MODERN APPROACHES AND CHALLENGES IN DATA MIGRATION BETWEEN TOOLS AND REPOSITORIES

ABSTRACT

In today's rapidly evolving world, where technology is constantly improving, database (DB) migration plays a crucial role in the development of companies. This is especially important for companies that have both small databases and huge amounts of data, known as Big Data. The purpose of this article is to analyze issues related to safe and efficient database migration. The scientific value of the work lies in an attempt to give recommendations on the process of transferring data to various platforms. The author applies theoretical research methods, practical experience, and also uses the results of other scientific studies.

АННОТАЦИЯ

В современном быстро развивающемся мире, где технологии постоянно совершенствуются, миграция баз данных (БД) играет решающую роль в развитии компаний. Это особенно важно для компаний, которые имеют как небольшие базы данных, так и огромные объемы данных, известные как большие данные. Целью данной статьи является анализ вопросов, связанных с безопасной и эффективной миграцией баз данных. Научная ценность работы заключается в попытке дать рекомендации по процессу переноса данных на различные платформы. Автор применяет теоретические методы исследования, практический опыт, а также использует результаты других научных исследований.

Keywords: Information technology, Big Data, database migration.

Ключевые слова: Информационные технологии, Big Data, миграция баз данных.

1. Reasons for database migration

Data migration is a critical process that involves transferring data from one system or storage infrastructure to another. It plays a crucial role in various scenarios, including system upgrades, platform migrations, and consolidation of data sources.

Small databases can be migrated to improve performance, reliability and data security. This may include migrating data to cloud platforms that offer flexibility and easy access to data from anywhere in the world. In addition, migrating to a new system can provide more advanced data management and analytics tools that help companies make faster, more informed decisions and extract more value from their data.

In the case of Big Data, database migration becomes even more important. Processing and analyzing large amounts of data requires high performance, scalability and parallel processing.

Another reason to migrate databases is to reduce costs. Migration reduces costs in the long run. Upgrading older systems to a current platform can be a complex and resource-intensive process. In addition, in this case, the company is forced to support both platforms, which leads to additional costs for equipment and support. Migrating data to one modern platform avoids these costs.

Also, database migration can be a trigger to improve the efficiency of the entire IT department. If the migration process is carried out correctly and takes into account the target architecture, it can be the starting point for the digital transformation of the company. A well-designed migration can bring benefits such as efficient big data handling and new functionality.

In addition, database migration to new platforms ensures data security. Older systems can be vulnerable and susceptible to external threats. If done well, migrating to modern platforms will provide stronger data protection measures and reduce the risk of leakage or loss of confidential information.

And finally, database migration can provide new opportunities for efficient analysis and use of data. More modern platforms support working with large amounts of data and provide advanced capabilities and tools for data analysis and data-driven decision-making [3].

2. Main approaches to database migration

ELT (Extract, Load, Transform) is the most widely used approach to delivering data from various sources to a centralized system for ease of access and analysis. This approach includes the steps of data extraction, loading, and transformation.

The ELT approach has emerged with the growth of volumes and types of data. Before its appearance, a large number of data types that needed to be converted first, and then transferred, was a rather significant problem, since this, first of all, is time-consuming and inefficient.

The main advantage of the ELT approach is that the data is loaded into the target system before it is converted. This approach has many advantages, especially when the target system has enough power and analytical capabilities to work with the data directly, without the need for prior conversion.

Modern technologies open up opportunities for the effective application of the ELT approach. They allow us to store and process huge amounts of data in various formats. An example of such technology is Apache Hadoop, an open-source platform that is able to process data from different sources, regardless of their type.

In addition, cloud data stores such as BigQuery, Snowflake, and Redshift play an important role in supporting the ELT methodology. They provide the ability to separate storage and computing resources and are highly scalable. This means that the data can be uploaded directly to the cloud storage without pre-processing. This approach allows you to effectively use the powerful analytical capabilities of the target system for working with data and provides flexibility and scalability in information processing [2].

CDC (Change Data Capture) is a data change capture approach that allows you to track and replicate changes that occur in the source database (DB) and transfer them to the target database or other systems in real-time.

CDC detects and captures only those changes that have occurred in the source database since the last capture and propagates them to the target database. Instead of migrating the entire data, CDC focuses on capturing change operations (insert, update, delete) and their metadata such as source time, row ID, and change type.

This approach has a number of advantages. For example, efficiency and saving resources. Because CDC transmits only changes, the amount of data transferred is reduced, allowing for more efficient use of resources and reducing network bandwidth requirements. CDC can also work with various types of data sources, including traditional relational databases, cloud storage, file systems, and other systems. Another advantage is the minimization of interruption of work. CDC allows you to update the target database or other systems in real-time, which minimizes downtime and reduces the impact on system performance [2].

Cloud Migration. With the growing popularity of cloud services, migrating databases to the cloud has become increasingly popular. This allows companies to avoid the hassle of managing their own infrastructure and instead use cloud storage and processing services. Cloud providers provide specialized tools and services to simplify the process of migrating databases to the cloud. Cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google^* Cloud Platform (GCP) offer a wide range of services and resources for storing and processing data.

3. Data migration challenges

When carrying out such a complex process as data migration, there may be some challenges and problems. The most common and serious aspects to pay attention to are:

• ­­ ­Security and privacy. When migrating data, you must ensure its security and the protection of confidential information. This includes the use of data encryption, access control, auditing, and data access control in the target tool or vault.
• Data loss. There is always a risk of data loss or corruption during data migration. To prevent such problems, it is recommended to back up data, test before and after migration, and have data recovery mechanisms in place in case of a failed migration.
• Monitoring and debugging. During the data migration process, it is necessary to monitor and debug to identify and eliminate possible problems and errors. This includes performance monitoring, data integrity monitoring, error tracking, and data compatibility issues.
• Interruption of work. During the data migration process, it may be necessary to temporarily stop access to data or even completely stop the system. This can cause inconvenience to users and the organization. Therefore, it is necessary to plan ahead and agree with business users and other stakeholders on the timing and operation of the migration to minimize system disruption.

Conclusion

In conclusion, data migration is a mission-critical task that requires careful planning, meticulous execution, and robust validation processes. It is a complex process that involves moving data from one system or storage infrastructure to another, often driven by the need for a system upgrade, platform migration, or data consolidation.

The success of a data migration depends on several factors, including a deep understanding of the data structure, dependencies, and relationships. It is critical to determine the appropriate migration strategy and prioritize data based on criticality and business requirements. In addition, factors such as data volume, complexity, security, and downtime limitations must be considered during the planning phase.

Data migration is not without problems. Incompatible data formats, data quality issues, data loss, and system compatibility issues can create significant bottlenecks. Overcoming these challenges requires a comprehensive understanding of the systems involved, effective communication between stakeholders, and the experience of skilled professionals who are well-versed in data migration techniques.

A successful data migration minimizes disruption to business operations, protects data integrity, and ensures a smooth transition to a new system or infrastructure. This enables organizations to take advantage of advanced technologies, increase data availability, and optimize their data management strategies [1].

References:

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3. Danilchik, V. V. Basic approaches to database schema migration / V. V. Danilchik // Young scientist. — 2020.
4. Marasanov A.M., Anosova N.P., Borodin O.O. Distributed databases and data warehouses // National Open University "INTUIT" - 2016.
5. Sukhomlin V.A., Belyakova O.S., Klimina A.S., Polyanskaya M.S.,Rusanov A.A. Model of digital cybersecurity skills 2020 // Modern information technologies and IT education. 2020.

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