Choose your optimal data migration strategy and environment with Intellias. <\/p>\n
What is data warehouse migration?<\/h2>\n
A data warehouse migration is the process of moving data from a data warehouse to a different storage system. The data could be transferred from one warehouse to another or to a different kind of storage, like a data lake.<\/p>\n
The most common data warehouse migration is from an on-premises to a cloud-based data warehouse. But migrations can also be spurred by the desire to change vendors. In these cases, the migration is from one cloud or on-prem warehouse to another.<\/p>\n
Why migrate your data warehouse to the cloud?<\/h2>\n
Migrating your data warehouse to the cloud can reduce licensing and maintenance<\/a> costs, enhance performance, improve scalability, and enable more advanced use cases.<\/p>\n These improvements are possible because of a few key features of cloud data warehouses:<\/p>\n The caveat, of course, is that the data warehouse migration to the cloud has to be done correctly. Moving data to the cloud doesn\u2019t automatically unlock the benefits of the cloud<\/a>; the system has to be reconfigured to work effectively in the cloud.<\/p>\n Each cloud data warehouse has unique attributes, the usefulness of which depends on your organization’s needs and goals and your existing technology stack.<\/p>\n Because of this, it\u2019s impossible to say which warehouse is ideal for your organization. Instead, the following section briefly summarizes each platform\u2019s core strengths to help you decide which options are worth researching further based on your needs.<\/p>\n As you might expect, Amazon Redshift is deeply integrated within the Amazon Web Services (AWS) ecosystem. So, if you\u2019re already using AWS services<\/a>, it\u2019s worth considering their cloud data warehouse.<\/p>\n Beyond that, Redshift\u2019s RA3 nodes offer uniquely flexible control over how you scale your compute and storage resources. Redshift Spectrum enables direct querying of data stored in Amazon S3. And with Concurrency Scaling, Redshift automatically adds commute capacity to handle spikes in user demand, which helps handle fluctuating workloads.<\/p>\n Redshift is best for organizations that:<\/p>\n Google BigQuery<\/a> is fully managed and designed for fast, cost-effective querying with pay-as-you-go pricing. Because BigQuery is fully managed, you don\u2019t have to worry about infrastructure management and can focus on using your data. BigQuery also supports real-time analytics and ML integrations with Google\u2019s AI tools.<\/p>\n It\u2019s worth noting that while the pay-as-you-go-pricing model can be cost-effective, it\u2019s not guaranteed. When usage spikes or if you have to run complex queries, costs can accumulate quickly.<\/p>\n BigQuery is best for organizations that:<\/p>\n Snowflake<\/a> offers multi-cloud support, which means it can run on AWS, Microsoft Azure<\/a>, and Google Cloud and integrate data across these platforms. Like Redshift\u2019s RA3 node type, Snowflake separates compute and storage resources. However, unlike Redshift, compute and storage are decoupled across all instances of Snowflake, regardless of your configuration.<\/p>\n Snowflake\u2019s flexibility makes it well-suited for dealing with diverse data types without significant performance sacrifices. This makes it a good choice when you require combinations of diverse data types, machine-learning applications, and real-time analytics.<\/p>\n Snowflake is best for organizations that:<\/p>\n Azure Synapse<\/a> is an analytics solution with tight integrations throughout the Azure ecosystem<\/a> that supports SQL-based cloud data warehousing and big data processing.<\/p>\n Because Azure offers traditional data warehousing and big data processing capabilities, it\u2019s useful for organizations that need to handle diverse data types and manage big data processing in the same environment that they manage data warehousing.<\/p>\n Azure Synapse is best for organizations that:<\/p>\n Although Microsoft has no current plans to retire Synapse, it believes that Microsoft Fabric<\/a> will \u201credefine the current analytics landscape,\u201d with the next versions of big data analytics products a core part of Fabric. Check out this article<\/a> to understand Microsoft Fabric if you already use Synapse.<\/p>\n At Intellias, we believe in a cloud-agnostic approach that puts your needs first. And we can execute this approach because of our deep expertise across all cloud providers and platforms.<\/p>\n Put simply, we can help you find the providers that ensure your migration project reduces costs, improves scalability, and unlocks modern capabilities.<\/p>\n Your data can do more with the Intellias data analytics services.