Amazon Aurora customers

Implementing and scaling hundreds of on-premises instances across the database fleet consumed all database team member resources for many weeks and was a relatively low value-add task for database administrators. In the Oracle world, a seemingly simple change such as scaling from a medium to a large database instance required provisioning hardware, standing up primary and standby databases, and managing failover during transitions, which could take a full day for each instance. Not to mention the fact that we were using specialized hardware that had to be ordered months in advance. After migrating to Amazon Aurora, provisioning additional capacity is achieved through a few simple mouse clicks or API calls.

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Prepping and getting a database cluster up and running took three weeks for installing, confirming network, testing for latency. On Amazon Aurora we’re now able to effectively do it in a day.

The hotel industry is rapidly changing, as more customers expect the ease and convenience of mobile computing. Moving to AWS brings our organization to the forefront of innovation and allows us to give our guests fast, reliable and secure data processing so they can organize their trips, change their reservations, and book their stay with us

Our Unity Next Peer Quality Control (QC) solution provides labs with instant, centralized access to peer reporting through an intuitive platform. By simplifying access to QC insights, we empower labs to refine their processes, mitigate errors, ensure regulatory compliance, and prioritize patient safety with confidence. To address key challenges—driving product adoption, educating users on features, and supporting multiple languages— Bio-Rad enables customers to engage with a chatbot that references product and user manuals to deliver answers directly from these documents, complete with feature-specific image references. We wanted to build a generative AI solution aimed at enhancing our support services and speeding up product feature education and adoption. We turned to AWS and built our Retrieval-Augmented Generation (RAG) solution leveraging Amazon Aurora PostgreSQL-Compatible Edition with the pgvector extension, integrated with Amazon Bedrock using the Claude V3 Haiku LLM, Amazon Titan Embeddings text models, and AWS Translate to facilitate multi-language support for 14 different languages in Unity Next Peer QC. Unlike traditional support methods, our chatbot leverages product and user manuals to deliver precise answers, complemented by feature-specific visual references. This approach reduces reliance on support teams while accelerating user education. With this innovation, we see a 20% reduction in customer call volume and improved feature adoption, driving better outcomes for labs worldwide.

The idea of having a company like Amazon Web Services stand behind an open source product and take care of important attributes like high availability, scaling, and overall data management is huge. Amazon Aurora provides the open source database management layer we’ve been looking for.

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Amazon Aurora PostgreSQL is helping us solve performance and scalability challenges to provide deeper and faster data analysis to our customers. In addition, the built-in security, durability, and high availability capabilities of Aurora PostgreSQL help to simplify and automate most of our DBA requirements, lowering our costs while increasing reliability.

Amazon Aurora has become the database of choice for all our MySQL workloads. Our True Cost Engine delivers cost efficiency to customers, using a new predictive model based on petabytes of customer cost optimization data. To support this kind of analysis, we need a database that is performant, scalable, cost-effective, and easy to maintain and tune. Aurora solves our biggest issues with managing large, production databases with strict SLAs. Now that PostgreSQL is supported, we expect all our PostgreSQL workloads to move to Aurora as well.

After DriveWealth successfully migrated the production workload from CockroachDB to Aurora PostgreSQL, we improved our read/write throughput up to 5X, enhanced data consistency, and achieved an 80% cost reduction. We were impressed with the AWS team’s ability to resolve our challenges from working with previous vendors – they earned our immediate trust by recommending Aurora.

Aurora PostgreSQL is instrumental for Decisiv as we upgraded our infrastructure to support the growing scale, performance and reliability requirements that our customers expect. We've consolidated multiple SQL Server instances into a single database that boasts higher performance at a better cost. Thanks to Aurora, our migration from SQL Server to PostgreSQL went more smoothly than we anticipated, and we are now well-positioned to rapidly scale.

At Emma, we’re focused on thoughtful product design and friendly expert services to help marketers do their very best work. We manage several multi-terabyte OLTP databases that process nearly 300 million transactions per day. Aurora PostgreSQL provides us with a highly performant, secure, and more scalable database cluster than we are able to affordably create in a traditional datacenter. Aurora PostgreSQL also allows us to be much more responsive when meeting expected business growth demands for the foreseeable future, all while reducing the time needed to manage, maintain, and scale the database systems.

With Amazon Aurora, we were able to bring Detection On Demand to the market in a matter of months thanks to its serverless architecture and fully managed database services.

