how it ensures data security, and whether the people who work on the project know how to use this data migration tool.
  • Migration planning: At this stage, you need to plan the process of extracting, transforming, and validating the data. You should find the answers to the following questions:
  • ~How is data retrieved, stored, and validated?
  • ~What are the data matching rules?
  • ~How is the data loaded into the new system?
  • ~What are the recovery plans for each migration step?
  • ~What is the schedule of steps required to run the migration?
  • Project testing: This stage includes the selection of tools for testing, drawing up a test plan for all stages of migration, and determining the form in which information about the test results will be submitted.
  • Execution: Running the migration process and checking how it works on your data.
  • Updating the migration system due to changes in business requirements, new data formats, etc.

When choosing a data migration solution that is right for your project, it is important to take into account the following angles:

  • Functionality: Determine how complex systems are mapped and how the data migration tool ensures data quality. You’ll also want to make sure the tool allows you to plan and control workflows, and whether automated scripting tools are available for deeper customization.
  • Sources and destinations of data: To find out whether the migration system is suitable for your migration project or not, you will want to confirm if your tech stack is supported by the solution. You’ll need to confirm the types of systems it is designed for, what data formats it supports, and what types of data storage it allows you to work with.
  • Performance and scalability: This criterion determines what volume of data the platform can handle, how many processes it can run simultaneously, and how fast data moves between systems. This also entails confirming that the system is performant for moving legacy systems and data to the cloud, if required.
  • Ease of use: This indicator allows you to understand what kind of knowledge the employees who will use the migration system should have, and how highly qualified they should be. Depending on the skillset of the implementing team, this could also be a large factor in budget and spend for the migration project.
  • Safety: Learn how it protects your data from system failures, possible downtime, and data corruption.
  • Support: If you are new to the platform or system, you’ll want to ensure that documentation is easy to follow and extensive. You’ll also want to make sure access to the support team is easy and widely accessible if you need help with implementation or have a major issue.
  • Prices: For both small and large businesses, an important criterion for choosing a data migration solution is its price. The price should fit within your budget now and also when you scale. Make sure to look at future needs and the cost associated as well.

Which tool is best for data migration?

There are different types of tasks that the migration solution can perform. It can be a simple database upgrade, database migration, application migration, and transfer from the entire local system to the cloud. To solve these different problems, there are several different types of data migration systems.

Let’s take a closer look at what types of data migration systems are available, when you should use them, and what are the advantages and disadvantages of using them. 

Self-scripted tools 

Engineers can complete a data migration project themselves, without the use of any external tools. In the past, this was how most data was migrated. Data was extracted through a batch job into an intermediary location and then loaded, usually record-by-record, into the target location. The process of ETL, Extract, Transform, and Load, was still required but done so in a synchronous fashion through individual jobs. These jobs were generally scripts which ran to migrate data single time or on a schedule to migrate data to another location.

Self-scripted tools can be useful when you are moving a relatively small amount of data and dealing with simple requirements. Sometimes, a self-scripted approach is the only method available if the platforms for your source or target platforms are not supported by any migration tool. These tools can be developed quickly and are relatively inexpensive if used for smaller data sets without complex requirements for mapping or transformation. The main advantage of these tools is the flexibility to use them with any source and target platforms.

The self-scripted approach has several important drawbacks, though. These drawbacks include:

  • The possibility for extensive engineering hours to build out the tool or script 
  • Additional time to test the output of the tool and ensure mapping and transformations are accurate
  • Possible scalability problems since, as data sets grow, processing times may become exponentially longer

The cost of developing your self-scripted tools can be much more than the cost of using ready-made ones. These tools should be reserved as a last resort if no other platform supports the exact use case you require for your business. If they are used, the code and process for the migrations should be well documented for troubleshooting or future additions to the process. Otherwise, supporting, maintaining, and making changes to the process may be very difficult and risky.

On-premise tools

These tools are designed for data migration but are hosted on an organization's own infrastructure. This could be on bare metal servers, VMs, or a private cloud which is hosted on-premise. A large reason for using an on-premise installation of a tool is due to security or compliance standards which may not be supported by a cloud product. Traditionally, before the cloud became a mainstream, all platforms and tools were hosted on-premise.

On-premise tools keep your data secure and usually allow for a high level of customizations at all levels. Customization at the infrastructure layer usually entails tasks such as hardening the server where the platform or tools is hosted to adhere to compliance rules. At the application layer further steps may also be taken, such as customizing the application-level encryption. In addition, they can provide low latency and be tuned at multiple levels to match the needs of the organization

There are a few note-worthy disadvantages to be aware of while using on-premise tools. These include:

  • More costly, in time and budget, to run a proof of concept
  • PoCs require infrastructure and ample resources to spin up the servers and install/configure the tool even just to validate feasibility. This can take a significant budget compared to potentially running a trial on a cloud platform in a matter of minutes.
  • Limited scalability compared to the cloud solutions.
  • Increasing memory or storage on an on-prem solution is generally more manual compared to a cloud instance that can auto-scale up and down.
  • Platform and infrastructure maintenance is handled internally.
  • An internal team will be needed to monitor and maintain the tool and the infrastructure that the tool is deployed on.

Cloud-based tools

Cloud-based tools allow you to transfer data from on-premise infrastructure to the cloud or from one cloud product to another. Some products also allow for moving data between platforms that are both hosted on-premise, or from the cloud back to an on-premise installation. The capabilities of these cloud-based tools allow for many different scenarios, even ones that are not conventional.

These tools are very flexible, allow you to process different data types, and generally have support for a wide number of platforms. Cloud-based solutions also scale more easily than on-premise solutions. This makes the size of your application and the amount of data you are migrating less relevant from a system load and performance standpoint. Auto-scaling allows cloud-based platforms to cope well with sudden spikes in demand, generally caused by intermittent or temporary events. The platform can scale up or down as certain demands are put onto the system. This is generally difficult or impossible to do with a bare metal, on-premise implementation.

For data migrations, cloud-based solutions are well-suited for business analysts and data scientists who need access to shared tools and data warehouses with minimal code, deployment, and configuration steps. Many tools can be deployed and configured without deep technical knowledge or reliance on other teams. For instance, an on-premise deployment of such tools would require many engineers to create the infrastructure needed to host the tool, install the tool itself, and to monitor the setup. With a cloud-based platform, getting started could be as simple as signing up and clicking a few buttons.

Another flexible aspect of most cloud-based systems is that they offer different pricing plans depending on the number of resources used. This allows for fine-grained cost management since businesses can choose the best option for their business and waste money on more resources than they require to complete the job or project.

The main reservation for using cloud-based tools for data migrations is data security while data is in-flight. When selecting a cloud-based tool for data migration, it is important to understand how data is protected, what part of the security is configurable versus automatic or default, and if any additional steps are needed to ensure the security of your data.


In this article, we talked about what data migration is and the main problems that you will likely encounter in your data migration process. We also looked at the steps involved when migrating data and what criteria should be considered when choosing a migration tool. Additionally, we listed the types of tools that can help with data migration, their advantages and disadvantages, and specifics on how each of them works. 

If you’re looking for an easy way to migrate data, sign up for a free trial with Arcion Cloud or book a personalized demo with our database experts to see Arcion Self-hosted in action.

Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
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