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Data movement is an essential aspect of modern computing, as it involves the transfer of data from one location to another. This process is critical for data processing, storage, and analysis, and it can be performed in various ways, including network transfers, file transfers, and memory transfers.

Here are the 5 key takeaways:

  • Data movement refers to transferring data between different systems or locations using techniques like ETL and ELT.
  • Replication and synchronization are the two main methods used in data movement to ensure data consistency between different data ecosystems.
  • Data movement solutions are becoming basic competencies for enterprise companies, with the global data migration market projected to grow to nearly $23 billion by 2026.
  • Common use cases of data movement include archiving data, data warehousing in the cloud, database replication, and cloud data lakes.
  • Benefits of data movement include synchronization control, better server performance, and data protection in case of data breaches or corruption.

In this article, we'll take a deep dive into data movement, exploring the different types of data transfers, their strengths and weaknesses, and some of the challenges associated with moving data efficiently and securely. We'll also discuss some best practices for optimizing data movement and explore some of the emerging technologies that are helping to improve data transfer speeds and reduce latency.

Introduction

Data movement refers to transferring an organization's data between different systems or locations. It is a smaller step in the overall data migration process that involves other steps such as preparing data for the target system, data validation, and updating any processes or applications that use the data. It uses techniques like ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) to facilitate this movement of data between on-premises or cloud-based data centers.

ETL is used when the data format in the old system is not adequate for the new system. ELT, conversely, is more suitable when the target system has the computational capability and resources to handle the data transformation. This method is more efficient than ETL, as it allows the data to be transformed while the loading process is on-going. ETL and ELT are widely used in data movement, but the method of choice will vary based on the situation and the company's needs.

Data movement solutions are becoming basic competencies for enterprise companies. The global data migration market is exponentially growing and its projected growth by 2026 is nearly $23 billion. With the IT infrastructure and operations landscape constantly transforming in big data companies, it is now vital to have reliable and efficient data warehousing and data movement solutions to ensure that data is seamlessly migrated without adversely impacting performance.

Methods of Data Movement

The two main methods used in data movement are replication and synchronization. Both these methods are used to ensure data consistency between different data ecosystems. The choice of which method to use depends on the particular requirements of the data movement process at the company, such as real-time updates between managed services and systems or the availability of resources.

Replication

Replication involves creating multiple copies of data from single or multiple-source databases or datasets and storing them at different locations. It offers a dependable and cost-effective method of creating and maintaining accurate copies of the original data and metadata. Replication improves data availability and enables faster access by deploying the replicated copies to relevant users who can access them from any location. Therefore, companies can maintain control over the replicated data.

Synchronization

Synchronization is about ensuring data consistency across different systems and locations. It automates the process of syncing the replicated copies with each other. As soon as there is a change in the original data source, the copies will update accordingly. The updates can be scheduled at specific intervals by retrieving data from the source (batch-oriented) or can be done instantly by transmitting data from the source to the copy (real-time). Data synchronization becomes crucial when multiple individuals or systems require access to view and update the same data. Software tools and web services that offer data synchronization provide additional features like versioning, data backup, conflict resolution, and disaster recovery.

Common Use Cases

Data movement and transformation capabilities assist in the modernization and extension of IT operations in a company. They are essential in meeting different integration and scalability requirements, such as migrating data from transactional databases to data lakes for data analysis purposes or consolidating various data sources for better management, etcetera. There are several other use cases for data movement, and some of them are listed below:

