When searching for a new in-memory analytic database system or types of database in general, it can be overwhelming to sift through the options if you don’t follow a defined process.
Before you get started, it’s key that you identify the business applications that need to benefit from performance. Indeed, it is imperative that you clearly specify the criteria needed in a database and define a complete set of benchmark requirements.
So, without further ado, here are 5 key things that you should consider when conducting research on in-memory analytic databases:
1. General system architecture
One of the first things to keep in mind is that every solution is different, and true analytic performance can only be guaranteed through a tight integration of in-memory computing, as opposed to just adding a cache. Through an integrated in-memory computing approach, users can run larger and more complex analytic workloads, as well as use the database for a wider range of use cases.
2. Costs and scalability
The next thing you should inquire about when researching systems are the software acquisition, or licensing costs, as well as hardware costs. Furthermore, you should ask if the solution is a scalable massively parallel (MPP) system where additional servers can be added in easily. If not, be prepared to foresee further costs as requirements and data volumes continue to grow.
Next, you need to investigate whether the solution is mature enough to handle complex analytic workloads. Does it support commonly-used drivers and interfaces? Does it integrate with the most widely-used Extract, Transform, Load (ETL) and business intelligence (BI) tools, too? As analytic ecosystems adapt over time, you need to ensure that the database will still be compatible in the future.
4. Vendor maturity and proven customer success
Enquire about the vendor behind the product and the levels of ongoing support that it offers. Reaching out to existing customers to discuss the system’s true real-life advantages as well as any shortcomings is also beneficial, that is if their customers are willing to share any insight. Failing that, demand to see customer success stories. It’s not just enough to use a technically solid solution; you need to assure yourselves that they can depend on the vendor and its customer ecosystem for ongoing support.
Finally, anyone considering an in-memory analytic database needs to ask whether the solution is easy to install and operate, or whether an army of database administrators is required to tune, design and implement the database as well as ETL processes. The more automated the solution, the fewer the hurdles you will have to jump over in order to find value in your analytics and BI projects.
Overall, performance and simplicity are two of the main driving factors in achieving ultimate satisfaction with an in-memory analytic database. Only when performance is super-fast can you run the most complex analyses in near real-time and concentrate on your analytics instead of your database setup.
When researching a new in-memory database system, we recommend that everyone uses the checklist above in order to ensure they have considered all of the key features they want in a new system, but also use this as an opportunity to simplify their IT infrastructure and use an in-memory database that can process much bigger workloads with much fewer hardware resources.
When choosing in-memory database systems, you will find that they scale extremely well, run far more analyses and allow many more users to analyze the data concurrently. This ultimately leads to less automation and less optimization of business processes allowing companies to focus on creating a data-driven business.
It’s time to kiss goodbye to slow queries and say hello to super-fast answers.