There are a lot of database products on the market. So why did we build HarperDB?
The simple answer is that we were frustrated by the options available to us on the market. We felt that the market was highly fragmented due to the fact that most databases are designed to address specific workloads. Every time we found a strong product for scale, it lacked analytical capability and we found the reverse too often be true as well. Additionally, we know that big data is complex; however, we feel that most database management software products shift that complexity onto the developer instead of handling it internally.
Most companies today potentially have a NoSQL database for handling big data ingestion and transactions, a SQL database product to run the business applications and potentially analytics data lakes like Hadoop or Spark for more advance analytics and data science, and then Middleware and infrastructure tools to tie it all together. That is a lot of licenses and a lot of skill sets to manage.
This means that products from MongoDB to SAP HANA essentially rely on two things. One, that you are going to buy multiple database products in order to support your data value chain, and two that you are going to build out an expensive and large team to support this massive big data footprint. This means that your engineering budget is going to infrastructure instead of advancing your own intellectual property.
We wanted to build a database that any developer of any skill level could roll out in minutes and that would scale with them as their big data needs grow. We wanted to empower developers like us to tackle massive big data challenges with a simple solution that does the heavy lifting for them. We feel that developers should focus on coding and not spend their time managing database infrastructure.
HarperDB is designed to handle most workloads, eliminating the need for multiple products. Our product has a single model that supports both SQL and NoSQL transactions in a single storage mechanism allowing you to harness the power and scale of NoSQL, while utilizing full ANSI SQL without duplicating your data in memory or on disk. Our SQL supports complex math functions, multi-table sql joins, and multiple operators.
HarperDB is also fully indexed natively without requiring any configuration or increasing your storage footprint. This means that you can focus on coding your application, and have access to the data you need at the speed you want without having to worry about overly complex configurations, crazy data models, or database management.
In keeping with a small footprint, we wrote our product in Node.js. This allows us to have a small install footprint as well as a small runtime footprint. In fact, it is so small that we have downloadable versions for ARMv6 and ARMv7 that can be run on a Rasberry Pi or other micro-computing devices. We see a big shift in computing towards the edge, and having the ability to run an enterprise class database directly on an IoT device will be a strong competitive advantage for HarperDB customers.
In our careers we have also found that simply integrating with a database can be challenging. For HarperDB, we built a native REST API which is developer friendly and familiar. You don’t need to figure out a new language, or configure any drivers. Simply copy our sample code from docs.harperdb.io and you are good to go. You probably already know SQL or NoSQL and you can use the full functionality of HarperDB using either interface making the learning curve minimal.
So if you are a developer like us who would prefer spending your time coding rather than configuring an overly complex database solution give our community edition a try. It’s free and includes all of the features mentioned above. For larger organizations, we also offer an enterprise edition which includes clustering and analytics drivers.
Questions? Email us at email@example.com