Data Migration

What is Data MigratioWhat is Data Migration, and Why is it Needed

Modern businesses mostly rely on big data, which helps them improve their overall performance. This means that data migration and integration processes must be efficient and seamless, regardless of whether the data is moving from a source to a data lake, from a data warehouse to a data mart, or any other destination system. But, what is the data migration process?

Data migration is transferring data across different data formats, databases, and storage systems. Generally, it involves more than simply moving data from one system or database to another. Although the data migration process might sound easy, it entails many complex tasks such as data mapping, reformatting, etc. It includes numerous pre- and post-migration steps, such as planning, creating backups, testing for quality, validating the outcomes, and much more. The migration process concludes only when the old environment, database, or system stops working. Data migration is crucial when moving from on-premises IT infrastructure to a cloud computing environment.

People often use the term ‘data migration’ interchangeably with other data transfer methods, such as data integration, but these are entirely different. Data integration entails combining two separate data repositories and creating a single, more extensive repository.

There are various reasons for which an organization might need to start a data migration project. It may be consolidating or downsizing a data center or upgrading servers or storage devices, for instance. Here are a few reasons that outline the need for data migration processes in an organization -

It allows businesses to maintain data integrity and quality.

It gives businesses greater flexibility to quickly scale up or down and boosts overall productivity.

The migration process allows businesses to restructure quickly by integrating with other platforms and makes data easily accessible.

It cuts down data storage expenses by improving the business ROI.

Migrating data also means efficient system upgrades, allowing employees in an organization to save time and focus on other critical tasks.

Data Migration Process | What are the Steps Involved in Data Migration?

Data migrations can be beneficial for an organization. Upgrading from a traditional system to a more advanced one can boost productivity. A proper data migration plan allows the teams to streamline and simplify the data migration process. There isn't a one-size-fits-all data migration strategy that works for everyone. Every organization has various needs, and every data set is unique. Therefore, it is crucial to take the time necessary to assess the data migration goals of the organization and build a data migration strategy depending on its business requirements.




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Below are the seven data migration steps one can follow as a checklist while creating a comprehensive data migration strategy.

1.Create a Data Migration Plan

Assessing your current data status should be the first step in a data migration strategy. You should identify every bit of data that needs to migrate, where it is stored, what format it is in right now, and what format is preferable after the migration. Investing enough time in the planning phase is essential to ensure everything works according to the plan. Making adjustments after your data migration is underway can be challenging. Furthermore, you must choose either of the two data migration approaches for your big data project-

i. Big Bang Data Migration- This data migration method transfers all data from the source to the destination environment, and it is faster, simpler, and less expensive.

ii. Trickle Data Migration- Trickle data migration divides the migration process into smaller stages where data goes in discrete units. The migration continues in parallel while the old system continues to function. The live system never goes down, which makes it less prone to mistakes and unplanned breakdowns.

2. Evaluate your Data and Other Resources

The evaluation stage entails checking the data quality, anomalies, and any conflicts and duplications of the migrating data set. Determine whether your team has sufficient resources, such as migration tools, to finish your migration project before the deadline. You must also take into account a few things about the data migration tool in addition to evaluating its characteristics, such as how flexible your preferred migration tool is.

3. Backup your Data Before Migration

The data backup stage entails backing up all data that will migrate to prevent migration failures that could result in data loss or inaccurate data to maintain data integrity. Additionally, having several backup options is usually a brilliant idea. Using the cloud is one of the best ways to handle data backups. If you have an off-site cloud backup, your source data will be safe even if the location of the servers becomes corrupt for any reason.

4. Design the Migration Process

The migration process stage specifies the migration testing protocols, acceptance standards, and additional staff duties. This stage involves hiring ETL developers or data migration specialists to oversee the procedure. It is also essential to recognize and hire any additional expertise required for the migration process, such as business and system analysts. The data extraction, storage, and verification processes, mapping rules, data loading procedures, recovery measures for each stage of the migration, and a timeline of the steps necessary for going live should all be present in the migration design.

5. Execute the Data Migration Plan

The migration process goes into action in this step. At this point, the extraction, transformation, and loading (ETL) activities go live. Data mapping is a crucial part of this step. The amount of data involved and the method of data migration adopted will determine how long the procedure takes. You must ensure that the correct system permissions are applied to enable successful data migration and extract all the data from the source system to the destination system. Ensure the data is clean to safeguard the target system before converting it to a suitable format for migration.

6. Test and Validate the Migration Design

When you use a trickle approach, you should test each section of migrated data to identify errors as soon as possible. As data elements enter the target system, frequent testing ensures security, quality, and compliance with requirements. You must ensure there are no connectivity issues with the source and target systems after the migration is complete.

7. Audit the Final Result of the Migration

Even with testing, there's still a chance that an error might occur during the migration. After the data migration process, ensure everything is accurate by thoroughly auditing the system and data quality. Use a backup to restore files with errors, missing, incomplete, or corrupt data. This step allows the relevant team members and other stakeholders to discuss what went well and what could be improved.