<\/p>\n Planning your data warehouse migration strategy is as much about deciding what you will do as it is deciding what you won\u2019t do. A plan helps you stay focused on your business objectives as you navigate these decisions.<\/p>\n Here\u2019s how to start forming your plan:<\/p>\n Creating a plan for your data warehouse migration strategy starts with identifying a concrete destination. To do that, you need to choose measurable key performance indicators (KPIs) such as:<\/p>\n Based on your chosen KPIs, you can articulate a measurable, time-bound goal aligned with a long-term strategic objective. For example, your goal might look something like:<\/p>\n \u201cMigrate from on-premises to a cloud data warehouse within 12 months (time-bound) to reduce data storage costs by 30%, improve query performance by 50% (measurable), and enable access to new machine learning capabilities (long-term strategic objective), with no more than 2 hours of downtime during the transition.\u201d<\/p>\n Budgetary constraints impact every aspect of your data warehouse migration, from what data you move to where, when, and how you move it.<\/p>\n Creating a realistic budget greatly simplifies the many decisions you\u2019ll need to make as you formulate your data migration strategy<\/a>. So, it should be among your first steps.<\/p>\n Not only that, your budget could make or break your ability to garner internal support for your project. So, you should structure your budget plan based on your finance team\u2019s preferences.<\/p>\n Even the most straightforward data warehouse migration project is a team sport. Who you choose to involve in your data warehouse migration strategy is critical. Following Amazon\u2019s Two-Pizza Rule<\/a>, you\u2019ll want to limit your project team size to no more people than could be fed by two pizzas.<\/p>\n That means choosing representatives from teams that serve the following functions:<\/p>\n You\u2019ll also want to include a representative from each distinct group of people who use and rely on the data from your data warehouse. These groups may include data teams, business analysts, data scientists, and other end users.<\/p>\n Understanding different types of data warehouse migrations will help you align your approach to the unique demands of your project.<\/p>\n To that end, below are seven types of data warehouse migrations, classified based on the environment being migrated to and the technology architecture involved.<\/p>\n These 13 steps cover everything from pre-migration preparation to post-migration optimization to provide a clear path for a smooth transition. Follow these steps to stay on track and set your data infrastructure up for long-term success.<\/p>\n Like any other project that requires time and resources, defining objectives, establishing requirements, and building a business case is the first step. Without a strong business case, you can\u2019t get the funding and organizational buy-in you\u2019ll need. And without clearly defined objectives and requirements, your project will inevitably fall victim to scope creep.<\/p>\n Don\u2019t rush this step. While it might feel good to jump right in, gaining clarity on objectives and requirements provides a North Star for decision-making throughout the migration process.<\/p>\n Every organization is different, so the departments you need to involve will be unique to your business.<\/p>\n But it\u2019s critical to assemble a team that can provide the necessary perspective on the data infrastructure, the existing data warehouse\u2019s architecture and design, data flow, data security and compliance, and data governance<\/a>.<\/p>\n You\u2019ll also need to include at least one representative for each distinct group of end-users (e.g., business analysts and data scientists) as well as executive leadership.<\/p>\n You may have a general sense of the state of your organization\u2019s data, but you need to get granular to ensure a seamless migration. This means identifying all data sources, classifying data by type, usage, format, and sensitivity, documenting metadata, and assessing the data for completeness, consistency, accuracy, relevance, and redundancy.<\/p>\n After auditing your data, you\u2019ll likely have found plenty of issues. This is an ideal time to update governance policies to address these issues and identify data that can be left out of the migration or cleaned before it\u2019s moved.<\/p>\n Depending on what you find in your audit, you may also need to revisit your project budget and timeline to ensure they align with the work that needs to be done.<\/p>\n The point of planning for schema mapping<\/a> and data transformation is to ensure that the data in your source system is accurately migrated to your destination system. That means aligning your old and new schema, transforming data as needed, and preserving data relationships.