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Compared to commercial databases, we observed similar or better performance at the 90th percentile. But at the 99th percentile, we saw even better response times.

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The shift out of Oracle will save us an estimated $1 million over the next three years in licensing fees—funds that Globe Telecom can use for new digital transformation projects. Performance has been solid—exceeding the latency target of 40 milliseconds.

From raw GPS points, INRIX generates large-scale vehicle movement data and ingests the data into sharded Amazon RDS for PostgreSQL instances. We are hitting the storage and performance limits per shard and looking for a more scalable solution. With Amazon Aurora’s compatibility with PostgreSQL, we’ve seen three times performance improvements in our benchmarks. We love Amazon Aurora’s ability to scale storage independently of computing resources at better price points.

As our customers started demanding faster onboarding of our chatbot, we wanted our chatbot solution to offer an easy setup, provide a personalized experience (based on customer specific data and intent), and automate repetitive tasks better. The integration of Retrieval-Augmented Generation (RAG) capabilities using Amazon Aurora PostgreSQL with the pgvector extension and LLMs available in Amazon Bedrock has empowered our chatbots to deliver natural language responses to out-of-domain inquiries, enhanced dialogue management, and reduced our manual efforts. With RAG, we can retrieve data from outside the LLM, for example from the website, knowledge base, or help documentation, and augment the prompts by adding the relevant retrieved data in context. RAG capabilities have made our chatbot better by allowing the system to provide natural language answers to questions that aren’t in or a variation of the intent list. Consequently, we have observed a 20% improvement in customer onboarding times.

Thanks to AWS and Amazon Aurora PostgreSQL, our company has been able to build an infrastructure that scales to meet our customers’ demands. We found that Aurora PostgreSQL is a drop-in replacement for Amazon RDS PostgreSQL, with a few very important improvements: write throughput and automatically-expanding storage. We migrated 700+ instances of Microsoft SQL Server, and LOVE the simplicity of management that Aurora PostgreSQL provides. Gone are the days of dealing with tuning and tweaking configuration files for optimal performance.

In our testing of Amazon Aurora PostgreSQL in the preview, we have seen very good performance upwards of 7-11 times that of RDS PostgreSQL, for both write and read/write workloads. We are also excited about the expected scalability and reliability, giving us great confidence that Amazon Aurora PostgreSQL will meet our requirements as we move some of our core database workloads to AWS.

We've gotten so many benefits from migrating our database from Oracle to Amazon Aurora such as high availability and easier scaling for reads. All of these benefits come out of the box and at a lower cost and with far less licensing complexity. For data migration, we used the AWS Database Migration Service (DMS). It only took 24 hours to migrate 1.25 Terabytes of data with our first attempt. We worked with the AWS team who helped us experiment with parallelization and organizing the migration by breaking it down to multiple tasks to get the final production data migration down to four and a half hours!

We are enhancing our products by incorporating new heavily structured data such as market consensus estimates, products relationships, and supply chain information, to name a few. Many of our analytics apps have to access this data in real-time and maintain point-in-time sensitivity, which requires a fast performing and heavily relational database, resilient to failures, and distributed across multiple AZ’s. In our testing, Aurora PostgreSQL has performed significantly faster than standard PostgreSQL, and has shown high compatibility with standard PostgreSQL. Given that we already have a deep usage of Amazon Redshift, and we are used to PostgreSQL interfaces, Aurora PostgreSQL will perfectly fit into our system.

We moved from SQL Server to using Amazon Aurora PostgreSQL as an Enterprise Data Store (EDS) and have continued to expand its reach within the business. Building our EDS in Aurora PostgreSQL allowed us to reduce complexity through automation and consolidation, while producing more accurate and stable reports for business users. It also allowed us to substantially reduce our licensing costs as Aurora PostgreSQL is an open-source solution. Finally, the high availability and disaster recovery capabilities of Aurora PostgreSQL help us to deliver on the SLAs that our business users expect. The ability to retrieve data at a high speed for developing ad-hoc analysis for near real-time issues has increased our speed to customer, vastly. We can now identify valuable insights over larger data sets, instead of selected sampling, utilizing Aurora PostgreSQL. We have been able to reduce our total number of reports from 3,000 to 1,000, decrease time spent managing the legacy reporting platform by 30 percent, and, most importantly, resource time spent on manual reporting historically is now being utilized to develop valuable skills in other areas that provide higher value for career development.