Enhance your data analytics knowledge with end-to-end solved big data analytics mini projects for final year students.

Types of Data Migration

Businesses can use the appropriate data migration tools and optimize their migration processes when they know the various types of data migration. Here are some of the most popular types of data migration, along with an explanation of what each entails.

1. Storage Migration

When an organization switches to a new system/software and abandons its old software, it is known as storage migration. Storage migration allows businesses to migrate data from one storage system to another. An organization might, for instance, upload data to the cloud or move data from a hard drive to an SSD. Businesses use this method due to technological advancement, not a lack of storage space. Larger companies require more time for transferring data using storage migration. Storage migration also involves data validation, duplication, cleaning, etc.

2. Application Migration

When investing in one, an organization must transfer all data into a new software system. This kind of data migration happens more often since companies must update their software frequently. Problems occur when the latest and previous data systems use different data formats and models. If so, an application migration procedure needs to be managed by an experienced data specialist.

3. Database Migration

A database management system directs the data storage and organization in databases. Upgrading an existing database or switching to a new vendor after using an outdated database can be two different types of database migration. The second case is more challenging than the former, mainly if the source and target databases support other data structures. When a company employs data migration software to migrate from a hierarchy, flat file, or network database, the database migration is significantly more challenging. Although these source systems are outdated, most businesses still use them since it would be expensive to redesign and transfer data.

4. Data Center Migration

Businesses keep their essential software and data in a data center. A data center is the data storage space that hosts tools and other IT-related technology. When a company moves all of its digital assets or current systems to different areas of the operating facility, it may engage in data center migration. When moving equipment, a company needs to take extra care because it is expensive to restore.

5. Cloud Data Migration

Any data transfer from one location to the cloud is called "cloud migration" in the software industry. Most businesses transfer data to the cloud since cloud migration provides so much storage space at a low cost. The amount and source of potential cloud data determine how long it takes to migrate data to the cloud environment. While larger projects can take almost a year for cloud migration, smaller amounts of data can migrate in less than an hour.

6. Business Process Migration

Business process migration involves transferring business applications, data on business processes, and KPIs to a new environment. If there is a merger between two businesses, either one or both must migrate data from legacy systems into the target location. Such business mergers are necessary for business optimization, i.e., to expand into new markets and stay competitive. Data transfer due to a competitive risk or changing customer demands are examples of other business process migrations.

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Data Migration Examples

This section will walk you through a few data migration scenarios to help you better understand how and where it applies.

Application Data Migration to the Cloud

This instance of a legacy application migration using Microsoft SQL Server leverages the AlwaysOn availability groups (AG) feature and is hosted between two hosts with locally attached disks. These availability groups help control data loss during the migration process and impact data backup policies. Local object repositories store the weekly backup, whereas the file system stores the daily backups. These backups take place in the secondary server without affecting the primary servers.

Migrate PostgreSQL databases to Azure

Businesses often use PostgreSQL for several of their big data activities. They can migrate the database to a PostgreSQL instance of Azure Database using the Azure Database Migration Service. Additionally, they can update all applications and processes to employ the new Azure Database for PostgreSQL instance. Such a migration scenario involves building a new data processing pipeline using Azure Data Factory that connects to the Azure Database for PostgreSQL instance.

Migrate MariaDB databases to Azure

Some businesses choose to employ MariaDB instead of MySQL due to it’s extensive range of storage engines, fast cache and indexes, open-source support with features and extensions, etc. Despite all these features and benefits, businesses often face certain challenges while using MariaDB and decide to migrate their data to a reliable platform such as Azure. In such scenarios, they assess the environments for compatibility with the migration process and move databases to the Azure Database for MariaDB instance using standard open-source tools. 

When Can Data Migration be Beneficial?

Data migration generally occurs when improving hardware infrastructure, switching to a new system, upgrading applications, altering business processes, increasing data volumes, or improving performance. Here are some instances where data migration may be beneficial.

Performance-Driven Modernization

When an existing system has trouble meeting performance demands, for instance, performance issues, duplicate data issues, and compatibility issues might be caused by two outdated systems.

Migrating a Data Warehouse between Databases

Such data migrations might be more complex than they initially seem since the migration typically does not result in exact data replication. The migration usually entails data cleaning operations or more complex changes if the two data sources function differently.

Cloud-based data migration

It might be challenging since it typically entails migrating several applications. This type of migration often requires customization regardless of the migration options offered by cloud providers.

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Data Migration Tools

There are many different tools available nowadays to help with enterprise data migrations. These consist of licensed and open source data migration tools and vendor-specific solutions provided by cloud service providers to assist clients' migration into public or private cloud environments. The ideal data migration tools for a migration project depend on the data migration approach. In addition to using cloud-based or on-premises tools, organizations can also develop data migration scripts. Self-Scripted Data Migration is an internal solution; however, it is not extensible for large-scale data migration projects and may be appropriate only for small migration projects. On-Premises Data Migration tools are the best option if all the data is in one place. Organizations migrating their data from various data sources/platforms to a cloud-based destination may find cloud-based data migration tools useful.

