Secure and govern data once, without data copies.
Data is an enterprise’s most important asset. Improving performance, agility, and the return on investment in analytics is important, but means nothing if data is not properly secured and governed. Enable self-service access to curated data sets without the risks associated with data movement and the complexity of integrating a myriad of security and authorization protocols. Empower business users to find new insights using a single data interface, no matter where the data is stored. Allow IT to secure access and adhere to corporate security and governance policies.
Ensure secure communication channels with support for TLS-secured communications. Secure data at rest using data-platform-level encryption zones and masking.
Manage all of your users and groups using your existing LDAP or Active Directory infrastructure. Use delegated authorization for any BI tool or custom application.
Selectively mask measures, dimensions and hierarchies to provide multiple views for users and groups based on their access and visibility rights.
Leverage AtScale’s Security Dimensions to constrain data visibility for end users. Manage data access rules through flexible, multidimensional lookup tables.
Control exactly which users and groups can access AtScale projects and virtual cubes. Define and assign roles for administrators, designers, query users and more.
One of the world’s largest chemical companies faced a scenario where a large percentage of their data was getting siloed into disparate databases. As a result, business intelligence users wishing to analyze data across multiple sources had to extract copies of data onto local machines, creating their own databases. Over time, these copies were edited locally and became increasingly less accurate relative to the original data. In addition to reporting on inaccurate data, the presence of millions of extracts represented a huge security headache. The existence of these copies essentially bypassed all of the corporate data securities the chemical company had put into place.
The chemical company centralized its data in a hybrid cloud data lake, and implemented AtScale’s Adaptive Analytics Fabric to improve performance and accuracy of their analytical workloads. AtScale has enabled BI users to connect live to the central data repository regardless of what BI tool they use, which is critical as the chemical company has substantial constituencies using Excel, SAP, Tableau, and Power BI. With a self-service view into live data, BI users no longer need to extract data to local machines. Eliminating millions of data extracts ensures that the chemical company’s data is secured and governed to the highest level.
Provide data discoverability with safety using AtScale’s virtual cube discoverability tools. Publishers can advertise their virtual cubes based on user and group access rules while consumers can view the dimensions, metrics and usage statistics with a self service, web based interface.
Give publishers the tools they need for creating a direct conversation with their data consumers using AtScale’s request/grant access workflow. Consumers can browse AtScale’s virtual cube catalog and request access from the data owner to provide full visibility and auditability for managing access to data without burdening IT.
Leverage your corporate data catalog and governance tools for setting rules and managing information policies and enforce those rules using AtScale’s virtualized governance layer. Since AtScale intercepts every analytics query, rest assured that your data governance is applied uniformly and consistently.
Webinar: AtScale Office Hours with CTO Matt Baird
AtScale CTO and Co-Founder Matt Baird discusses the importance of security to the modern enterprise’s operational analytics, and AtScale’s approach to ensuring data security and governance.
Video: Securing Data In The Cloud
In video five of AtScale’s Cloud Transformation Course, learn how AtScale integrates with and augments the security programs of cloud data platforms such as Snowflake, Google BigQuery, and Amazon Resdshift.