<\/p>\n Fortunately, half the job is done already if you\u2019ve done a good job auditing your data. Next, you\u2019ll need to identify what data needs to be transformed or can be migrated directly and how the source schema maps to the target schema.<\/p>\n The right migration approach helps you manage operational impact, cost, and timeline. To determine the right approach, consider factors such as data volume, downtime tolerance, compliance and security requirements, and operational dependencies.<\/p>\n Common data warehouse migration options include the lift-and-shift, re-architecting, rehosting, refactoring, and re-platforming approaches. You can also mix and match these methods to align with your business needs and goals.<\/p>\n The right migration infrastructure gives you the sandbox, staging, and testing environment you need to validate your migration strategy.<\/p>\n And tools help you simplify and accelerate the migration. Depending on your target data warehouse, there are likely pre-built tools such as Redshift Data Transfer Service, Google Cloud<\/a> Dataflow, or Snowpipe for Snowflake.<\/p>\n With a pilot migration on a representative dataset, you can test your migration setup and uncover potential issues before you start the full migration.<\/p>\n Migrate a subset of your source data warehouse\u2019s data to the target system to validate schema mappings, transformations, and loading processes. Assess the pilot for data accuracy, query performance, and compatibility with existing workflows and then make any necessary adjustments.<\/p>\n This step is the culmination of all your preparation work. Begin migrating your data warehouse and stick to the plan you set up in step six and tested in step eight.<\/p>\n Monitor the migration closely to catch potential problems early so you can make any necessary on-the-fly adjustments to keep the migration on schedule and within scope.<\/p>\n Testing and validation are how you ensure data accuracy and performance in your new environment. This should include:<\/p>\n Adjust indexing, partitioning, and other settings based on your pilot test, expected workloads, and simulated load testing on the new system to optimize performance.<\/p>\n Update your documentation to reflect the new environment\u2019s architecture and processes. This final optimization phase ensures your new environment is resilient and operates efficiently.<\/p>\n Once the migration is complete, it\u2019s time to start training users on any new processes, tools, or reporting interfaces. As you go, solicit feedback from users to identify and deal with any issues they\u2019re facing.<\/p>\n This step is as critical as any technical implementation because it will encourage user adoption.<\/p>\n The end of the migration phase is the beginning of the maintenance phase.<\/p>\n Monitoring tools are key to maintaining data quality, performance, and user satisfaction. You can use these tools to track system performance, usage patterns, and data integrity, and address any issues when they occur.<\/p>\n This also gives you insight you can use to plan for iterative improvements by refining queries, adjusting configurations, and adding or updating data pipelines as needed.<\/p>\n Nothing is more important to the success of your data warehouse migration than clear goals and system requirements. But, defining your goals and clarifying your requirements is not straightforward.<\/p>\n It requires deep, careful analysis of your existing workloads, resources, workflows, budget, continuity requirements, compliance and legal regulations, and potential future needs.<\/p>\n The labor in planning is worthwhile, though. Clear goals and requirements will help you eliminate the long list of alternative tools, configurations, and strategies you could potentially pursue.<\/p>\n Understanding 1) who will use the new data warehouse and 2) how it interacts with other systems gives you the necessary perspective to minimize disruption and optimize the system.<\/p>\n Identify and meet with key user groups\u2014such as business analysts, data scientists, and executive teams\u2014and work to understand their unique requirements.<\/p>\n The insights you glean from these meetings will help you make better configuration choices, ensure compatibility, and optimize workflows. Equally important is documenting dependencies like integrations, data flows, and data ownership so nothing is overlooked in the transition and all connections remain intact.<\/p>\n Migrating data is an excellent opportunity to address inconsistent, inaccurate, or duplicate data. Rather than transferring flawed data, clean it before and during migration. This could include standardizing formats, validating records, and removing redundant information.<\/p>\n Cleaning data as you go minimizes the quantity of low-quality, unreliable data in the new warehouse. This enhances performance and establishes trust in the system.