Our team has enjoyed the scalability benefits of Aurora PostgreSQL, which has allowed us to quickly ingest significant amounts of data without negatively impacting customers. The addition of the Amazon Aurora I/O-Optimized feature has stabilized our daily costs at RocketReach, reducing our total monthly Aurora costs by 60% and allowing our team to focus on business priorities rather than cost management efforts. We are now able to reap the scalability benefits of Aurora without having to worry about cost increases as our traffic scales. Aurora I/O-Optimized was a critical addition for our team at RocketReach, not only did it allow us to leverage the advantages of Aurora more economically, it also helped prevent unplanned work across our teams. We will absolutely continue to leverage Aurora I/O-Optimized when the use case aligns with its capabilities. At RocketReach, we have many workloads that require substantial amounts of data to be processed efficiently while maintaining performance for our customers. For these use cases, Aurora I/O-Optimized allows us to balance cost and performance, while rapidly delivering value to our customers.

Our service is 24/7 and our transactional database is highly critical such that any unavailability will result in service downtime. We wanted to upgrade our PostgreSQL database to Aurora, but the traditional upgrade approach required a two-step process, with each step resulting in a multi-hour outage - far too much impact to our business and customers. Looking to the unconventional, we explored using AWS DMS for the upgrade. After completing a very positive POC with outstanding support from AWS DMS team, we decided to go ahead with DMS and were able to skip the intermediate PostgreSQL versions and migrate directly to Aurora. DMS was stress-free and reduced our upgrade downtime to minutes. Post-upgrade, Aurora has proven to have better availability, performance, and scalability which gives us the comfort that we can continue to support Shippo’s rapid growth.

We are an internet-only bank based in Japan. Our databases are critical to our ability to provide excellent personal banking and investment services. Since we migrated from Oracle RAC to Amazon Aurora, we have cut our database management costs by 83% while also getting 50% better performance. We've benefitted from improved speed, availability and scalability.

In our performance testing of Amazon Aurora’s PostgreSQL compatibility, we found that the performance was three times better than standard PostgreSQL. Our testing also showed that Amazon Aurora is fully compatible with PostgreSQL 9.6, and we believe customers will be able to move large enterprise workloads from on-premises commercial databases to Amazon Aurora because of its high performance, high availability, and PostgreSQL compatibility. SRA OSS will add support for Amazon Aurora to the next version of pgpool-II, which provides clustering management middleware for PostgreSQL.

Tenable relies on AWS to provide the flexibility and scalability needed to run its large-scale Tenable.io vulnerability management platform. Thanks to Aurora, Tenable can more easily handle large and complex unscheduled queries from customers in seconds without breaking the bank, unlike with standard PostgreSQL instances. Aurora’s cost model for IO and storage means Tenable’s usage and costs are correlated. Plus, what previously took 10+ minutes on a similarly sized RDS instance takes a few seconds on Aurora, and in cybersecurity, every second counts. Tenable uses Aurora for workloads with large-scale unpredictable queries, and ultimately, Aurora enables Tenable to better serve our customers.

We are an early adopter of Amazon Aurora PostgreSQL and used the AWS Database Migration Service to transition TIBCO Cloud Live Apps to Amazon Aurora seamlessly, while it was in production, without our customers noticing any interruption in our service. Amazon Aurora's reliability, security, and fast failover will continue to help us scale Live Apps, giving customers constant access to our service so they can build and run apps quickly and with high availability.

We applaud AWS for providing the extended support for Amazon Aurora and Amazon RDS. AWS is a company that listens to the voice of user companies and makes them come true. This is one of the biggest reasons we use AWS. We want to continue to strengthen our partnership for mutual  growth and development.

We moved our Oracle and SQL databases to Amazon Aurora to improve the speed, latency, and processing times of our databases. The databases migration to Aurora PostgreSQL took less time and resources than we anticipated. Verisk 3E has compliance and performance requirements from our customers to run 3E Insight in various regions globally. Thanks to Amazon Aurora, we can now deliver a highly responsive, scalable, and highly available intelligent compliance solution to our worldwide customers. The migration gave us a significant cost reduction and improved our ability to deliver value to our customers.

Verizon is helping our customers build a better, more connected life. As part of this journey, we are undergoing a major transformation in our database management approach, moving away from expensive, legacy commercial database solutions to more efficient and cost-effective options. Testing of Amazon Aurora PostgreSQL showed better performance over standard PostgreSQL residing on Amazon EC2 instances, and the AWS Database Migration Service and Schema Conversion Tool were found effective at identifying areas for data-conversion that required special attention during migration.

