diff --git a/docs/docs-new/pages/product/configuration/data-sources/snowflake.mdx b/docs/docs-new/pages/product/configuration/data-sources/snowflake.mdx index 5243740ac6e81..ad548243ee604 100644 --- a/docs/docs-new/pages/product/configuration/data-sources/snowflake.mdx +++ b/docs/docs-new/pages/product/configuration/data-sources/snowflake.mdx @@ -5,14 +5,6 @@ redirect_from: # Snowflake - - -[Join our upcoming Office Hours on July 26 at 9am PST](https://cube.dev/events/unlock-data-cube-snowflake) -on Getting Started with Cube Cloud and Snowflake. Learn how to easily connect -Cube Cloud and Snowflake, load your data, and integrate your BI tools. - - - ## Prerequisites - [The account ID][snowflake-docs-account-id] for [Snowflake][snowflake] diff --git a/docs/docs-new/pages/product/getting-started/cloud.mdx b/docs/docs-new/pages/product/getting-started/cloud.mdx index d1b8a7e9388af..a77bca02d19b7 100644 --- a/docs/docs-new/pages/product/getting-started/cloud.mdx +++ b/docs/docs-new/pages/product/getting-started/cloud.mdx @@ -5,14 +5,6 @@ redirect_from: # Getting started with Cube Cloud - - -[Join our upcoming Office Hours on July 26 at 9am PST](https://cube.dev/events/unlock-data-cube-snowflake) -on Getting Started with Cube Cloud and Snowflake. Learn how to easily connect -Cube Cloud and Snowflake, load your data, and integrate your BI tools. - - - This getting started guide will show you how to use Cube Cloud with Snowflake. You will learn how to: diff --git a/docs/docs-new/pages/product/getting-started/cloud/connect-to-snowflake.mdx b/docs/docs-new/pages/product/getting-started/cloud/connect-to-snowflake.mdx index 1430b25280155..4adfafbbc88ff 100644 --- a/docs/docs-new/pages/product/getting-started/cloud/connect-to-snowflake.mdx +++ b/docs/docs-new/pages/product/getting-started/cloud/connect-to-snowflake.mdx @@ -5,14 +5,6 @@ redirect_from: # Connect to Snowflake - - -[Join our upcoming Office Hours on July 26 at 9am PST](https://cube.dev/events/unlock-data-cube-snowflake) -on Getting Started with Cube Cloud and Snowflake. Learn how to easily connect -Cube Cloud and Snowflake, load your data, and integrate your BI tools. - - - In this section, we’ll create a Cube Cloud deployment and connect it to Snowflake. A deployment represents a data model, configuration, and managed infrastructure. diff --git a/docs/docs-new/pages/product/getting-started/cloud/create-data-model.mdx b/docs/docs-new/pages/product/getting-started/cloud/create-data-model.mdx index dcf922e297d9a..2d349540cd491 100644 --- a/docs/docs-new/pages/product/getting-started/cloud/create-data-model.mdx +++ b/docs/docs-new/pages/product/getting-started/cloud/create-data-model.mdx @@ -5,14 +5,6 @@ redirect_from: # Create your first data model - - -[Join our upcoming Office Hours on July 26 at 9am PST](https://cube.dev/events/unlock-data-cube-snowflake) -on Getting Started with Cube Cloud and Snowflake. Learn how to easily connect -Cube Cloud and Snowflake, load your data, and integrate your BI tools. - - - Cube follows a dataset-oriented data modeling approach, which is inspired by and expands upon dimensional modeling. Cube incorporates this approach and provides a practical framework for implementing dataset-oriented data modeling. diff --git a/docs/docs-new/pages/product/getting-started/cloud/load-data.mdx b/docs/docs-new/pages/product/getting-started/cloud/load-data.mdx index cb549652545b1..95e4730369c6c 100644 --- a/docs/docs-new/pages/product/getting-started/cloud/load-data.mdx +++ b/docs/docs-new/pages/product/getting-started/cloud/load-data.mdx @@ -5,14 +5,6 @@ redirect_from: # Load data - - -[Join our upcoming Office Hours on July 26 at 9am PST](https://cube.dev/events/unlock-data-cube-snowflake) -on Getting Started with Cube Cloud and Snowflake. Learn how to easily connect -Cube Cloud and Snowflake, load your data, and integrate your BI tools. - - - The following steps will guide you through setting up a Snowflake account and uploading the demo dataset, which is stored as CSV files in a public S3 bucket. diff --git a/docs/docs-new/pages/product/getting-started/cloud/query-from-bi.mdx b/docs/docs-new/pages/product/getting-started/cloud/query-from-bi.mdx index 8c3ba8b40ecc8..cf84189e8e798 100644 --- a/docs/docs-new/pages/product/getting-started/cloud/query-from-bi.mdx +++ b/docs/docs-new/pages/product/getting-started/cloud/query-from-bi.mdx @@ -5,14 +5,6 @@ redirect_from: # Query from a BI tool - - -[Join our upcoming Office Hours on July 26 at 9am PST](https://cube.dev/events/unlock-data-cube-snowflake) -on Getting Started with Cube Cloud and Snowflake. Learn how to easily connect -Cube Cloud and Snowflake, load your data, and integrate your BI tools. - - - You can query Cube using a BI or visualization tool through the Cube SQL API. To provide a good end-user experience in your BI tool, we recommend mapping the BI's data model to Cube's semantic layer. This can be done automatically with diff --git a/docs/docs-new/pages/product/introduction.mdx b/docs/docs-new/pages/product/introduction.mdx index cf0037d0ed0a1..27d5c415a5a84 100644 --- a/docs/docs-new/pages/product/introduction.mdx +++ b/docs/docs-new/pages/product/introduction.mdx @@ -6,32 +6,40 @@ redirect_from: # Introduction -Cube is a universal semantic layer that makes it easy to connect data silos, create