June 2022 Summaries

Databricks has announced its official integration with Cube, a unified platform for working with Spark that brings together the reliability and governance of data warehouses with the openness and flexibility of data lakes. The integration allows users to build analytics features into customer-facing apps in minutes, not weeks, by combining Databricks' unified analytics and data store with Cube's data modeling, access control, caching, and APIs. Users can connect their workspace to Cube using Databricks' SQL endpoint, which provides a simple way to create a new SQL endpoint and generate credentials for the integration. With this integration, users can model their data, manage security rules, apply caching, and expose their data definitions to any downstream application via Cube's REST, GraphQL, or SQL APIs.

Deepnote is a Jupyter-compatible notebook that enables real-time collaboration among data teams, allowing for instantaneous exploration, analysis, and presentation of data in infinite ways. It's designed as a "Google Docs" of data science, providing a fully managed multi-user Jupyter server. Cube, on the other hand, is a headless BI platform that sits between data sources and downstream applications, providing consistent metrics definitions for all data flowing downstream. Together, Deepnote and Cube provide a common starting line for various teams working together within an organization, eliminating inconsistent data and misaligned insights. By using Cube to define consistently defined data upstream, teams can ensure reliable data to drive the whole organization in the same direction.

Cube has recently enhanced its blog to improve user experience for its diverse audience, which includes data engineers and application developers, by offering better access to guides and tutorials on integrating Cube with various data tools. As a headless BI platform, Cube has expanded its integrations with tools like Tableau, Ahana, Supabase, Materialize, QuestDB, and dbt, each supported by comprehensive technical guides. The blog now features revamped navigation with top-level categories that cater to both application developers and the growing interest from the data engineering community, facilitating easier access to themed collections of posts. To better serve its technically inclined readers, Cube provides extensive guides and tutorials, each accompanied by a table of contents for easier navigation, and highlights the collective authorship by showcasing authors' profiles with social links. The Growth Engineering team at Cube has aimed to make the blog resemble a textbook on data engineering and app development, ensuring users derive immediate value while maintaining regular content updates. The team eagerly anticipates further discussions on the blog's development, inviting feedback through Slack or Twitter.

Cube has launched its SQL API, which enhances compatibility with BI tools and data notebooks, allowing consistent, performant, and secure data access across various applications within a company. The API makes Cube definitions universally accessible, ensuring seamless integration with any BI tool that connects via SQL. A tutorial demonstrates how to connect Tableau Online to Cube Cloud using the SQL API, providing a step-by-step guide from creating a Cube Cloud project and defining custom measures or dimensions, to configuring Tableau with Cube's SQL connection details. This integration allows for the creation of visualizations in Tableau using measures and dimensions defined in Cube, making data accessible to both business users and app developers. The API is part of Cube's ongoing efforts to expand support for more BI applications, encouraging users to explore its capabilities further.

Ahana provides managed Presto clusters running in your AWS account, allowing you to deploy a single compute engine for an entire data lake without spending hours configuring infrastructure management. This enables data engineers to focus on processing data while hiding the burden of infrastructure management. Ahana connects to multiple databases or data sources, including Amazon S3, and implements the data federation feature, which processes data from multiple sources as if they were stored in a single database. By using Ahana, you can quickly build data applications or analyze your data in BI tools like Cube. The integration between Ahana and Cube allows for real-time data retrieval, caching, and access control, making it easy to create personalized dashboards showing the results of your hard work. With Ahana, you can deploy a Presto cluster within minutes without spending hours configuring the service, VPCs, and AWS access rights, allowing you to focus on processing your data.

Databricks recently set a new data warehousing performance record in the 100 TB TPC-DS benchmark, claiming it was 2.7x faster and 12x better in terms of price performance compared to Snowflake. However, Snowflake responded with a blog post that critically reviewed Databricks' findings, reported different results for the same benchmark, and claimed comparable price/performance to Databricks. This back-and-forth between respected data vendors led to Databricks eliminating the anti-competitive DeWitt clause from their service terms, which Snowflake followed up with a similar update. The DeWitt clause is a provision in end-user license agreements that prevents publishing information about proprietary software without explicit approval from the vendor. This clause has become widespread in the industry and can have significant implications for benchmarking and competitive testing. As a result, some vendors have chosen to restrict or eliminate benchmarks with DeWitt clauses, while others permit it but require reciprocity. Open-source vendors do not typically include DeWitt clauses in their licenses, allowing users to publish benchmark results freely. Some cloud vendors, such as Amazon Web Services and CockroachDB Serverless, do not have DeWitt clauses and allow customers to perform and disclose benchmarks without restrictions. In contrast, some proprietary vendors like Cloudera Data Platform and Oracle restrict or require prior approval for publishing benchmark results. The absence of a DeWitt clause can facilitate open competition in the industry, while its presence can create barriers to entry. By exploring which vendors have DeWitt clauses and which do not, users can make informed decisions about whether to use their services for competitive testing and benchmarking purposes.

The tutorial provides a step-by-step guide on building a dynamic and performant data visualization dashboard using Supabase for data storage and Cube for efficient data querying and aggregation, all within a 15-minute timeframe. It addresses the challenges of handling large datasets and slow query times associated with traditional row-oriented databases like PostgreSQL, which Supabase uses. The tutorial introduces Cube, an open-source business intelligence platform that offers faster query responses through pre-aggregations and an API layer, enhancing the user experience in data applications. It explains how to set up a Cube instance using Cube Cloud, auto-generate data schemas, and create visualizations in React, using features like autogenerated front-end code and pre-aggregations to significantly improve query performance. The tutorial also highlights the additional features of Cube, such as multi-tenancy and various API supports, encouraging further exploration through Cube’s documentation and community resources.