Here is a thorough breakdown of some of the top data migration tools available in the market.

AWS Data Pipeline

AWS Data Pipeline is a popular web service that enables reliable data processing and migration between various AWS computing and storage services and on-premises data sources. You can simply build fault-tolerant, scalable, and highly available complex data processing workflows with the aid of AWS Data Pipeline. AWS data pipeline has many benefits, such as assuring resource availability, handling inter-task dependencies, developing a failure notification system, etc. Additionally, using AWS Data Pipeline, you can transfer and process data previously kept in on-premises data silos.

You may efficiently transfer the outcomes to AWS services like Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR by using AWS Data Pipeline to frequently access data wherever it goes, transform and process it at scale.

IBM Informix

An efficient and extensible database server, IBM Informix handles traditional relational, object-relational, and dimensional databases. IBM Informix uses a hybrid cloud infrastructure that lowers the expense of hardware and software management for businesses and promotes data migration. Fast data transfer from IBM Informix enables real-time data analytics on transactional data loads. The IBM Informix database server supports more than one operating system, such as UNIX, Linux, Mac OS X, and Windows. Besides the database server, all editions of Informix include the additional client tools- IBM Informix Client Software Development Kit (Client SDK), IBM OpenAdmin Tool (OAT) for Informix, and the Informix DataBlade Developers Kit (DBDK).

Fivetran

Fivetran is a highly extensive ELT tool that enables automated data integration and efficient extraction of business operations and customer data from associated servers, websites, and applications. The resultant data subsequently moves to other tools for analytics, advertising, and data warehousing. You can quickly start working with Fivetran if you have an existing account.

Once you connect a data warehouse, it displays a dashboard where you can add new connectors.

The setup page with precise notes on the screen and configuration verification will appear after adding a new connector.

When you set up your first connector, it will start synchronizing instantly, allowing you to immediately utilize the data in your data warehouse. You can add as many connectors as necessary to finish your first configuration.

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Data Migration Services

This section will cover the two most popular data migration services- AWS Data Migration Service and Azure Data Migration Service.

1. Azure Data Migration Service

Azure Data Migration Service enables seamless migrations from various database sources to Azure Data platforms with the least downtime possible. The service creates assessment reports using the Data Migration Assistant and offers suggestions to lead you through the changes necessary before any data transfer. Azure Database Migration Service takes care of all the essential tasks once you're ready to start moving data.

You can move SQL Server databases to either Azure SQL Managed Instance (Platform-as-a-Service) or SQL Server on Azure Virtual Machines by using the Azure SQL Migration extension in Azure Data Studio (Infrastructure-as-a-Service). With the help of the wizard provided by the Azure SQL Migration extension for Azure Data Studio, you can determine whether your SQL Server databases are ready for moving data, receive suggestions for the best Azure plan size, etc. The Azure Database Migration service coordinates data relocation tasks and offers task monitoring.

2. AWS Data Migration Service

You can move databases to AWS rapidly and securely using the AWS Database Migration Service (AWS DMS). In this migration service, the application downtime that depends on the source database is minimal as it stays entirely functional across the migration. You can migrate your data between the most common commercial and open-source databases with the help of the AWS Database Migration Service.

In addition to heterogeneous migrations from various database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora, the AWS Database Migration Service also allows homogenous migrations, such as Oracle to Oracle. You can actively duplicate data from any eligible source to an acceptable target with minimum latency using AWS Database Migration Service. For instance, you can develop a highly available and scalable data lake solution by replicating data from several sources to Amazon Simple Storage Service (Amazon S3). You can combine databases into giant data warehouses by streaming data to Amazon Redshift.

Best Practices for Data Migration

Data Migration Challenges

Although modern IT systems have advantages over the risk associated with data migration, especially in the long run, it can be difficult and risky. You can run into some challenges and roadblocks typical to most migration projects, even with a solid data migration plan. Here are a few risks to be aware of.

Lack of Automation- Data migrations include migrating several elements, and the absence of automation and additional software solutions might cause delays or errors.

Data Backup- Data loss during migration is possible; therefore, it is essential to back up data and carefully plan the migration with help and support from professionals.

Data Security- Encrypt all sensitive data before migration to improve stability and security. 

Expensive- Poor planning frequently leads to hidden costs. Online transfers, for instance, will cause additional expenses if they take longer than planned.

Data Governance- It is essential to understand who has the authority to review, change, add, or remove data from the source system.

When conducting a data migration process, here are a few best practices you must follow to ensure that the process is smooth, successful, and doesn't incur expensive delays.

1. Build a Migration Plan and Adhere to it.

You need to build a detailed strategy that specifies what data needs to migrate, where it needs to go, how it will get there, etc. Your plan must also include the criteria for who should access the data. Organizations must also form a dedicated migration team with suitable professionals to oversee and manage the project.