Beyond defraying the costs, the move to AWS makes the tedious on-premises jobs we worry about go away. We look forward to using the capabilities of Amazon Aurora to access data more efficiently.

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To help our customers reduce corporate travel expenses, our platform needs to find rides quickly and accelerate the budgeting, payment, and reporting processes. Since migrating our Oracle database to Amazon Aurora, our user validation process has become 60 percent faster, reporting time per user has dropped 75 percent, and the payment process is 70 percent faster. We’re clearly seeing the results in our user growth numbers and user satisfaction ratings of our application.

Xata has customers that require tenant isolation and want to build AI-driven applications that deliver low latency, highly relevant vector similarity searches to their users. In one particular case, Xata migrated a customer from Pinecone to using Amazon Aurora PostgreSQL-Compatible Edition with pgvector and realized a 65% cost reduction while meeting our query latency targets and providing additional functionality by being able to store the metadata in the same tables as the vector data. This resulted in a significant simplification of their application architecture.

Amazon Aurora allowed me to do something innovative. We didn’t have to wait for infrastructure to be procured and provisioned, and we didn’t have to free up DBA’s.

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It’s a very rapid process from “let’s do it,” to the proof of concept, to getting it done on Amazon Aurora. We have one DBA working on it and another person helping him, and we expect to have a production workload running at scale in under two months.

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Aurora has provided operational value, speed to market, cost, and ultimately customer value. AWS constantly provides us with new innovation that you can't build internally...EPSi has done a great job using technologies like AWS to meet clients where they are and take that next step as a thought leader... The only way we can do that is with robust back-end and analytics and performance that can only be built on AWS to meet those demands.

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Amazon Aurora's fast failover capabilities and storage durability have minimized technical obstacles for our online gaming service. After launching Romancing SaGa Re;univerSe, which achieved more than 10M downloads in the first 3 weeks, Aurora's high durability brought us continuous and stable service operation without any maintenance emergencies. As the backend database for our gaming service, Aurora has provided invaluable reliability and stability.

Amazon Aurora was able to satisfy all of our scale requirements with no degradation in performance. With Alfresco on Amazon Aurora we scaled to 1 billion documents with a throughput of 3 million per hour, which is 10 times faster than our MySQL environment. It just works!

ASU continues to leverage the AWS cloud as a means to improve our agility and capacity in meeting our customer needs. One such AWS service we’re bullish on is Amazon Aurora. We’re moving much of our on premise database workload, currently MySQL, Sybase, Oracle, and Microsoft SQL Server to Aurora or RDS. Aurora also plays an integral role in our data lake strategy, along with Redshift and S3, as we build out our new advanced analytic platform. Data behind our ASU Alexa skill and ASU mobile app benefits from Amazon Aurora's scalability and high performance as enormous concurrent student users during the school year creates new peak loads each season. And, we see Amazon Aurora Serverless as a next step in our cloud maturity to help us improve development agility while reducing costs on infrequently used systems, to further optimize our overall infrastructure operations.

Our Account Control Management (ACM) application is a central component for many Autodesk applications, and usage has grown this year. ACM requires a high performance database with low-lag replication, automatic scaling, and MySQL compatibility. Amazon Aurora allowed us to increase our database connections and greatly improve our scalability, response times, and CPU utilization. Our customers’ experience improved in a short time so we’re looking into Aurora for additional applications.

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To support the transition from DVD sales to our Beachbody on Demand streaming service, we needed a cloud service that provides agility, scalability, and high availability. Amazon Aurora provides the foundation for our digital services, supporting over a million users every month with peaks of over 250,000 users in a single day. Aurora also makes live events possible, as it allows us to scale up quickly for popular live workouts and then scale down, so we keep our costs low while our customers enjoy fast, reliable service.

At Capital One, our cloud-first approach led us to start testing the preview of Amazon Aurora’s PostgreSQL compatibility in November 2016. We anticipated performance benefits, as well as the high availability and fast failover capabilities ideal for serving our customers. In our testing during the preview, we’ve been impressed with the performance and high availability offered by Amazon Aurora.

Our Learning Management System (LMS) is central to the student experience at Cal Poly, and we have high standards for performance, scalability and high availability. Amazon Aurora meets our high standards. Given LMS usage patterns, with peaks during the first week of class, mid-terms, and finals, we expect to economize by paying only for the resources we need when we need them and eliminating the need for on-premises hardware. We also capitalize on the flexibility of the AWS environment that allows us to automatically shut down development and test instances when not in use, and spin them up quickly when needed.