<\/p>\n A breach carries significant downside risk to an organization. And a data warehouse migration project<\/a> is an ideal opportunity for hackers.<\/p>\n Security should be a top priority throughout the project. Protocols should be reviewed and updated to align with the new cloud environment\u2019s requirements and capabilities.<\/p>\n This includes configuring access controls, encryption settings, and monitoring tools to ensure your system meets compliance standards and protects sensitive data throughout the migration process.<\/p>\n With so many moving pieces, validating your approach is key to a successful migration.<\/p>\n Before you move any data, test your plan with sample datasets, checking for accuracy, performance, and compatibility. Before and after migrating, review data integrity to verify that no records are missing, duplicated, or corrupted.<\/p>\n Validating multiple times helps reduce the risk of errors, streamline troubleshooting, and increase confidence in your migration.<\/p>\n Each data warehouse handles data formats, schemas, and structures differently. These differences are especially stark in an on-premise data warehouse vs. a cloud data warehouse.<\/p>\n This is why carefully mapping schema from source to target system, using schema conversion tools, and conducting comprehensive testing are vital to preventing issues.<\/p>\n As the scale of your data warehouse migration increases, the likelihood of corrupting, duplicating, or losing data increases. Issues with data integrity can cause reporting errors, less trustworthy data, and broken processes.<\/p>\n Again, testing and validation are crucial to maintaining data integrity. Breaking your migration into phases can also help mitigate the risk of large-scale data integrity issues.<\/p>\n Depending on your organization\u2019s reliance on continuous data access, minimizing disruption to daily operations can be tricky. Since downtime can lead to productivity loss and lost revenue, maintaining business continuity is critical.<\/p>\n A phased data migration can help you balance your organization\u2019s needs while moving forward with the migration.<\/p>\n Optimizing performance is a project in itself when migrating from an on-premises data warehouse to a cloud-based warehouse.<\/p>\n This is because the way on-premises warehouses store and process data is much different from how cloud-based warehouses do. However, to maximize the benefits of the cloud environment, tailoring your system\u2019s performance optimization strategies is critical.<\/p>\n Data is at its most vulnerable when it\u2019s on the move. A breach or failure to maintain compliance with regulations can lead to fines, legal action, and damage to the company\u2019s brand.<\/p>\n To keep data secure, plan to use encryption, secure transfer protocols, and carefully assign access controls. Work with your vendors to ensure they\u2019re helping you stay compliant and secure.<\/p>\n By design, a wide variety of applications, databases, and third-party tools typically rely on a data warehouse. And disrupted integrations with these systems can impact business operations.<\/p>\n This makes it critical to document, test, and establish compatibility with all integrations, workflows, and systems.<\/p>\n Cloud-based pricing is much different from traditional on-premises costs. And if you don\u2019t plan for and monitor costs, you may incur unexpected expenses. Between data storage, processing, and additional services, these costs can escalate quickly.<\/p>\n To avoid this, use cost-monitoring tools, set budgets, and optimize resources by right-sizing storage and processing power based on actual needs.<\/p>\n A cloud data warehouse migration isn\u2019t complete when the last byte of data is transferred; it\u2019s complete when the last byte is transferred, and the system\u2019s users understand how to use the system.<\/p>\n Lack of user adoption kills the ROI of even the most technically sound data warehouse migrations. This is why providing thorough, ongoing training for end users is critical.<\/p>\n Tools are key to accelerating your migration by automating repetitive tasks and eliminating human error. Which ones are most useful to you depends on which vendor you work with.<\/p>\n The major cloud providers offer a variety of tools to make your data warehouse migration easier.<\/p>\n AWS\u2019s SCT allows you to automate much of the schema conversion when you\u2019re moving data from a non-AWS database to an AWS data service. And for the schema, it can\u2019t automatically convert, SCT will produce a report of where the schema still needs to be mapped.