Top Vertica Alternatives

It transforms your current data warehouse into the Vertica Analytics Platform, which enhances the dynamics of your industry by providing network optimization, financial compliance, and much more. Data on the Exabyte scale is also efficiently handled using this system. Your present SQL analytical system is scalable using as many commodity servers as per your business requirements.

Get faster analytics

Using Vertica, you can analyze your data in real-time, and the queries you write will run up to 50 times faster compared to other systems without worrying about the size of data being input. The amount of time it took to perform calculations on data is drastically reducible, and hence your developers get more time to deliver more critical tasks. Analytical models made in other languages or systems such as Python, Spark, and SPSS are easily importable into Vertica to provide portability. On the other hand, you can quickly export models from this framework as edge nodes in IoT networks.

Work with your existing tools

You can easily integrate your pre-existing Business Intelligence tools like Cognos, Looker, Tableau, and others in Vertica. Its open-source nature provides analytical capabilities for any size of the business, and all your current work is importable within a few clicks, which saves time and effort required to redesign according to a new system. Any dimension of analytical work on data is efficiently analyzable using this system, no matter if its in Hadoop or ordinarily formatted. All popular SQL SDKs such as Java and Python are inherently supportable, which provides the opportunity to create and utilize custom tools.

Achieve better storage

All the data is storable in a cloud-based system, which ensures its protection from malicious agents and onsite attacks. Any catastrophic failure is minimizable as many fail-safes are built into the cloud systems. For situations in which critical information needs to be stored in your disks, you will be able to compress the data, which ensures low space usage and highly efficient function execution. Columnar storage of data smartly arranges it in such a way that only necessary fields are read during querying, and time taken is vastly reducible.