Leveraging serverless computing is key to our system architecture strategy at CB Insights as it allows us to handle bursts in computation needs in an effective and cost efficient manner. We are excited about the opportunity to bring the serverless model to databases through Amazon Aurora Serverless. Through the preview program we have had early access to Aurora Serverless and it has confirmed the applicability for unpredictable data access scenarios. It’s also a good option for development/staging databases, which can be shut down when not in use to achieve additional cost savings.

At Choice Hotels, we process millions of requests every day for guests looking to stay at our properties. Transaction volume has grown over 300 percent in the last three years, and sets new daily records every summer. To accommodate this growth, we undertook a significant challenge and developed the first new global reservation system from a hotel company in over 30 years, and built it on AWS. Leveraging Amazon Aurora and other AWS services allowed us to both exceed our performance requirements and lower our costs.

The launch of Amazon Aurora Serverless has been highly anticipated by the market. It complements several of our offerings, including Cognizant Adaptive Data Foundation, a solution which enables the creation of cloud-based, holistic data strategies requiring insights, scale and speed. Amazon Aurora Serverless will enhance products like Cognizant Lab Insights, an intelligent, connected lab performance management solution, because a serverless database architecture supports the intermittent data usage requirements of lab environments and provides better flexibility, scalability, and cost-effectiveness.

We chose Amazon Aurora for its scalability, flexibility, and ease of use, which enable our engineers to focus their time on customer-facing features rather than routine database maintenance and administration. Our online registration system has irregular usage patterns, with traffic spiking during registration season and flash sales. After trying Aurora Serverless, we are confident it will provide the quick and seamless scaling we need.

We migrated from Microsoft SQL Server to Amazon Aurora to take advantage of Aurora’s scaling capabilities, which couldn't be better suited to meet the spikes in traffic we get during the football season while also allowing us to minimize costs. We get all of the capacity we need, but don't pointlessly run at high capacity on non-game days where our needs are much, much smaller. We also pair Aurora with Elastic Beanstalk, which lets us mirror that optimization of performance and cost on the server side, and has helped us become more productive, spending less time on DevOps and more time writing code and creating value for the league.

Amazon Aurora made my life easier. I was dealing with three possible system degradation alerts each night after hours, up to 15 or 20 some nights, that all had to be investigated. Now I am typically alerted once or twice a week.

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Our data-based services are often not used at full capacity, resulting in high database running costs. Amazon Aurora Serverless will allow NTT DOCOMO to reduce operational costs by matching capacity to actual demand. Because developers can use Amazon Aurora Serverless just like a traditional MySQL database, it will be easy to adopt in our service infrastructure.

Dow Jones’ first legacy on-prem database to Aurora migration was a high-profile workload that plays a critical role in engaging and retaining our customers. We were an early adopter of Aurora and the AWS Database Migration Service, which we’re utilizing to move data from our legacy on-prem environment to Aurora. The migration allowed us to replace a legacy platform with performance challenges that required 400k in DBA staff to manage, with $1M in licensing costs annually, to a cloud-based, highly scalable and resilient solution. It uses a 1TB Aurora cluster with 200 transactions per second, along with replication to another region for high availability and disaster recovery. By moving much of the operational overhead to AWS, and eliminating the need to manage storage completely, Aurora frees up funds for innovation.

AWS gave us the opportunity to build an enterprise-class, cost-effective, open source-based database strategy with Amazon Aurora. We achieved the database freedom our team needs to deliver specialized insights and relevant connections to our customers.

We were very excited after testing our workloads on the initial Amazon Aurora beta because of its performance potential. When we moved our production systems from Amazon RDS to Aurora, we found that we were able to double the number of customers per database cluster while increasing performance at the same time. Aurora’s flexibility with adding read slaves on demand as well as automatically managing all underlying storage lets our engineers focus more on customer experience and less on how to speed up data access layers.

