February 2025 Summaries

Organizations face challenges in structuring data for analytics, as traditional Online Transaction Processing (OLTP) systems prioritize speed and reliability rather than analytical efficiency. OLTP systems, which use a Third Normal Form schema, are not optimal for complex queries needed in Online Analytics Processing (OLAP) systems. Ralph Kimball's dimensional modeling, including the Star Schema, offers a solution by transforming OLTP data into a more analyzable format through ETL processes, though this can be time-consuming and require expertise. Despite the benefits of improved query performance and ease of use, adopting star schemas involves data duplication and maintenance efforts. Cube's universal semantic layer presents a modern alternative, enabling logical data mapping without extensive ETL, thereby providing quick and scalable data access across OLTP and OLAP systems. This approach facilitates both immediate data needs and long-term data strategy, allowing organizations to maintain agility and optimize their analytics capabilities in a cloud-native environment.

At the upcoming Gartner Data & Analytics Summit, Cube is focused on helping enterprise data leaders move from inspiring exploration to leading execution by providing a universal semantic layer that unifies, governs, optimizes, and integrates enterprise data for both AI and analytics. This enables organizations to balance opportunity and risk while delivering tangible value to their businesses, aligning AI strategies with business goals, making data AI-ready, delivering trusted insights across the enterprise, unifying and optimizing enterprise data, ensuring security, governance, and compliance, and showcasing Cube's capabilities at the summit.

The primary role of a semantic layer is to provide a dimensional view of a physical data model, allowing for efficient consumption of data without redundant storage. This is important because users consume data by domain, querying dimensions and metrics related to an invoice or user. A universal semantic layer bridges the gap between the logical model desired by consumers and the physical model imposed by the database. The goal of a semantic layer should also reduce the complexity of securing data. However, traditional approaches to security often lead to "security sprawl" due to multiple consumption points added to a data platform, resulting in gaps in authentication, authorization, row-level security, column-masking, and caching. Cube Cloud's universal semantic layer eliminates model chaos by centralizing your model and providing a centralized security model that can be version controlled and approved via PR. This approach enables user-centric controls, logging, governance, and adaptability, securing data extracted from sensitive data while preventing consumers from accessing the physical layer where sensitive data is stored.

Spreadsheets remain a crucial tool for business users and data analysts worldwide, with many relying on them as much as standalone BI tools. Microsoft Excel support was previously limited to Windows due to the MDX API restriction, but an official add-in called Cube Cloud for Excel is now available in public preview, expanding its reach to other operating systems and platforms. This add-in enables users to build reports in Excel using metrics from their Cube Cloud semantic layer, streamlining their workflow and providing a native-like pivot table UI experience.

Microsoft Power BI remains unrivaled and strong despite recent changes in the data space. Cube Cloud has introduced support for Power BI, bridging the gap with BI tools using SQL API and Semantic Layer Sync. The DAX API enables native connectivity between Cube Cloud and Microsoft Power BI, allowing users to connect to their semantic layer in a similar way as Azure Analysis Services or regular SSAS instances. This feature is currently available in public preview, offering support for frequently used visualization types, live connection and DirectQuery methods, and an essential subset of DAX functions. The DAX API is now available on Enterprise product tiers, requiring deployment tier M for optimal querying experience and low-latency responses.

Microsoft has had a significant impact on the analytics field, with its Excel spreadsheet software being the most successful and enduring tool of all-time. However, another Microsoft technology, Power BI, has quietly captured an enormous amount of the business intelligence market in the preceding decade. To support users who integrate Cube Cloud with both Excel and Power BI, two exciting new features have been introduced: the Cube Cloud for Excel Add-in and the DAX API for Power BI. The Cube Cloud for Excel Add-in provides a custom pivot table UI experience that allows users to easily perform detailed analysis, while the DAX API for Power BI enables improved feature compatibility and performance, allowing users to connect their Cube Cloud environment to Power BI over the XMLA protocol. These new features continue to build on Microsoft's commitment to support the ecosystem and provide large compatibility and accessibility improvements for users who integrate Cube Cloud with Excel and Power BI.

The latest release of Cube Core version 1.2 includes several exciting features and improvements, including data access policies for access control, hierarchies and folders in the data model, new features in Playground, support for Apache Pinot and Vertica as data sources, performance optimizations in Cube Store and APIs, a new configuration option for scheduled refresh time zones, and various enhancements to the client library and CLI tool. This release has no breaking changes, making it easy to upgrade without affecting existing data models or configurations.

Playsground has received an update with new features that enhance its functionality. The latest version supports hierarchies and folders in the data modeling layer, allowing users to organize and structure their data model more effectively. Custom time dimension granularities are also supported, enabling users to define granularities such as a fiscal year or week starting on Sunday for any time dimension. Advanced filtering capabilities have been added, including nested filtering conditions with unlimited depth and filters that accept values from the dataset. Additionally, new querying options have been introduced, including the ability to set limits and offsets for queries and request additional query results. These features are available in Cube Core v1.2.0 and Cube Cloud, and users are encouraged to upgrade to take advantage of them.

The text discusses the evolution of OLAP systems and the communication protocol between a standalone semantic layer and data consumption layers, specifically focusing on SQL as a potential solution. Historically, MDX was used for OLAP servers, but it has largely been replaced by DAX within Microsoft's ecosystem. However, outside of Microsoft, SQL remains a dominant protocol due to its widespread adoption and maturity in the database world. Despite being well-suited for this purpose, SQL faces a significant challenge: its bottom-up evaluation flow is incompatible with multidimensional primitives like measures, which are crucial for analytical queries. To address this issue, rewriting queries to align with bottom-up evaluation is necessary, but it requires careful transformation to avoid incorrect aggregation issues. The text concludes that rewriting queries is not new and can be efficiently achieved using E-Graph theory, as demonstrated by Cube's open-source semantic layer engine.

Cube, the only open-source semantic layer with significant adoption, has enhanced its data modeling capabilities by introducing support for hierarchies and folders, aimed at improving the organization and navigation of data models. These new features allow users to define hierarchies that group related dimensions with appropriate levels of granularity and organize view members into logical folders, making data models more intuitive and accessible in BI tools like Microsoft Excel, Power BI, and Tableau. Available in Cube Core and Cube Cloud from version 1.2.0 onwards, these enhancements are designed to be seamlessly integrated into both existing business intelligence environments and custom analytics solutions, with additional support and documentation provided to facilitate user adoption.

Cube has announced the general availability of data access policies in both Cube Core and Cube Cloud, enhancing its universal semantic layer by allowing users to define access control rules directly in data model files. This feature, which supports both member-level and row-level security, has been refined based on feedback from Cube Cloud customers. Cube Cloud now also includes an LDAP integration, enabling the importation of user roles and permissions from LDAP servers, which can be used in data access policies. Additionally, Cube Cloud authentication integration enhances authentication processes by allowing connections to Cube's SQL API to be authenticated against user lists in Cube Cloud accounts. These new capabilities aim to streamline access control management, particularly for enterprise customers, by leveraging existing LDAP infrastructure and providing a structured approach to security.