2. Establish Migration Policies.

 It is not enough to just have a plan in mind; it is also necessary to create policies for data migration throughout the entire business and implement enforcement measures. Your policies should make sure that data is being moved to the appropriate location and is given protection after migration.

3. Gain a Clear Understanding of the Data.

Analyze the data you plan to migrate before writing mapping scripts. Focus more on removing data that is no longer necessary or outdated. This will ensure that a clean dataset is available for your team to work with after the migration and make your migration easier overall.




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Data Migration Project Ideas to Practice in 2022

We believe you have a clear picture of data migration and everything you need to know about it. Check out these easy yet exciting data migration project ideas to help you put everything you have read in this blog into practice.

Enterprise Sales Data Migration Idea

Consider a merger between companies operating in the same industry and using the same platform to manage their sales and marketing data. The main challenge here is migrating a large amount of unorganized and error-prone data to the common platform. Other challenges with this business scenario include duplicate data in the system because both organizations may have many of the same clients and the need to identify proper ownership after the Lead, Accounts, and Opportunities migrate. This project idea entails migrating all of the data for Leads, Accounts, Contacts, and other entities from one organization’s database to another and combining it into one organization’s database. For this project, you can use any e-commerce sales dataset (e.g., Olist store sales dataset and BigMart sales dataset) from Kaggle.

Finance Data Migration Project Idea

A financial service ((loan) provider buys a leasing company and incorporates its IT system. The resulting company is now in need of migrating its computer services to a new cloud provider—especially one with a solid track record for compliance with regulations and data security. Use Microsoft Azure for this project, a cloud service that would significantly minimize recurring infrastructure costs. You can migrate its data onto Azure following the guidelines provided by Microsoft's Cloud Adoption Framework. You can use the Loan dataset available on Kaggle for this project idea.

Healthcare Admin Data Migration Project Idea

Consider a scenario where a software provider provides hospitals and medical clinics with management software that handles data management, billing, patient records, telemedicine, and other tasks. For this project idea, use the AWS data migration tool since it offers limitless scalability, highly available data redundancy, enhanced data compression and elimination of unwanted data, and automated tiering. You can migrate the entire healthcare administration database to Cloud Volumes ONTAP for AWS using the simple drag and drop interface of Cloud Manager. Use the healthcare analytics dataset from Kaggle for this project idea. 

Getting Started with Data Migration

By now, you know that data migration plays a crucial role in the growth of an organization. Not just organizations, every individual planning to enter the field of data analytics, data science, or big data engineering must possess efficient data migration knowledge and skills to excel in their career. Although this blog covers the theoretical aspects of data migration, you must focus on gaining practical experience by working on real-world data migration projects. Try looking for some data engineering projects on Github, or you can even check out the ProjectPro repository. It offers over 250 solved end-to-end industry-level projects on data science and big data, with free guided project videos, reusable solution code, downloadable datasets, and one-to-one industry expert guidance.

Access Data Science and Machine Learning Project Code Examples

FAQs on Data Migration
1. What is data migration example?

A data migration example scenario is when two businesses merge, or one of them acquires the other. IT frameworks and data typically need to be integrated to establish a single unified system for the new company.

2. What are the steps in data migration?

The steps in data migration are as follows.

Planning- This is the most crucial phase of your data migration process, and it lays out the performance targets, gives a defined path, and sets your expectations for the entire project period.

Preparing the data- After determining the project's scope, forming a data migration team, and laying the groundwork, it's time to prepare your data for migration.

Designing migration process- This stage of the migration process specifies the testing protocols, acceptance criteria, and other employee duties.

Executing the process- This step involves the ETL operations- extracting, transforming, and loading the data.

Testing- As data elements enter the target infrastructure, regular testing ensures their security, high quality, and compliance with standards.

Auditing- Conduct a thorough audit of the system and data quality after the data migration process to ensure that everything is accurate.

3. What is data migration in ETL?

Data migration in ETL refers to extracting data from one system, converting and aggregating it if necessary, and then loading it onto the target system.

4. Why is data migration needed?

Data migration is needed as it enables you to consolidate data from one source into a single storage system, such as a cloud data warehouse, data lake, or lakehouse. The migration process leads to faster insights as data analysts, and other staff members can quickly access all the data they require from a single system.

5. What is Data Migration in SQL?

Data migration in SQL is moving data to or from the SQL Server. The migration process can be very complex, especially when moving a significant amount of enterprise data.

6.How do you make a data migration plan?

You can make a data migration plan by considering the following points-

Understand the data with the help of thorough data analysis.

Clean up and prepare the data using various additional tools and expert assistance.

Create data backup as it helps in avoiding any data loss and maintaining the data quality.

Keep track of the data quality to ensure data integrity.

7. What is a data migration strategy?

A database migration strategy is a detailed plan that simplifies the movement of your data from one platform to another. Such an approach encompasses several variables, including data auditing, cleaning, maintenance, security, and regulation. A well-defined strategy can minimize the effects of database migration on businesses.D?n, and Why is it Needed

Modern businesses mostly rely on big data, which helps them improve their overall performance. This means that data migration and integration processes must be efficient and seamless, regardless of whether the data is moving from a source to a data lake, from a data warehouse to a data mart, or any other destination system. But, what is the data migration process?