At FirstCry, a smooth customer experience is key for us to build trust and strong relationships with our customers. Using Amazon Aurora has helped us increase our agility, scalability, and resilience as we scale to meet our customers’ needs. As our application is I/O-intensive, we were looking to eliminate variability in our I/O spend. With the adoption of the Aurora I/O-Optimized feature, we have realized 50% cost savings per month, achieving price predictability and improved price performance for our I/O heavy workloads. Based on the great experience we have had using the fully managed service, we are migrating more of our self-managed databases to Aurora. We have also made both Aurora MySQL- and PostgreSQL-compatible editions core pillars of our data strategy, along with Amazon Redshift and Amazon S3. We look to further enhance our Analytics and AI/ML capabilities in the organization utilizing these services. We are very pleased with the performance that Aurora has delivered and it will continue to be a trusted service we utilize as we scale our partnership with AWS.

Amazon Aurora has immediately become a key component to meeting FirstFuel's uptime and disaster recovery Service Level Agreements (SLAs) with some of the world's most demanding enterprise clients. Key functionality such as restore to point in time, reader nodes, cross-region replication, and automatic node failover have dramatically reduced or eliminated our data loss and downtime risks. Aurora has also reduced the time we spend on database management, leading to real productivity improvements.

Our first tests of Aurora were difficult to believe because the performance increase was substantial. Aurora has greatly improved the responsiveness of our web site and our data warehouse analytics. After migrating to Aurora, we were able to rapidly add more read slaves to our site as needed, while maintaining great performance. Overall the performance and throughput have been excellent and Aurora's performance metrics are very detailed and helpful, simplifying database management and scalability. Aurora made our migration from traditional colocation to AWS easier because the storage was fully managed and replication was extremely fast.

Amazon Aurora improves the backend database performance of our online gaming services and reduces the operational cost. For instance, Update query performance runs 8 times faster and Select query performance runs about 10 times faster on Princess Punt Sweets, a long-running gaming title from 2012. In addition, backup time was shortened by 80%, and we achieved a 40% reduction in our AWS cost by reducing the number of instances. After the migration, Aurora continues to achieve high reliability and stability as our backend database.

We began using AWS in April 2024 and chose Aurora, a MySQL-compatible and fully managed database service, to support our core business - food delivery platform. From the initial deployment, we opted for Graviton2 (R6g) instances due to competitive pricing, 12% lower compared to Intel x86 instances. As we expanded to more countries, we started to hit the limits of our current r6g instance class. While there was always an option to scale vertically, this would almost double costs. The launch of Graviton3 (R7g) instances offered a solution. Switching to Graviton3 (R7g) instances brought significant improvements: 22% lower CPU utilization, 27% reduced latency, and fewer reader instances(5 to 4). This transition allowed us to accommodate our growing business needs while optimizing costs and performance.

Intuit recently migrated their commerce platform to Amazon Aurora MySQL to support increasing global demand. All direct purchases of Intuit’s software will go through the Intuit’s Commerce Platform running on Aurora, with TurboTax already live to meet traffic demands during tax season. A large portion of our workload involves low-latency, read-only access to data. An example is pricing information, which is infrequently updated but needs to be readily available for reads from coast to coast. Aurora Global Database, with sub-second global replication, enables us to address this business requirement without performance or latency constraints. As a financial services company, we also care deeply about business continuity even in the face of large-scale events. Aurora Global Database allows us to maintain a strong disaster recovery posture by distributing data across AWS regions with failover typically taking under a minute to complete.

We project that the size and throughput requirements of our relational databases will more than double year-over-year for the next several years. We need a scaling strategy that delivers reliable performance with growth, yet is simple to operate. Amazon Aurora’s ability to auto-grow database sizes all the way up to 64 TB, as well as provide consistent throughput and latency, is an extremely promising approach to reducing our operational risk.

We're evaluating migrating our relational datastore from SQL Server to Aurora. The most important thing to us is the flexibility... whereas with SQL Server... it's very cumbersome. The goal is to introduce a modern relational data store that allows us to scale at will as our customer onboard.

We were fortunate to have had the opportunity to partner with AWS during the development/rollout of the DMS migration service to migrate from Microsoft SQL Server to Aurora MySQL. In performing this migration, Jobvite realized cost reduction of 40%, improved responsiveness to customer requests by up to 40%, eliminated monthly database maintenance that took the application down for 3 hours, and reduced refresh time from 20 hours to 2 hours. All of these benefits have allowed Jobvite to invest efforts into other aspects of the business to improve customer satisfaction.

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We migrated all systems of the Mainichi Shimbun News website to AWS, and celebrated the first anniversary in December 2016. We use Amazon Aurora for the core database of our system, and it shows excellent performance. The database has been running with 100% availability in the last 16 months, and we spent almost no time on maintenance and administration. By having such a wonderful database service, engineers could focus on developing better applications, allowing us to provide better service to our customers. As a result, annual page views on our website increased by 20% from the previous year.