<\/p>\n BigQuery Migration Service provides a set of features designed to facilitate data warehouse migrations to BigQuery. It is built for tasks like assessing and transferring data and SQL script conversion from on-premises systems to BigQuery.<\/p>\n Snowflake\u2019s Bulk Data Loaders are built-in utilities and APIs that help you load large-scale data into Snowflake from external storage, primarily cloud storage like AWS S3, Google Cloud Storage, and Azure Blob Storage. With Snowflake\u2019s loaders, you can move large datasets into Snowflake with minimal manual intervention.<\/p>\n The Azure Data Box is a physical device used to transfer large volumes of data to Azure<\/a>. It is useful when data transfer via network is not feasible due to bandwidth constraints, time sensitivity, or security concerns.<\/p>\n While all the tools, platforms, and providers discussed so far are effective in their own way, they can\u2019t replace a trusted technology partner.<\/p>\n To see the power of a partner, here\u2019s a short case study of how Intellias assisted one of our customers with a phased migration from Oracle to Snowflake.<\/p>\n Challenge<\/strong>: Solution<\/strong>: Business Impact<\/strong>:<\/p>\n Data warehouse migration is complex, but it\u2019s a worthwhile process that can modernize your organization\u2019s approach to data management<\/a>. With a smart strategy<\/a>, the right tools, and the right partners, you can complete a successful migration that meets your current needs and future goals.<\/p>\n\n
What to know about each cloud data warehouse<\/h2>\n
Amazon Redshift<\/h3>\n
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Google BigQuery<\/h3>\n
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Snowflake<\/h3>\n
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Microsoft Azure Synapse Analytics \/ Microsoft Fabric<\/h3>\n
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A note on Intellias\u2019 cloud-agnostic approach<\/h3>\n
Plan your data warehouse migration strategy<\/h2>\n
Define success<\/h3>\n
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Create a realistic budget<\/h3>\n
Get the right people in the room<\/h3>\n
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Types of data warehouse migration<\/h2>\n
Environment-Based Migrations<\/h3>\n
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Architecture-Based Migrations<\/h3>\n
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13 steps for a successful data warehouse migration<\/h2>\n
Pre-migration preparation<\/h3>\n
1. Define clear objectives and requirements<\/h4>\n
2. Assemble your team<\/h4>\n
3. Audit and assess your data and its quality<\/h4>\n
4. Document your findings and make necessary adjustments<\/h4>\n
5. Plan for schema mapping and data transformation<\/h4>\n
Finalize migration strategy and execute<\/h3>\n
6. Determine your migration approach<\/h4>\n
7. Set up the migration infrastructure and tools<\/h4>\n
8. Run a pilot<\/h4>\n
9. Execute the migration<\/h4>\n
Post-migration testing, training, optimization, and monitoring<\/h3>\n
10. Test and validate<\/h4>\n
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11. Optimize and finalize<\/h4>\n
12. Conduct end-user training<\/h4>\n
13. Monitor and continuously optimize<\/h4>\n
5 best practices for data warehouse migration<\/h2>\n
1. Be clear on your needs and wants<\/h3>\n
2. Know your users and your dependencies<\/h3>\n
3. Clean as you go<\/h3>\n
4. Double-check security protocols<\/h3>\n
5. Validate twice, migrate once<\/h3>\n
8 challenges of data warehouse migration<\/h2>\n
![\"8](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2024\/10\/body-im-1-Data-warehouse-migration.png\")
<\/p>\n1. Data compatibility and schema conversion<\/h3>\n
2. Data quality and integrity<\/h3>\n
3. Downtime and business continuity<\/h3>\n
4. Query performance optimization<\/h3>\n
5. Data security and compliance<\/h3>\n
6. Existing systems and applications integrations<\/h3>\n
7. Cost management<\/h3>\n
8. User adoption and training<\/h3>\n
4 data warehouse migration tools<\/h2>\n
1. AWS Schema Conversion Tool<\/a> (SCT)<\/h3>\n
2. BigQuery Migration Service<\/a><\/h3>\n
3. Snowflake\u2019s Native Bulk Data Loaders<\/a><\/h3>\n
4. Azure Data Box<\/a><\/h3>\n
How a private aviation company migrated to the cloud with Intellias<\/h2>\n
\nA leading private aviation company with a fleet of over 350 aircraft faced high costs due to its technology choice (Oracle) and inefficient data management practices. This led to operational and budgetary challenges that limited their ability to effectively manage data across finances, marketing, customer relationships, and more.<\/p>\n
\nIntellias helped develop a Snowflake-based data warehouse and created a gradual migration plan. Key components of the new solution included:<\/p>\n\n
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Conclusion<\/h2>\n
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