Data migration is transferring data across different data formats, databases, and storage systems. Generally, it involves more than simply moving data from one system or database to another. Although the data migration process might sound easy, it entails many complex tasks such as data mapping, reformatting, etc. It includes numerous pre- and post-migration steps, such as planning, creating backups, testing for quality, validating the outcomes, and much more. The migration process concludes only when the old environment, database, or system stops working. Data migration is crucial when moving from on-premises IT infrastructure to a cloud computing environment.

People often use the term ‘data migration’ interchangeably with other data transfer methods, such as data integration, but these are entirely different. Data integration entails combining two separate data repositories and creating a single, more extensive repository.

There are various reasons for which an organization might need to start a data migration project. It may be consolidating or downsizing a data center or upgrading servers or storage devices, for instance. Here are a few reasons that outline the need for data migration processes in an organization -

It allows businesses to maintain data integrity and quality.

It gives businesses greater flexibility to quickly scale up or down and boosts overall productivity.

The migration process allows businesses to restructure quickly by integrating with other platforms and makes data easily accessible.

It cuts down data storage expenses by improving the business ROI.

Migrating data also means efficient system upgrades, allowing employees in an organization to save time and focus on other critical tasks.

Data Migration Process | What are the Steps Involved in Data Migration?

Data migrations can be beneficial for an organization. Upgrading from a traditional system to a more advanced one can boost productivity. A proper data migration plan allows the teams to streamline and simplify the data migration process. There isn't a one-size-fits-all data migration strategy that works for everyone. Every organization has various needs, and every data set is unique. Therefore, it is crucial to take the time necessary to assess the data migration goals of the organization and build a data migration strategy depending on its business requirements.




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Below are the seven data migration steps one can follow as a checklist while creating a comprehensive data migration strategy.

1.Create a Data Migration Plan

Assessing your current data status should be the first step in a data migration strategy. You should identify every bit of data that needs to migrate, where it is stored, what format it is in right now, and what format is preferable after the migration. Investing enough time in the planning phase is essential to ensure everything works according to the plan. Making adjustments after your data migration is underway can be challenging. Furthermore, you must choose either of the two data migration approaches for your big data project-

i. Big Bang Data Migration- This data migration method transfers all data from the source to the destination environment, and it is faster, simpler, and less expensive.

ii. Trickle Data Migration- Trickle data migration divides the migration process into smaller stages where data goes in discrete units. The migration continues in parallel while the old system continues to function. The live system never goes down, which makes it less prone to mistakes and unplanned breakdowns.

2. Evaluate your Data and Other Resources

The evaluation stage entails checking the data quality, anomalies, and any conflicts and duplications of the migrating data set. Determine whether your team has sufficient resources, such as migration tools, to finish your migration project before the deadline. You must also take into account a few things about the data migration tool in addition to evaluating its characteristics, such as how flexible your preferred migration tool is.

3. Backup your Data Before Migration

The data backup stage entails backing up all data that will migrate to prevent migration failures that could result in data loss or inaccurate data to maintain data integrity. Additionally, having several backup options is usually a brilliant idea. Using the cloud is one of the best ways to handle data backups. If you have an off-site cloud backup, your source data will be safe even if the location of the servers becomes corrupt for any reason.

4. Design the Migration Process

The migration process stage specifies the migration testing protocols, acceptance standards, and additional staff duties. This stage involves hiring ETL developers or data migration specialists to oversee the procedure. It is also essential to recognize and hire any additional expertise required for the migration process, such as business and system analysts. The data extraction, storage, and verification processes, mapping rules, data loading procedures, recovery measures for each stage of the migration, and a timeline of the steps necessary for going live should all be present in the migration design.

5. Execute the Data Migration Plan

The migration process goes into action in this step. At this point, the extraction, transformation, and loading (ETL) activities go live. Data mapping is a crucial part of this step. The amount of data involved and the method of data migration adopted will determine how long the procedure takes. You must ensure that the correct system permissions are applied to enable successful data migration and extract all the data from the source system to the destination system. Ensure the data is clean to safeguard the target system before converting it to a suitable format for migration.

6. Test and Validate the Migration Design

When you use a trickle approach, you should test each section of migrated data to identify errors as soon as possible. As data elements enter the target system, frequent testing ensures security, quality, and compliance with requirements. You must ensure there are no connectivity issues with the source and target systems after the migration is complete.

7. Audit the Final Result of the Migration

Even with testing, there's still a chance that an error might occur during the migration. After the data migration process, ensure everything is accurate by thoroughly auditing the system and data quality. Use a backup to restore files with errors, missing, incomplete, or corrupt data. This step allows the relevant team members and other stakeholders to discuss what went well and what could be improved.