At Mitel, we’re reinventing our products using cloud-based, microservice applications to give our customers more specialized and innovative ways to communicate and collaborate. We’re using Amazon Aurora at the core of our Cloudlink platform and our Officelink product. We’re excited about the new Aurora Serverless offering, which allows us to further focus on delivering applications at scale without having to think about infrastructure.

We were able to test Aurora’s parallel query feature and the performance gains were very good. To be specific, for queries doing full table scan or fetching fat indexes with billions of rows, we noticed the query time reduced from 32 minutes to 3 minutes. We were able to reduce the instance type from r3.8xlarge to r3.2xlarge. For this use-case, parallel query was a great win for us.

We are excited by the flexibility that Amazon Aurora Serverless offers in our current managed WordPress hosting stack, and the new serverless WordPress application hosting model that it enables us to pursue. Many of our large-scale WordPress sites have unpredictable database usage. Amazon Aurora Serverless gives us the ability to lower customer costs for these fluctuating workloads and free internal resources by eliminating the need for manual or monitored database resizing. Ultimately this is about saving time, money, and serving our customers better, and Amazon Aurora Serverless will help us do that.

The cloud plays a major role in Pearson’s transformation into a digital education company. Using AWS, the IT team created a platform product teams use to create next-generation apps for education. AWS enables our teams to release features and update them globally at any time, lower costs to internal developer partners, transforming work with security teams, and delivering value to students. Amazon Aurora has been a core part of our AWS infrastructure to ensure we have best performance, high availability, and scalability with our increasing users.

Adopting Amazon Aurora is a game-changer because we offload management, security, and backup to AWS. When we managed our own databases, it could take days or even weeks to spin up a new instance, replicate data, load test, patch, and so on. With Amazon Aurora, it takes a few clicks or a few lines of script and a new instance is running in minutes. If you multiply those productivity gains across the organization, it’s huge.

If you work with databases on AWS, there is no reason not to choose Amazon Aurora. At peak periods the membership system is accessed via API more than 250 times per second, but there have been no issues at all even under this kind of processing load. Also, because it is a fully managed service, we can focus our resources on service development.

Our business requires real-time resource expansion without downtime to handle sudden traffic spikes. Traditional scaling methods often necessitate minutes of downtime, which is unsuitable for our needs. To address this challenge, we adopted Amazon Aurora Serverless database to support our short drama platform, ShortMax, including multiple core modules such as payment and user management. Aurora Serverless can scale within seconds, reducing business interruption from 10+ minutes when using traditional scaling methods to zero seconds during the expansion period. It also supports scaling capacity down to zero Aurora Capacity Units (ACUs) with no charges during periods of database inactivity. This solution allows us to efficiently manage our database resources while maintaining maximum business continuity for our 5,500,000 daily active users.

With support for 325+ data formats and limitless transformations, our products’ automated daily testing scenarios are demanding: 15,000 x 4 operating systems x 3 products, running 24/7. Add our developers and QA team hitting the servers, and they were tipping over. We migrated our 200 tables and 10 million rows to Aurora with the click of a button. Now we can build better, faster — which means we can deliver a higher quality product. We have no more maintenance headaches, Aurora is scalable without hassles, and we’re saving 40% in annual costs.

Amazon Aurora has become the default choice for any kind of relational data for us because of its ease of use.

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Moving to Amazon Aurora allows us to reclaim 15% of our operations team’s time.

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Sumo Logic processes hundreds of petabytes of data every day. Any disruption, delay, or downtime becomes orders of magnitudes worse given that customers use our solution to deal with similar issues of their own. We migrated to Amazon Aurora for a fully-managed, highly-available, durable, MySQL-compatible, VPC-enabled database, and were able to migrate about a hundred database instances with hundreds of databases within them, with zero downtime from our customers’ perspective. Our systems are now more robust and future proof, not just in terms of technology but also in terms of database management processes and readiness for future migrations.

The migration of Sengoku Enbu -KIZNA- from our on-premises data center to AWS was completed very successfully in February 2019. We are now using several Amazon Aurora clusters (MySQL-compatible edition), with our application servers running on Amazon EC2 instances in the same availability zone for low latency. During gaming events, when we have access peaks three times a day, we use auto-scaling and EC2 Spot for our application servers, and have found that both Aurora and EC2 handle these peaks without any issues whatsoever.