  • Archiving Data: As your databases expand, it becomes increasingly important to implement measures to archive data for long-term retention. Data movement solutions can automate the process of identifying, transferring, and storing the data to be archived using different methods like file-based archiving and database archiving. Additionally, they maintain smooth operations during archiving and can facilitate future audits for the archived data.
  • Data Warehousing in Cloud: Companies must ensure that their data warehouses contain accurate and up-to-date information. Data movement can be used to move data from legacy to a centralized data warehouse in the cloud, such as from MySQL or Oracle to Microsoft Azure SQL or Amazon AWS. Crucially, data movement solutions achieve this without negatively impacting the uptime and performance of the overall system.
  • Database Replication: Data movement enables efficient usage of distributed resources through database replication. Replicating data across multiple databases can provide the company with disaster recovery and workload-balancing capabilities. It also minimizes downtime as the system remains readily available and accessible even when there is a failure in one of the databases.
  • Cloud Data Lakes: As aforementioned, companies can employ data movement to migrate data from transactional database systems like Adabas to data lake environments like Hadoop and Snowflake. Data lakes allow faster accessibility, preparation, and analysis of data. 

Related Reading: Data Lakes: The Achilles Heel of the Big Data Movement

Benefits of Data Movement

Synchronization Control

Data movement allows for complete control over when and how data is transferred, including scheduling incremental or full transfers and performing tasks before or after a transfer. The frequency of data transfer can also be adjusted based on the needs of the business, such as scheduling updates during low-activity times or as frequently as every minute when real-time data is necessary. In short, such solutions enable your organization to scale the data synchronization process on-demand, providing flexibility and scalability.

Better Server Performance

Data movement enables the efficient use of server resources by directing operations to the servers that have the most capability. This can lead to improved performance, which is particularly beneficial for industries that require timely processing, such as healthcare or banking. For example, by directing read operations to a copy of the original database, you can free up resources on the primary server for more crucial write operations.

Data Protection

Data movement solutions help mitigate the threat posed by data breaches and data corruption. The copies created and maintained through replication and synchronization act as data backups. In a security breach, the danger can be addressed without disrupting workflow. If a portion or the entirety of a database is damaged or lost, data movement can be used to restore it by utilizing the two-way transfer feature in synchronization. This allows the impacted database to be brought back to its previous state.

Real-time Replication

Such solutions allow the accumulation and replication of data from multiple sources, such as cloud platforms, data warehouses, and databases. The change data capture (CDC) technique is typically used to replicate that data to one or more other databases. It performs incremental data replication for better scalability.

Integration of Data Sources

The replication and synchronization of data from different sources (legacy and cloud) mean there is a single source of truth. This can enable organizations to monitor performance across the board through interactive analytical dashboards and also helps in maintaining data integrity and quality.

Risks Involved in Data Movement

Data Security

Ensuring the security of data is one of the significant concerns when implementing data movement, especially when data is of a sensitive or confidential nature. The transfer of data can put organizations at risk for security breaches. If the movement is taking place on an online channel, data encryption rules can be implemented to prevent violations, and other security measures like access controls can also be utilized.

Loss of Data

When the data shifts from a legacy system to a new one, some data may not correctly migrate, leading to temporary or permanent data loss. This could happen due to hardware malfunctions, network outages, underlying data types/format changes, or other reasons. This can be avoided by replicating, using checksums, and defining disaster recovery protocols.

Latency Issues

There is a problem of extended downtime when the data movement process takes longer than initially anticipated for various reasons. This could pose potential risks and rising overheads for those companies that cannot afford such latency issues to disrupt the business. Such problems can be mitigated by defining adequate data transfer protocols and enhancing the overall data movement process.

Storage Problems

Stakeholders can encounter unforeseen capacity problems during the data movement process. The storage space available for the data being transferred may be limited. There is also a possibility of the same data being migrated multiple times, resulting in unnecessary storage usage and potential consistency issues. This can be prevented by allocating adequate storage for the data movement beforehand and running data validation techniques to double-check the transferred data's originality.

Data Movement With Integrate.io

It is becoming imperative for businesses to equip themselves with the proper data movement tools and strategies. The Integrate.io toolkit offers all the cutting-edge tools you need to perform smooth and secure data migration and movement.

Are you ready to discover how the Integrate.io platform can help you with your data movement needs? Contact our team today to schedule a 7-day demo or pilot and see how we can help you reach your goals.