The amount of data produced by businesses is ever-increasing. It becomes nearly impossible and very expensive to store it on-premises. So, many companies are adopting cloud solutions to move their data. However, you need to develop an effective cloud data migration strategy to do it successfully. How to do that, and what best practices should you stick to? Our cloud and data experts have shared some thoughts.
Where can you move your data?
No matter what type of cloud (public, hybrid, or multi-cloud) you choose for cloud data migration, you will likely use data warehouses. Moving to data warehouses allows organizations to centralize their data from various sources for analysis, reporting, and decision-making. Let’s look at the leading market players, such as Redshift, Snowflake, BigQuery, and Teradata.
Redshift
Amazon Redshift is a fully managed cloud data warehouse service provided by AWS. In comparing market shares among cloud data warehouse services, Amazon Redshift leads with 18.7% of the market, as reported by Enlyft, and is utilized by approximately 8,007 companies for cloud data management. Key applications of Redshift include Business Intelligence, operational analytics, and data sharing.
Snowflake
Snowflake stands out as a leading cloud-based data warehouse solution offered as a Software as a Service (SaaS), eliminating the need for developers to install, configure, or manage any hardware or software. According to Enlyft, Snowflake has 14% of the market share, with 6,250 companies worldwide utilizing it for cloud data management. Key applications of Snowflake include data warehouse modernization, secure data sharing, integrated data engineering, advanced Data Science, and more.
BigQuery
BigQuery, provided by Google Cloud Platform (GCP), is a cloud data warehouse service with a market share of approximately 12.5%, as per Enlyft. It is the choice of over 5,000 companies for storing and analyzing data. Unique to BigQuery is its absence of virtual machines, keys, indexes, or software installations, eliminating the need to deploy multiple clusters or duplicate data across them. BigQuery is utilized for various purposes, including predictive analytics and data consolidation.
Teradata
Teradata is a prominent provider of cloud data analytics solutions, renowned for its comprehensive data warehouse technology. It holds an 8.58% market share, according to InfoCrunch. Applications of Teradata include data integration and multi-cloud and hybrid cloud environment support.
With any type of cloud and any data warehouse you will choose to use, to make your cloud data migration work, you need an effective strategy. How to choose it? Let’s find out together.
Cloud data migration: the best strategies to choose from
Choosing the right strategy depends on the specific business requirements, data volume, system architecture, and tolerance for downtime. Often, a combination of these strategies is employed to balance risk, downtime, and resource allocation effectively. Here are the main strategies you can choose from.
1. Big Bang migration
This is a method of transferring all components of one system to another in a single, comprehensive move. Big Bang migrations are characterized by their speed and immediacy but come with their own set of challenges and risks.
2. Phased migration
This is an approach to system or data transition where the process is broken down into manageable stages or phases, allowing for a gradual move from an old system to a new one. Phased migration is particularly useful for complex, large-scale projects, where a step-by-step approach can help manage risk, minimize disruption, and ensure a smoother transition.
3. Parallel run
Parallel run data migration is a strategy where the old system and the new system run simultaneously for a certain period. This approach allows organizations to compare and validate the outputs of both systems to ensure the new system performs as expected before fully transitioning.
4. Incremental migration
This approach is about moving in small, manageable batches over time. Incremental migration focuses on segmenting the migration process into discrete, manageable parts, ensuring minimal disruption and providing the ability to troubleshoot issues as they arise.
But how can you ensure that your strategy will be successful? During years of experience with cloud solutions, our experts have developed some best practices that make migration smoother and easier.
Best practices that will help you move your data to the cloud successfully
1. Start with Proof of Concept
In data migration to the cloud, PoC is a preliminary project to demonstrate the feasibility of migrating specific data or applications to the cloud. It helps identify potential issues, assess the compatibility of the cloud environment with existing systems, and evaluate the performance and cost implications. By executing a PoC, you can make informed decisions about full-scale migration, mitigate risks, and refine your migration strategy.
2. Develop contingency strategies
Have a detailed rollback plan for each migration stage to quickly recover from unexpected issues. Regularly test the effectiveness of these plans and set a maximum execution time for each migration step, initiating rollback if exceeded. Recognize potential disruptions, like data loss, and prepare for automation failures and communication channel backups.
3. Perform data cleansing
Data cleansing, also known as data cleaning or scrubbing, is the process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset, database, or table. This activity involves identifying incomplete, incorrect, irrelevant, duplicated, or improperly formatted data and then modifying, replacing, or deleting the dirty or coarse data.
