If you store all your data in legacy systems and on-site servers (that you pay IT staff to maintain), it’s time to migrate to a cloud data warehouse. With all your data in a central repository, you can get a single view of your data, make decisions faster and more accurately, and increase data quality. Migrating to a cloud data warehouse also enables capabilities vital to modern software development – e.g. cloud-native architecture, microservices, and containerization.
Sure, migrate to a cloud data warehouse. Easier said than done. It takes understanding your complex data systems, building new data pipelines, etc. But in the end, you get a more scalable, flexible, and cost-effective solution for managing and analyzing large volumes of data. And there are common steps to overcome challenges and make the migration less messy while you avoid downtime.
We’ve done these migrations before, and here’s what you need to know about moving to a cloud data warehouse quickly, efficiently, and successfully (the first time).
What is a cloud data warehouse?
A cloud data warehouse is a data warehousing solution hosted and operated in the cloud by a cloud service provider. Snowflake, AWS, and Azure are some of the most popular solutions. It serves as a centralized repository for storing, transforming, and analyzing large volumes of structured and unstructured data from many sources.
Traditional, on-premises data warehouses require significant upfront hardware, infrastructure, and maintenance investments. And you have to build out new physical infrastructure whenever you want to scale. A cloud data warehouse immediately plugs you into a flexible, fully managed data solution. That’ll save you a lot of money – e.g. IT headcount, paying for physical infrastructure, electricity, rent, security, and maintenance.
Why should I migrate to a cloud data warehouse?
It’s a good question, and one you want to make sure is answered well. Get all your requirements in order, understand your budget, map your timeline, and do a cost analysis. Once you do, you’ll likely see a big opportunity to save money. So, you should do it if it makes sense for your business, and we think it will.
Otherwise, here are the main reasons CIOs, CTOs, and VPs of Engineering continue investing in the move to a cloud data warehouse solution.
- Scalability: Easily handle large amounts of data and adjust resources as needed. This eliminates the limitations and upfront costs of on-premises infrastructure.
- Flexibility: Dynamically allocate storage and computing resources based on requirements. Scale storage capacity and processing power to match workload demands.
- Rapid deployment: Quickly set up a fully functional data warehouse and avoid time-consuming hardware procurement and installation processes.
- Cost efficiency: Pay only for what you use with a pay-as-you-go model. This optimizes costs and eliminates upfront investments in hardware.
- Performance and speed: Process complex queries and gain real-time insights with distributed computing and parallel processing techniques.
- Data integration: Seamlessly combine data from various sources for unified analysis. You’ll need to leverage connectors, APIs, transformation tools, and ingestion tools to get this right.
- Advanced analytics and machine learning: Utilize built-in capabilities and integrate with popular tools for advanced analytics and AI-driven insights.
- Security and compliance: Benefit from robust security features, encryption, access controls, and adherence to regulations.
- Continuous innovation: Stay up-to-date with the latest features and enhancements without frequent hardware and software upgrades.
What are the popular cloud data warehouse platforms?
From Snowflake to Azure, cloud data warehouses have a wide range of options. Ultimately the best option for your business depends on your requirements, but this is the list we’d start with.
- Snowflake: A cloud-based data warehousing platform that separates compute and storage resources, allowing them to scale independently. Snowflake supports various data formats, has strong support for JSON, and integrates well with both AWS and Azure. It offers pay-as-you-go pricing and handles all aspects of setup and administration.
- Amazon Redshift: An Amazon Web Services (AWS) product that allows users to analyze data using standard SQL and existing Business Intelligence tools. Redshift is fully managed, scalable, secure, and integrates seamlessly with other AWS services.
- Google BigQuery: A Google Cloud product that offers super-fast SQL queries using the processing power of Google's infrastructure. It's serverless, highly scalable, and cost-effective. BigQuery supports real-time analytics with its in-memory BI Engine and machine learning capabilities.
- Microsoft Azure Synapse Analytics: Formerly SQL Data Warehouse, Azure Synapse Analytics is an integrated analytics service that accelerates big data and dynamic data exploration. It gives the ability to query both relational and non-relational data at petabyte-scale. It's also deeply integrated with other services within the Microsoft Azure ecosystem.
- Oracle Autonomous data warehouse: This is a fully autonomous, high-performance, and highly secure data warehouse cloud service that is easy to use and elastic. It uses machine learning to automate administration, tuning, backups, updates, and scaling.
Choosing the right cloud data warehouse for migration is a whole process on its own. Our experts would be happy to talk with you here if you want advice.
What are the main challenges in cloud data migration?
If you’ve ever been involved in any major platform migration, you know how messy it can get. And when it comes to migrating your data to one place from many sources, it gets complicated. Especially if you’ve never been through this specific type of migration. That’s why many data and engineering execs turn to outside consultancies.