Enhance your data analytics knowledge with end-to-end solved big data analytics mini projects for final year students.

Types of Data Migration

Businesses can use the appropriate data migration tools and optimize their migration processes when they know the various types of data migration. Here are some of the most popular types of data migration, along with an explanation of what each entails.

1. Storage Migration

When an organization switches to a new system/software and abandons its old software, it is known as storage migration. Storage migration allows businesses to migrate data from one storage system to another. An organization might, for instance, upload data to the cloud or move data from a hard drive to an SSD. Businesses use this method due to technological advancement, not a lack of storage space. Larger companies require more time for transferring data using storage migration. Storage migration also involves data validation, duplication, cleaning, etc.

2. Application Migration

When investing in one, an organization must transfer all data into a new software system. This kind of data migration happens more often since companies must update their software frequently. Problems occur when the latest and previous data systems use different data formats and models. If so, an application migration procedure needs to be managed by an experienced data specialist.

3. Database Migration

A database management system directs the data storage and organization in databases. Upgrading an existing database or switching to a new vendor after using an outdated database can be two different types of database migration. The second case is more challenging than the former, mainly if the source and target databases support other data structures. When a company employs data migration software to migrate from a hierarchy, flat file, or network database, the database migration is significantly more challenging. Although these source systems are outdated, most businesses still use them since it would be expensive to redesign and transfer data.

4. Data Center Migration

Businesses keep their essential software and data in a data center. A data center is the data storage space that hosts tools and other IT-related technology. When a company moves all of its digital assets or current systems to different areas of the operating facility, it may engage in data center migration. When moving equipment, a company needs to take extra care because it is expensive to restore.

5. Cloud Data Migration

Any data transfer from one location to the cloud is called "cloud migration" in the software industry. Most businesses transfer data to the cloud since cloud migration provides so much storage space at a low cost. The amount and source of potential cloud data determine how long it takes to migrate data to the cloud environment. While larger projects can take almost a year for cloud migration, smaller amounts of data can migrate in less than an hour.

6. Business Process Migration

Business process migration involves transferring business applications, data on business processes, and KPIs to a new environment. If there is a merger between two businesses, either one or both must migrate data from legacy systems into the target location. Such business mergers are necessary for business optimization, i.e., to expand into new markets and stay competitive. Data transfer due to a competitive risk or changing customer demands are examples of other business process migrations.

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Data Migration Examples

This section will walk you through a few data migration scenarios to help you better understand how and where it applies.

Application Data Migration to the Cloud

This instance of a legacy application migration using Microsoft SQL Server leverages the AlwaysOn availability groups (AG) feature and is hosted between two hosts with locally attached disks. These availability groups help control data loss during the migration process and impact data backup policies. Local object repositories store the weekly backup, whereas the file system stores the daily backups. These backups take place in the secondary server without affecting the primary servers.

Migrate PostgreSQL databases to Azure

Businesses often use PostgreSQL for several of their big data activities. They can migrate the database to a PostgreSQL instance of Azure Database using the Azure Database Migration Service. Additionally, they can update all applications and processes to employ the new Azure Database for PostgreSQL instance. Such a migration scenario involves building a new data processing pipeline using Azure Data Factory that connects to the Azure Database for PostgreSQL instance.

Migrate MariaDB databases to Azure

Some businesses choose to employ MariaDB instead of MySQL due to it’s extensive range of storage engines, fast cache and indexes, open-source support with features and extensions, etc. Despite all these features and benefits, businesses often face certain challenges while using MariaDB and decide to migrate their data to a reliable platform such as Azure. In such scenarios, they assess the environments for compatibility with the migration process and move databases to the Azure Database for MariaDB instance using standard open-source tools. 

When Can Data Migration be Beneficial?

Data migration generally occurs when improving hardware infrastructure, switching to a new system, upgrading applications, altering business processes, increasing data volumes, or improving performance. Here are some instances where data migration may be beneficial.

Performance-Driven Modernization

When an existing system has trouble meeting performance demands, for instance, performance issues, duplicate data issues, and compatibility issues might be caused by two outdated systems.

Migrating a Data Warehouse between Databases

Such data migrations might be more complex than they initially seem since the migration typically does not result in exact data replication. The migration usually entails data cleaning operations or more complex changes if the two data sources function differently.

Cloud-based data migration

It might be challenging since it typically entails migrating several applications. This type of migration often requires customization regardless of the migration options offered by cloud providers.

Are you a beginner looking for Hadoop projects? Check out the ProjectPro repository with unique Hadoop Mini Projects with Source Code to help you grasp Hadoop basics.

Data Migration Tools

There are many different tools available nowadays to help with enterprise data migrations. These consist of licensed and open source data migration tools and vendor-specific solutions provided by cloud service providers to assist clients' migration into public or private cloud environments. The ideal data migration tools for a migration project depend on the data migration approach. In addition to using cloud-based or on-premises tools, organizations can also develop data migration scripts. Self-Scripted Data Migration is an internal solution; however, it is not extensible for large-scale data migration projects and may be appropriate only for small migration projects. On-Premises Data Migration tools are the best option if all the data is in one place. Organizations migrating their data from various data sources/platforms to a cloud-based destination may find cloud-based data migration tools useful.