consistent metrics, and make them accessible -to any data experience your business or your customers needs. Data engineers and application developers use Cube’s developer-friendly platform to organize data from your cloud data warehouses into centralized, -consistent definitions, and deliver it to every downstream tool via its APIs. + -Your business data becomes consistent, accurate, easy to access, and, most importantly, trusted. -Once trusted, the use of data accelerates throughout your organization, delivering better experiences +Join our [next webinar on August 16 at 9am PST](https://event.on24.com/wcc/r/4303994/246D3B84D2FC1E29AA0038EDAEC0C2B7?partnerref=docs) +to gain insights into Cube’s semantic layer. Explore its key components and +find out how it enhances your modern data stack. + + + +Cube is a universal semantic layer that makes it easy to connect data silos, create consistent metrics, and make them accessible +to any data experience your business or your customers needs. Data engineers and application developers use Cube’s developer-friendly platform to organize data from your cloud data warehouses into centralized, +consistent definitions, and deliver it to every downstream tool via its APIs. + +Your business data becomes consistent, accurate, easy to access, and, most importantly, trusted. +Once trusted, the use of data accelerates throughout your organization, delivering better experiences to your customers and driving intelligence back into the business. -With Cube, you can build a data model, manage access control and caching, and expose your data to every application -via REST, GraphQL, and SQL APIs. With these APIs, you can use any charting library to build custom UI, -connect existing dashboarding and reporting tools, and build AI agents with frameworks like Langchain. +With Cube, you can build a data model, manage access control and caching, and expose your data to every application +via REST, GraphQL, and SQL APIs. With these APIs, you can use any charting library to build custom UI, +connect existing dashboarding and reporting tools, and build AI agents with frameworks like Langchain. ## Code-first -Throughout the evolution of software engineering, numerous tools and methodologies have been developed to effectively handle codebases of all sizes. -These include [version control systems](https://git-scm.com/) for seamless collaboration and code reviews, +Throughout the evolution of software engineering, numerous tools and methodologies have been developed to effectively handle codebases of all sizes. +These include [version control systems](https://git-scm.com/) for seamless collaboration and code reviews, infrastructure for testing and documentation, as well as [established patterns](https://en.wikipedia.org/wiki/Design_Patterns) and best practices to structure codebases for reusability and maintainability. -At Cube, we firmly believe that the future of data engineering lies in the application of these proven practices and tools to data management. +At Cube, we firmly believe that the future of data engineering lies in the application of these proven practices and tools to data management. By doing so, we can facilitate collaboration at scale and create high-quality data products that are easily maintainable. -The foundation of this approach lies in adopting a code-first workflow. +The foundation of this approach lies in adopting a code-first workflow. That's why everything within Cube, from configurations to data models, is meticulously managed through code. @@ -43,11 +51,11 @@ We believe that a complete, universal semantic layer should have the following f **Data modeling framework is a foundational piece of the universal semantic layer.** It helps data teams to centralize data models upstream from data consumption tools, such as BIs, embedded analytics applications, or AI agents. It makes your data architecture DRY -([Don’t Repeat Yourself](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself)) by reducing the repetition of data modeling across multiple presentation layers. +([Don’t Repeat Yourself](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself)) by reducing the repetition of data modeling across multiple presentation layers. **Cube data model is code-first.** Data teams define data models with YAML or Javascript code. -The codebase is commonly managed with a version control system. Cube enables git flow for -changes to data model and managing multiple isolated environments per project. +The codebase is commonly managed with a version control system. Cube enables git flow for +changes to data model and managing multiple isolated environments per project. **Cube data model is dataset-centric.** It is inspired by and expands upon dimensional modeling. Cube provides a practical framework for implementing dataset-centric data modeling. @@ -57,31 +65,31 @@ When building a data model in Cube, you work with two dataset-centric objects: * you define all the calculations within the measures and dimensions of these entities. Additionally, you define relationships between cubes, such as "an order has many line items" or "a user may place multiple orders." -**Views** sit on top of a data graph of cubes and create a facade of your entire data model, +**Views** sit on top of a data graph of cubes and create a facade of your entire data model, with which data consumers can interact. You can think of views as the final data products for your -data consumers - BI users, data apps, AI agents, etc. When building views, you select measures and dimensions +data consumers - BI users, data apps, AI agents, etc. When building views, you select measures and dimensions from different connected cubes and present them as a single dataset to BI or data apps. ### Access Control **One of the benefits of semantic layer is the active security layer.