TOMIA, a leading provider of transformative connectivity solutions, needed to resolve their performance issues, gain the ability to scale their capacity up or down, and improve their disaster recovery. dbSeer migrated TOMIA’s platform to the AWS cloud, increasing performance with EC2 Instances, using a VPN for security, and utilizing amazon S3 for storage and disaster recovery. Learn more about the process and the results by downloading the White Paper!
Data holds the answers to some of our toughest questions, but it may seem like you have to be a data science or statistics expert to get those answers. If you are an analytics specialist looking to learn more about data science, or even if you are someone who is simply interested in learning more about the topic, then this eBook is for you. Download the eBook to find how-to guides for sampling and distribution, correlation and AB hypothesis testing, and single and multiple linear regression.
dbSeer’s customer, Telarix, wanted to increase their productivity and quality by moving to an environment that is accessible by project owners and can be easily scaled up or down depending on their needs. dbSeer migrated Telarix’s SQL Servers and .NET applications to the AWS cloud using Amazon EC2 for Microsoft Windows. dbSeer successfully completed all planned SQL Server migrations to AWS in two months. Learn more about the process by downloading the White Paper!
dbSeer architected and implemented a consolidated data platform on the AWS cloud for Abt Associates using Amazon S3 and Amazon Redshift. To date, dbSeer has successfully integrated 10 data sources onto the platform, giving access to 110 self-service users and providing a foundation upon which Abt can continue to build. Please download our case study to learn more.
dbSeer demonstrated that by moving Subject7 to Amazon RDS from PostgreSQL and creating a new network infrastructure, scalability can be achieved without downtime for users, all at a fraction of the original cost. Please download our case study to learn more.
dbSeer showcased that by re-architecting client’s data processing engine and leveraging AWS
elastic services and open-source technologies, unlimited scalability can be achieved at a fraction of the cost. Please Download our case study paper to learn more.
The benefits of the cloud, such as elasticity and costs savings, are well understood. What isn’t well understood is the specific pathway required to realize these benefits. Too often, organizations simply copy and move their on-prem solution to AWS. While this “lift and shift” approach indeed translates costs from capital to operating expenses, in most cases, it leaves behind much of the reduced costs and additional benefits. Pure lift and shift enables you to benefit from operational expenses, rather than making large, upfront investments in capital expenditures. However, in order to fully realize all of the possible potential advantages, most organizations will need to reevaluate their architecture. Changes are required in order to fully benefit from the elasticity of AWS, and reduced costs by eliminating unnecessary licensing expenses through shifting to open source services. The structure of paying only for the capacity that you use has the potential to dramatically reduce costs for many organizations. The elasticity provided by AWS is simply impossible to achieve with on-prem computing. Additionally, for mission critical applications, re-architecture and AWS migration can lower costs for redundancy and high availability.
Download our white paper to learn more about AWS Database Migration Pitfalls.
When building out an Analytics platform, organizations need a framework that can evolve and grow as the organization evolves and grows. This framework should help clearly identify business needs and objectives, take advantage of current analytics tools and advances in technology, while facilitating communication among all stakeholders including IT, analysts, and executives. It is critical to follow a clear path that guides the organization through the levels of analytics maturity, including description, diagnostic, predictive, and prescriptive.
Download our white paper to learn more about the four key layers of an effective analytics framework.