Here is a thorough breakdown of some of the top data migration tools available in the market.

AWS Data Pipeline

AWS Data Pipeline is a popular web service that enables reliable data processing and migration between various AWS computing and storage services and on-premises data sources. You can simply build fault-tolerant, scalable, and highly available complex data processing workflows with the aid of AWS Data Pipeline. AWS data pipeline has many benefits, such as assuring resource availability, handling inter-task dependencies, developing a failure notification system, etc. Additionally, using AWS Data Pipeline, you can transfer and process data previously kept in on-premises data silos.

You may efficiently transfer the outcomes to AWS services like Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR by using AWS Data Pipeline to frequently access data wherever it goes, transform and process it at scale.

IBM Informix

An efficient and extensible database server, IBM Informix handles traditional relational, object-relational, and dimensional databases. IBM Informix uses a hybrid cloud infrastructure that lowers the expense of hardware and software management for businesses and promotes data migration. Fast data transfer from IBM Informix enables real-time data analytics on transactional data loads. The IBM Informix database server supports more than one operating system, such as UNIX, Linux, Mac OS X, and Windows. Besides the database server, all editions of Informix include the additional client tools- IBM Informix Client Software Development Kit (Client SDK), IBM OpenAdmin Tool (OAT) for Informix, and the Informix DataBlade Developers Kit (DBDK).

Fivetran

Fivetran is a highly extensive ELT tool that enables automated data integration and efficient extraction of business operations and customer data from associated servers, websites, and applications. The resultant data subsequently moves to other tools for analytics, advertising, and data warehousing. You can quickly start working with Fivetran if you have an existing account.

Once you connect a data warehouse, it displays a dashboard where you can add new connectors.

The setup page with precise notes on the screen and configuration verification will appear after adding a new connector.

When you set up your first connector, it will start synchronizing instantly, allowing you to immediately utilize the data in your data warehouse. You can add as many connectors as necessary to finish your first configuration.

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Data Migration Services

This section will cover the two most popular data migration services- AWS Data Migration Service and Azure Data Migration Service.

1. Azure Data Migration Service

Azure Data Migration Service enables seamless migrations from various database sources to Azure Data platforms with the least downtime possible. The service creates assessment reports using the Data Migration Assistant and offers suggestions to lead you through the changes necessary before any data transfer. Azure Database Migration Service takes care of all the essential tasks once you're ready to start moving data.

You can move SQL Server databases to either Azure SQL Managed Instance (Platform-as-a-Service) or SQL Server on Azure Virtual Machines by using the Azure SQL Migration extension in Azure Data Studio (Infrastructure-as-a-Service). With the help of the wizard provided by the Azure SQL Migration extension for Azure Data Studio, you can determine whether your SQL Server databases are ready for moving data, receive suggestions for the best Azure plan size, etc. The Azure Database Migration service coordinates data relocation tasks and offers task monitoring.

2. AWS Data Migration Service

You can move databases to AWS rapidly and securely using the AWS Database Migration Service (AWS DMS). In this migration service, the application downtime that depends on the source database is minimal as it stays entirely functional across the migration. You can migrate your data between the most common commercial and open-source databases with the help of the AWS Database Migration Service.

In addition to heterogeneous migrations from various database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora, the AWS Database Migration Service also allows homogenous migrations, such as Oracle to Oracle. You can actively duplicate data from any eligible source to an acceptable target with minimum latency using AWS Database Migration Service. For instance, you can develop a highly available and scalable data lake solution by replicating data from several sources to Amazon Simple Storage Service (Amazon S3). You can combine databases into giant data warehouses by streaming data to Amazon Redshift.

Best Practices for Data Migration

Data Migration Challenges

Although modern IT systems have advantages over the risk associated with data migration, especially in the long run, it can be difficult and risky. You can run into some challenges and roadblocks typical to most migration projects, even with a solid data migration plan. Here are a few risks to be aware of.

Lack of Automation- Data migrations include migrating several elements, and the absence of automation and additional software solutions might cause delays or errors.

Data Backup- Data loss during migration is possible; therefore, it is essential to back up data and carefully plan the migration with help and support from professionals.

Data Security- Encrypt all sensitive data before migration to improve stability and security. 

Expensive- Poor planning frequently leads to hidden costs. Online transfers, for instance, will cause additional expenses if they take longer than planned.

Data Governance- It is essential to understand who has the authority to review, change, add, or remove data from the source system.

When conducting a data migration process, here are a few best practices you must follow to ensure that the process is smooth, successful, and doesn't incur expensive delays.

1. Build a Migration Plan and Adhere to it.

You need to build a detailed strategy that specifies what data needs to migrate, where it needs to go, how it will get there, etc. Your plan must also include the criteria for who should access the data. Organizations must also form a dedicated migration team with suitable professionals to oversee and manage the project.

