Datavail: Migrating and modernizing commercial databases to open-source database engines on AWS
Modernizing commercial databases on AWS helps lowers costs by eliminating punitive licensing contracts and helps improve quality, efficiency, and performance.
Datavail, an AWS Advanced Consulting Partner with AWS Microsoft Workloads Competency, has successfully migrated over 200 database workloads from on premises to AWS. They also help customers modernize their migrated workloads to further reduce costs and increase operational efficiency.
I chatted with Dan Russell and Ali Dawson from Datavail. Dan is the Senior Vice President of Cloud and Strategic Alliances, and Ali is the Director of Strategic Partnerships and Alliances. They explained how their team is helping customers migrate away from commercial databases through their database migration and modernization initiatives.
Q. What challenges do you see customers experiencing as the size of their databases continue to grow?
Our customers manage terabytes of data from multiple sources. As their data (or database) footprint continues to grow, they are increasingly burdened with scalability, reliability, and cost associated with running these database instances. Here are some of the biggest challenges that we’ve heard from our customers:
- Spiraling costs – Existing commercial database applications are aging and always require new capital investments. Moreover, software licensing terms require millions of dollars and long-term commitments.
- Sluggish performance – Existing solutions are unable to handle exponential growth in data volumes.
- Operational overhead and maintenance cost – IT staff spend the majority of their time on low-value administrative tasks, such as maintaining the complex traditional database application.
It is imperative for customers to solve these challenges because they have an adverse impact on application availability and affect the total cost of ownership (TCO).
Q. So, what options do customers have who are facing these challenges?
Broadly speaking, there are two: 1) migrating to managed databases on cloud to reduce operational overhead, and 2) modernizing to open source database engines to save on licensing costs. Let’s elaborate more on each option.
Option 1: Migrating to managed databases. This option is for customers who are experiencing unplanned outages with database systems, particularly during off-business hours when they have limited resources. They may want to migrate those databases to a more scalable and reliable environment. Here, customers do not have the luxury of time to perform heterogeneous migration, which requires time and proper planning. Therefore, we help them migrate their databases to Amazon Relational Database Service (Amazon RDS) for SQL Server. Customers notice the improved database performance after migrating to Amazon RDS SQL Server, resulting in a significant reduction in downtime. Customers can reduce their operating and maintenance costs as well. In our experience, this is the easiest and fastest way to reinvent database operations.
Option 2: Modernizing to open source database engines. With this option, customers can scale workloads to petabytes of data at one-tenth the cost of their commercial databases. AWS offers Amazon Aurora with full MySQL and PostgreSQL compatibility. Customers can choose from various compute, memory, and storage options for their databases based on their workload type.
Q: What is Datavail’s approach or strategy to modernize commercial databases for customers?
Every customer has a unique environment. Our migration strategy is based on customer needs and requirements. But most of the time, we use the ten steps shown in the following illustration to meet our customers’ migration and modernization requirements.
The initial assessment phase is very important, and it is where we identify all the undiscovered marvels of current systems. We perform a comprehensive database assessment to determine what to move and which migration strategy we should use. Migrating to open source databases is no simple feat, but fortunately, AWS provides various services and tools for the schema conversion, code analysis, and data migration, such as AWS Schema Conversation Tool (AWS SCT) and AWS Database Migration Services (AWS DMS). Sometimes, we might need to use manual schema conversion, depending on the complexity of the code in the source database. In certain scenarios, to move databases successfully and without problems, we work on writing custom scripts to move data as needed.
As a next step, our team helps customers develop a plan for migration. We break down our approach into stages. The data migration process often varies, as the structure of the source databases differs. We may need to move the data gradually by taking several steps. Our team collaborates with customers’ database administrators (DBAs) to assess their database cloud operations to determine how they are performing and identify any modifications or updates to achieve optimization after migration. We also help with the mapping of software licenses, taking inventory of dependent applications, and configuring them with the new server arrangement. It helps to ensure that there is no interruption in essential business functions during the migration.
Q: This is a very interesting. Now, can we dive deeper into real-world use cases of database modernization? Do you have compelling case studies you want to share with us?
Yes, sure, we have many case studies. Let’s look at two of them.
For the first case study, we’ll use one of our customers from financial services. As you know, the financial services industry naturally has extremely stringent standards for security and compliance requirements, such as specific authentication standards, storage and data transport method requirements, etc. Besides adhering to strict security and compliance standards, this customer focused on their business and application development and let the cloud provider manage most operational issues. Our customer wanted business continuity, with the cloud provider being responsible for high availability, reliability, and disaster recovery services. So, we worked with the customer to modernize their on-premises databases to Amazon Aurora. We performed the analysis and executed the migration for the customer. The migration to Amazon Aurora met their security and compliance requirements, which focused on PCI compliance, authentication methods, and encryption for data at rest and in transit.
For the second case study, we’ll use a Healthcare Analytics provider customer.. Their system analyzes CT scan data to assess the risk of coronary heart disease. They needed improvements in performance and scalability, as their on-premises data analysis infrastructure had performance limitations. The number of cases they were analyzing each day went up from 125 to 300 and their system couldn’t support the heavy analytics workloads. The customer could not easily replicate data between geographic regions. So we worked with this customer to migrate their on-premises databases to Amazon Aurora. We helped them rightsize their database to the Amazon Aurora instance class that was appropriate for their processing and memory requirements. After the migration to Amazon Aurora, the customer saw a 20–30% improvement in performance compared to their on-premises environment. With this performance improvement, the customer could more than double the number of analysis cases with Amazon Aurora.
You can read more about other customers we helped migrate their databases here:.
- Massachusetts eHealth Collaborative
- How a Health Information Technology Leader Migrated to AWS
- How AWS RDS & Microsoft SQL Server Improved Patient Care
Q: Thanks for sharing these fascinating customer stories. As you work with customers to migrate and modernize their SQL Server workloads to AWS, do you see customers interested in any specific pathway?
Recently, we have been getting a lot of queries from our customers about Babelfish for Aurora PostgreSQL. When customers consider database modernization, the things they usually consider are how much code change will be required, the possibility of disruptive downtime, and the requirement for a lengthy time commitment. AWS designed Babelfish to be compatible with the SQL Server commands most commonly used by customers, so that customers can save a significant amount of time without needing to redesign and recode applications. Babelfish’ capability is enticing for them. Customers can leverage the full potential of Amazon Aurora PostgreSQL, such as flexibility, greater capacity, improved security, better performance, etc., after migration. Based on our experience with database migration and modernization projects, we’ve built modernization approaches and put together a package for our customers who want to use Babelfish for Aurora PostgreSQL to modernize their commercial databases.
Q: Finally, are there any resources where our readers can find more about your expertise, case studies, etc.?
Yes, we have a recorded webinar explaining database modernization and migration in detail. Customers can also look at our success story video as Microsoft workload on AWS partner. In this video, we talked about leading customer innovation through migration and modernization together with AWS leadership team.
Finally, we have Datavail’s AWS Partner Portal, which is a one-stop shop to view and download all resources, such as white papers, service overviews, case studies and more.
About the Microsoft workloads on AWS Partner Spotlight series
Please continue to join us in this blog series as we highlight our AWS Partners’ capabilities in migrating and modernizing Microsoft workloads on AWS. As you do, we hope you’ll also ask the question: “What’s my organization’s plan for moving off SQL Server or Windows Server or whatever technology is keeping us from modernizing to better care for our customers?” Let AWS Partners with their specific niche offerings help you assess how your company can get the most out of cloud.
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