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Analytics

SaaS in 60 - Customer Managed Keys - Phase 2

This week we have released the next phase of our Customer Managed Keys security offering. Last year, we gave customers complete control over data encryption in their Qlik Cloud Tenant by leveraging their own encryption keys provided by the AWS Key Management Service. In Phase 2 you can now convert an existing in-use Qlik Cloud Tenant, from Qlik’s Internal KMS to AWS KMS and back if needed. You can also convert a tenant from one AWS KMS Key to another AWS KMS Key to support key rotation, or effectively, a complete re-encryption.

Unleash cloud-native analytics and AI on-premises with Cloudera

Unlock the power of your on-premises data with Cloudera for private cloud. Harness cloud-native agility, flexibility, and cost efficiency within your private open data lakehouse for unparalleled access and control over your data. Build a foundation of secure, accurate, and trusted data for precise business insights and of course, trusted AI. Unleash the full potential of your data with Cloudera's Private Cloud Data Services.

Getting Started With Cloudera Open Data Lakehouse on Private Cloud

Cloudera recently released a fully featured Open Data Lakehouse, powered by Apache Iceberg in the private cloud, in addition to what’s already been available for the Open Data Lakehouse in the public cloud since last year. This release signified Cloudera’s vision of Iceberg everywhere. Customers can deploy Open Data Lakehouse wherever the data resides—any public cloud, private cloud, or hybrid cloud, and port workloads seamlessly across deployments.

How to Build Multi-Tenant Environments with Yellowfin BI

Multi-tenancy is almost a prerequisite to provide a secure environment for each of your customers when using business intelligence (BI) tools embedded in external services. Although it is possible to control the access rights by granting individual access to user accounts without separating tenants, it is obvious that the management will become more complicated as the number of customers grows. In a previous blog, we covered what multi-tenancy means in the context of embedded analytics.

The Value of an Enterprise Data Warehouse

Enterprise Data Warehouses (EDW) have emerged as a pivotal component for businesses striving to harness the power of data analytics and business intelligence. As technology advances, the complexity and volume of data sets have surged, accentuating the role of an EDW. This guide offers a deep dive into the intricacies of the Enterprise Data Warehouse, its significance, functionality, and the considerations for its implementation.

How Unravel Enhances Airflow

In today’s data-driven world, there is a huge amount of data flowing into the business. Engineers spend a large part of their time in building pipelines—to collect the data from different sources, process it, and transform it to useful datasets that can be sent to business intelligence applications or machine learning models. Tools like Airflow are used to orchestrate complex data pipelines by programmatically authoring, scheduling, and monitoring the workflow pipelines.

The Evolution of Search: How Multi-Modal LLMs Transcend Vector Databases

As we venture deeper into the data-driven era, the traditional systems we have employed to store, search, and analyze data are being challenged by revolutionary advancements in Artificial Intelligence. One such groundbreaking development is the notable advent of Large Language Models (LLMs), specifically those with Multi-Mod[a]l abilities (e.g., Image & Audio).