At SysAid, we describe Aurora as the solution to everything! We were looking for a database that could withstand the needs of our established customer base, who need all of their historical IT data at their fingertips. Aurora allowed us to not compromise the level of service that's expected of us. Furthermore, we were looking for a stable and scalable cluster technology and were amazed by how quickly and easily Aurora performed intensive ALTER TABLE operations. We're now free to move SysAid forward without worrying about either performance or stability.

We chose Amazon Aurora because we wanted a data store to support availability cross-region, so in the instance of a downed datacenter our customers can still authenticate.

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We went from being a bottleneck in our customer’s lifecycle to being invisible and even an enhancement in their process due to Aurora.

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TalentBin by Monster made the move to Aurora so as to reduce operational over-head and management of MySQL, which in turn allowed our development team to focus on innovation. Aurora offered significantly faster replication, providing larger write operations that wouldn't impact any downstream applications. Plus, Aurora’s tools eliminated the need to allocate excessive storage to account for usage and growth demands, which adds even more value and savings. Aurora made it possible for our team to consolidate various databases, reducing our database instance count by roughly 40%. Other gains were earned through automatic snapshots and point-in-time restoration, providing true operational improvements. All of these features made migrating to Aurora an easy decision for us.

We tested Aurora’s parallel query feature with analytics applications within our ClearIP software product hosted in AWS. We’ve been excited to find that larger, more intensive queries perform up to 20x faster with Parallel Query turned on.

We selected Amazon Aurora as our core database engine for its unparalleled performance and availability at global scale. Tuya currently manages billions of real-time online devices and can keep cloud message processing response times under 10 milliseconds. However, billions online devices provide a challenge. During holidays, there will be peak traffic volume, with tens of millions of devices going online and offline virtually simultaneously. Tuya used Amazon Aurora to construct a data storage solution to solve the main problem of rapid increase in short-term traffic, and to fully utilize resources. Aurora's design, which separates compute and storage and low-latency replication functionality, improves system throughput by enhancing the effect of read-write separation. Aurora provides up to 15 read replicas, setting the groundwork for Tuya's read flexibility development. At the same time, Tuya has integrated Aurora Serverless, which includes seconds-level elastic expansion and contraction, allowing Tuya to handle extremely heavy business traffic smoothly.

At the UN, we operate multiple websites with global reach that require mission-critical reliability and consistent performance. We were able to achieve superb performance even with Amazon Aurora’s smallest database engine. Amazon Aurora’s new user-friendly monitoring interface made it easy to diagnose and address issues. Its performance, reliability and monitoring really shows Amazon Aurora is an enterprise-grade AWS database.

We use Amazon Aurora MySQL-Compatible Edition for our TV operating system to achieve high performance, availability, and scalability. Using AWS Graviton4-based Amazon EC2 R8g instances for Aurora, our testing revealed an over 40% increase in performance and, most impressively over 20% reduction in business latency over Intel predecessors. The combination of Aurora and Graviton4 instances delivers the best performance for our workloads.

Kenvin Zhou, SRE Director - Whale TV

Ragnarok Online, an MMORPG (Massively Multiplayer Online Role-Playing Game) mobile game we launched in the Korea region in March 2018, needs to constantly update game statistics and player status. The burden of massive write operations can reach 25,000 queries per second easily, which exceeds the limit of traditional MySQL databases. Thanks to the Amazon Aurora database, we can accomplish this without changing any of the code, and even keep latency to less than 35 ms to ensure we can continue to expand our user base. Thanks to the high performance and availability that Amazon Aurora provides, Ragnarok Online reached number one in the free and top-grossing mobile game categories in Korea when it launched.

We migrated our MySQL databases to Amazon Aurora in order to handle our incredibly fast growth. Scalability and high availability can be challenging with large MySQL databases. Amazon Aurora allows us to easily scale our reads through near-zero-lag read replicas, even under a very heavy write load. That combined with reliable, out-of-the-box cross-availability zone failover means we favor Aurora for all of our live data serving needs.

Our existing MySQL databases perform millions of transactions per day and we expect them to continue to grow. Amazon Aurora will give us better performance and scalability than MySQL, as well as lower latency read replicas, and we see an opportunity to use Amazon Aurora to improve the latency of our website while also reducing the number of instances required to run it. Best of all, Amazon Aurora’s MySQL-compatibility means that we can use it without making changes to our existing applications.