2. Establish Migration Policies.

 It is not enough to just have a plan in mind; it is also necessary to create policies for data migration throughout the entire business and implement enforcement measures. Your policies should make sure that data is being moved to the appropriate location and is given protection after migration.

3. Gain a Clear Understanding of the Data.

Analyze the data you plan to migrate before writing mapping scripts. Focus more on removing data that is no longer necessary or outdated. This will ensure that a clean dataset is available for your team to work with after the migration and make your migration easier overall.




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Data Migration Project Ideas to Practice in 2022

We believe you have a clear picture of data migration and everything you need to know about it. Check out these easy yet exciting data migration project ideas to help you put everything you have read in this blog into practice.

Enterprise Sales Data Migration Idea

Consider a merger between companies operating in the same industry and using the same platform to manage their sales and marketing data. The main challenge here is migrating a large amount of unorganized and error-prone data to the common platform. Other challenges with this business scenario include duplicate data in the system because both organizations may have many of the same clients and the need to identify proper ownership after the Lead, Accounts, and Opportunities migrate. This project idea entails migrating all of the data for Leads, Accounts, Contacts, and other entities from one organization’s database to another and combining it into one organization’s database. For this project, you can use any e-commerce sales dataset (e.g., Olist store sales dataset and BigMart sales dataset) from Kaggle.

Finance Data Migration Project Idea

A financial service ((loan) provider buys a leasing company and incorporates its IT system. The resulting company is now in need of migrating its computer services to a new cloud provider—especially one with a solid track record for compliance with regulations and data security. Use Microsoft Azure for this project, a cloud service that would significantly minimize recurring infrastructure costs. You can migrate its data onto Azure following the guidelines provided by Microsoft's Cloud Adoption Framework. You can use the Loan dataset available on Kaggle for this project idea.

Healthcare Admin Data Migration Project Idea

Consider a scenario where a software provider provides hospitals and medical clinics with management software that handles data management, billing, patient records, telemedicine, and other tasks. For this project idea, use the AWS data migration tool since it offers limitless scalability, highly available data redundancy, enhanced data compression and elimination of unwanted data, and automated tiering. You can migrate the entire healthcare administration database to Cloud Volumes ONTAP for AWS using the simple drag and drop interface of Cloud Manager. Use the healthcare analytics dataset from Kaggle for this project idea. 

Getting Started with Data Migration

By now, you know that data migration plays a crucial role in the growth of an organization. Not just organizations, every individual planning to enter the field of data analytics, data science, or big data engineering must possess efficient data migration knowledge and skills to excel in their career. Although this blog covers the theoretical aspects of data migration, you must focus on gaining practical experience by working on real-world data migration projects. Try looking for some data engineering projects on Github, or you can even check out the ProjectPro repository. It offers over 250 solved end-to-end industry-level projects on data science and big data, with free guided project videos, reusable solution code, downloadable datasets, and one-to-one industry expert guidance.

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FAQs on Data Migration
1. What is data migration example?

A data migration example scenario is when two businesses merge, or one of them acquires the other. IT frameworks and data typically need to be integrated to establish a single unified system for the new company.

2. What are the steps in data migration?

The steps in data migration are as follows.

Planning- This is the most crucial phase of your data migration process, and it lays out the performance targets, gives a defined path, and sets your expectations for the entire project period.

Preparing the data- After determining the project's scope, forming a data migration team, and laying the groundwork, it's time to prepare your data for migration.

Designing migration process- This stage of the migration process specifies the testing protocols, acceptance criteria, and other employee duties.

Executing the process- This step involves the ETL operations- extracting, transforming, and loading the data.

Testing- As data elements enter the target infrastructure, regular testing ensures their security, high quality, and compliance with standards.

Auditing- Conduct a thorough audit of the system and data quality after the data migration process to ensure that everything is accurate.

3. What is data migration in ETL?

Data migration in ETL refers to extracting data from one system, converting and aggregating it if necessary, and then loading it onto the target system.

4. Why is data migration needed?

Data migration is needed as it enables you to consolidate data from one source into a single storage system, such as a cloud data warehouse, data lake, or lakehouse. The migration process leads to faster insights as data analysts, and other staff members can quickly access all the data they require from a single system.

5. What is Data Migration in SQL?

Data migration in SQL is moving data to or from the SQL Server. The migration process can be very complex, especially when moving a significant amount of enterprise data.

6.How do you make a data migration plan?

You can make a data migration plan by considering the following points-

Understand the data with the help of thorough data analysis.

Clean up and prepare the data using various additional tools and expert assistance.

Create data backup as it helps in avoiding any data loss and maintaining the data quality.

Keep track of the data quality to ensure data integrity.

7. What is a data migration strategy?

A database migration strategy is a detailed plan that simplifies the movement of your data from one platform to another. Such an approach encompasses several variables, including data auditing, cleaning, maintenance, security, and regulation. A well-defined strategy can minimize the effects of database migration on businesses.D?