They’ve done it before and know how to overcome this long list of challenges in cloud data migration.
- Actual migration of data: Transferring large volumes of data from on-premises databases to a cloud data warehouse can be a complex and time-consuming task. This process requires careful planning to minimize downtime and prevent data loss.
- Data security and privacy: Ensuring the security of sensitive data during the migration process and in the cloud environment is a significant challenge. This is compounded by regulatory compliance requirements, like GDPR or HIPAA, which dictate how certain types of data must be handled.
- Cost management: While cloud data warehouses can be more cost-effective than on-premises solutions, it's still essential to understand the pricing structure to prevent unexpected costs. This can be especially challenging due to the pay-as-you-go and on-demand pricing models used in the cloud.
- Skills gap: There may be a lack of necessary skills within your data team to manage and optimize the use of a cloud data warehouse. This includes understanding how to use the new technology, knowledge about cloud security, and the ability to optimize costs.
- Integration with existing systems: The new cloud data warehouse needs to work seamlessly with the company's existing software and systems. This might involve re-writing applications, implementing new APIs, or even replacing systems that are incompatible with the new cloud environment.
- Change management: Shifting to a cloud data warehouse can cause significant changes in how teams work. Overcoming resistance to change, training staff, and adjusting business processes can all be significant challenges.
- Data governance: Ensuring the quality, availability, integrity, security, and usability of data in a cloud data warehouse can be more difficult than in an on-premises environment. This is especially true when dealing with large volumes of data and when data is sourced from multiple locations.
What are the steps to migrate to a cloud data warehouse?
Migrating to a cloud data warehouse involves moving all your data and workloads from your existing system — whether it’s a traditional on-premises data warehouse, a collection of data marts, or a different cloud provider — to a cloud-based data warehouse.
The process can be complex and time-consuming, but following a systematic approach can simplify the process:
- Planning: Define the goals and objectives of your migration project. Why are you migrating to a cloud data warehouse? Is it to reduce costs, increase flexibility, or access new features? Understanding your objectives helps you choose the right platform and develop a solid migration playbook.
- Assessment: Take stock of your existing data architecture. Understand your data sources, data volume, data quality, and the complexity of your ETL processes and data pipelines. This assessment will give you a clearer picture of the scope of your migration project.
- Select a cloud data warehouse platform: Choose a cloud data warehouse provider that best fits your requirements. We’ll write a detailed article on that soon. Make sure to consider factors such as cost, performance, security, scalability, and compatibility with your existing applications.
- Data cleansing: Use this opportunity to clean your data and address any quality issues. Migrating dirty data to a new platform will only perpetuate existing problems.
Schema conversion: Convert your existing database schema to a format that's compatible with your new cloud data warehouse. Some platforms provide tools to automate this process.
- Data migration: Transfer your data from the existing system to your new cloud data warehouse. Depending on your data volume, this process could take a lot of time and attention. Incremental migration strategies, where you first migrate a small portion of your data to test the process, are beneficial.
- ETL process migration: Your ETL processes — extracting data from source systems, transforming it, and loading it into your data warehouse — may also need to be migrated or redeveloped to work with your new cloud data warehouse.
- Testing: Thoroughly test your new cloud data warehouse to ensure data has been migrated accurately. Make sure that all functions and services are working correctly. Test the performance of the system and validate that it meets the requirements you defined in the planning stage.
- Switch over: Once you're confident that the new system is working correctly, you can switch your operations from the old system to the new one. Depending on your strategy, this could be a phased approach or a big bang cut-over.
- Monitoring and optimization: After migration, continuously monitor the performance of your cloud data warehouse and optimize as necessary. Consider using automated monitoring tools to help with this.
Remember that migrating to a cloud data warehouse is not just a technical project, it involves a high level of change management. Engage all your stakeholders throughout the process, make sure they feel heard, and train your teams on the new technology.
Can I hire a team to migrate to a cloud data warehouse?
You might not have the experience with cloud data warehouses to feel comfortable planning and executing a migration. Most people don't. That’s why Codingscape exists. No need to wait 6-18 months before you start your migration. You can hire us to assemble a team in 4-6 weeks and start planning your migration.
We’ve done it before and we’ll be able to do it fast (from planning to change management) while your engineering resources stay focused on critical business growth. We’re not a software engineer recruiting agency either. You scope out the work with us, and we’ll integrate with your team, technology stack, and partner with you for as long as you need us.
Zappos, Twilio, and Veho are just a few companies that trust us to build software with fully managed data in the cloud.
You can schedule a time to talk with us here. No hassle, no expectations, just answers.
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Cole is Codingscape's Content Marketing Strategist & Copywriter.