There are many reasons why data cleansing is vital before cloud migration. For instance, transferring large volumes of data to the cloud can be expensive and time-consuming. By cleansing data first, you can reduce the volume of data being migrated, leading to lower storage costs and shorter migration timelines.
What’s more, In the cloud, data often needs to be integrated from various sources. Clean, standardized data simplifies this integration process and ensures that data from different systems can be combined and utilized effectively, enabling more comprehensive analytics and insights.
4. Validate your data
It is a critical process in ensuring the accuracy, quality, and integrity of data before it's used for analysis, reporting, or decision-making. It involves checking data against specific rules, constraints, or standards to identify and correct inaccuracies or inconsistencies. Data validation aims to ensure that data is both structurally and content-wise correct and useful for its intended purpose.
Data validation checks for data accuracy and consistency before and after cloud migration. This ensures that data has not been corrupted, altered, or lost during the transition process, maintaining its integrity. Also, by validating data, you can identify potential issues or discrepancies before they become problematic, ensuring a smoother migration process. This preemptive approach helps avoid costly and time-consuming corrections post-migration.
5. Establish data governance
Essentially, data governance acts as a framework for data management strategy, ensuring that data across the organization is accurate, accessible, consistent, and protected. As organizations accumulate more data, the need for proper management and utilization of this data becomes paramount. This is where the importance of data governance comes into play. However, to leverage data governance effectively, it's crucial to first develop a robust data governance strategy.
Migrating data to the cloud introduces complexities regarding data security and compliance with various regulations (such as GDPR and HIPAA). Data governance establishes the policies and procedures needed to protect sensitive information, manage data access, and ensure adherence to legal and regulatory requirements. Moreover, effective data governance helps classify data, identifying which data is critical and sensitive. This classification is crucial for applying appropriate security measures and efficient data management in the cloud, including storage optimization and cost management.
Following these practices helps achieve a successful data migration, minimize risks, and ensure alignment with business goals.
Read more about cloud migration roadmap
Key challenges of cloud data migration
1. Cloud security
According to the survey from Statista, almost half of the companies identified security and compliance risks as a major challenge of cloud migration. Those companies struggle to ensure data integrity and confidentiality during and after the transfer, manage access controls and identity verification, and comply with regulatory requirements. Organizations must also address potential vulnerabilities that could be exploited during the migration process and ensure that cloud providers' security measures align with their own security policies and standards.
N-iX tip: Effective ways to mitigate security risks involve encrypting data both in transit and at rest, using secure connections for data transfer, and conducting thorough security assessments before, during, and after migration.
Learn more about 9 tips to ensure robust cloud security
2. Cost overruns
Cost overruns were the second-most common cloud issue, with 43% of surveyed companies mentioning it. Factors that trigger cost overrun include underestimating the complexity of the migration, failing to predict the total cost of ownership in the cloud, and not adequately preparing for the required scale of resources. Additional costs may also emerge from unexpected technical challenges, the need for specialized skills, or extended timelines due to unforeseen difficulties. It's essential to have a comprehensive and realistic budget plan, considering all possible variables and contingencies, to mitigate the risk of cost overruns.
N-iX tip: To mitigate cloud cost overruns, planning and monitoring cloud usage is crucial. Start with a clear understanding of cloud pricing models and services. Implement budget tracking and set alerts for thresholds. Use automation to scale resources dynamically, ensuring you're not paying for idle capacity. Regularly review and optimize your cloud resource usage, removing unnecessary storage or instances. Consider reserved instances for long-term savings. Continuously monitor and adjust your cloud strategy based on actual usage patterns and evolving business needs.
3. Ensuring compliance
Ensuring compliance during cloud data migration is challenging due to the complexity of regulations across different regions and industries, the dynamic nature of cloud environments, and the need to maintain data integrity and security across the migration process. Additionally, understanding the shared responsibility model in cloud services, where both the cloud provider and the client have roles in ensuring compliance, adds to the complexity. Continuous monitoring and adapting to new compliance requirements also pose significant challenges.
N-iX tip: To ensure compliance, it is important to develop a comprehensive data governance strategy that includes data classification, risk assessment, and compliance checks. For instance, at N-iX, we have over 300 data governance consultants and engineers who have helped multiple businesses establish effective data governance. This allowed our clients to unlock the value of their data and ensure it continues to bring lasting business results.
Wrap-up
Cloud data migration is a complex process that is driven by the need for businesses to enhance scalability, flexibility, and accessibility while reducing operational costs. This process is often accompanied by challenges such as ensuring data security, cost overruns, and others. Successful data migration takes time and extensive tech expertise. Thus most businesses entrust it to reliable tech vendors.
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