Exasol Enhances Performance, Security and Cloud Deployment with New Database Features

New Innovations Include Cloud Deployment and Reconfiguration Wizard for AWS, Microsoft Azure, and Google Cloud Platform; Exasol Also Unveils Spark Connector and Row-Level Security

Exasol, the analytics database provider, launched version 6.2 of its analytics database to further strengthen its already excellent performance, enable even better integration, revamp its enterprise features, and enhance data science capabilities.

.@ExasolAG enhances performance, security and cloud deployment with new database features

Exasol also launched a cloud deployment and a reconfiguration wizard to ease the deployment, configuration, and operation of the Exasol analytics database in AWS, Microsoft Azure, and Google Cloud Platform (GCP).

“At Exasol, we believe in constant innovation. This is why we never stop working on our database, to make sure that we stay ahead of the curve and deliver the best possible service to our customers,” said Mathias Golombek, CTO of Exasol. “For enterprises transitioning to the cloud, a hybrid deployment is a must because it enables a smoother migration and allows the enterprise to set its own pace. The hybrid model keeps risks and costs under control while still providing scalability, infrastructure provisioning and efficiency, and effectiveness benefits. That’s why we’ve made it easy to deploy and configure our database on public cloud.”

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For organizations, speed to value is critical to achieve business objectives. Since its inception, Exasol has been the undisputed leader in performance, consistently beating TPC-H Benchmarks and other performance indicators. The new release continues to put emphasis in this critical feature. That’s why the Exasol team completely refactored the analytic functions of the database to deliver even more outstanding performance and scalability.

The new version of the Exasol database includes over 50,000 new lines of code to support all window frame units and all available sliding windows. Users can now run scoring queries – running sums or averages – with even better performance and a wider array of options, making use of Exasol’s in-database analytics abilities.

Exasol also continues to strive for better integration of its database. Another important innovation released with Exasol 6.2 is a Spark connector which enables data scientists to improve efficiency.

“Collecting data from various sources, such as data lakes and data warehouses, consumes a lot of time for data scientists when trying to get new insights from data. Exasol’s Spark connector is a result of close collaboration with customers to simplify data collection for data scientists,” said Mathias Golombek. “With the Spark connector, Exasol enables data scientists to access all the data they need at their fingertips. And, most importantly, all source codes are open source.”

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Data security has always been a priority for Exasol, and as such, the Exasol team has continued to improve the security and compliance features of its database. In Exasol 6.2, DBAs have a powerful framework to implement row level security based on the virtual schema concept. This will ease the implementation and, most crucially, simplify the maintenance of row-level security to allow users to invest their time in value-add projects instead.

Exasol enables teams to save time and gain invaluable business insights across the entire organization by running statistical models on raw data, in any data science language – straight from the database. The new release includes innovations to make life even easier for data science teams by allowing for enhanced machine learning (ML) and artificial intelligence (AI) capabilities. This was achieved by the release of PYTHON 3 with data frame support.

Exasol also released precompiled data science containers including Tensorflow Keras, Scikit learn, and others. In total, seven precompiled containers have been added, all of which are open source to enable for contributions and better adaptations of the technology. This approach further simplifies the implementation of data science, ML and AI with Exasol.

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