** -Semantic layer provides a comprehensive real-time understanding and governance of your data. -When all your data consumption tools access data through the semantic layer, it becomes an ideal place to enforce access control policies. +Semantic layer provides a comprehensive real-time understanding and governance of your data. +When all your data consumption tools access data through the semantic layer, it becomes an ideal place to enforce access control policies. -Cube provides infrastructure to define different access control policies and patterns, +Cube provides infrastructure to define different access control policies and patterns, including row-level and column-level security, data masking and more. Being a code-first, Cube enables data teams to **define access control policies with Python or Javascript.** -They can range from simple row-level access rules to completely custom data models per tenants backed by different data sources. +They can range from simple row-level access rules to completely custom data models per tenants backed by different data sources. ### Caching -The semantic layer can serve as a buffer to the data sources, protecting the cloud data warehouses from unnecessary and redundant load. +The semantic layer can serve as a buffer to the data sources, protecting the cloud data warehouses from unnecessary and redundant load. Caching optimizes performance and can reduce the cloud data warehouse cost. -Cube implements caching through the **aggregate awareness framework called pre-aggregations.** -Data teams can define pre-aggregates in the data model as rollup tables, including measures and dimensions. +Cube implements caching through the **aggregate awareness framework called pre-aggregations.** +Data teams can define pre-aggregates in the data model as rollup tables, including measures and dimensions. Cube builds and refreshes these pre-aggregates in the background by executing queries in your cloud data warehouse -and storing results in Cube Store, Cube’s purpose-built caching engine backed by distributed file storage, such as S3. +and storing results in Cube Store, Cube’s purpose-built caching engine backed by distributed file storage, such as S3. Pre-aggregations can be refreshed on schedule or as a part of the workflow orchestration DAG. When you send a query to Cube, it will use aggregate awareness to see if an existing and fresh pre-aggregate is @@ -89,22 +97,22 @@ available to serve that query. It can significantly speed up queries and reduce ### APIs -One of the key requirements of the semantic layer is **interoperability with data consumption tools**: BIs, embedded analytics, and AI agents. -The universal semantic layer cannot require one-off integration with every tool, framework, or library. -It is not feasible to support the ever-growing number of data consumption tools in a one-to-one model. +One of the key requirements of the semantic layer is **interoperability with data consumption tools**: BIs, embedded analytics, and AI agents. +The universal semantic layer cannot require one-off integration with every tool, framework, or library. +It is not feasible to support the ever-growing number of data consumption tools in a one-to-one model. Rather than inventing its own communication language or protocol, **the semantic layer must adhere to existing protocols and API standards** to ensure universal interoperability. Cube embraces and implements the three most commonly used protocols and API standards: **REST, GraphQL, and SQL.** -**REST and GraphQL** are commonly used in software development as a communication layer between the backend server and the frontend visualization layer. +**REST and GraphQL** are commonly used in software development as a communication layer between the backend server and the frontend visualization layer. **SQL** is universally adopted across all the tools in the data stack. Every BI and visualization tool can query a SQL data source. That makes SQL an obvious choice for a communication layer to ensure interoperability. Cube implements Postgres SQL and extends -it to support data modeling in the semantic layer. Cube adds the notion of **measure** to SQL spec, a special type that knows how to +it to support data modeling in the semantic layer. Cube adds the notion of **measure** to SQL spec, a special type that knows how to evaluate itself based on the definition in the data model. Every BI and visualization tool that can connect to Postgres or Refshift can connect to Cube. Finally, Cube exposes **robust meta API for data model introspection.** It is vital to achieve interoperability because it enables other tools to inspect the data model definitions and take actions, e.g. provide context to the AI agents querying the semantic -layer or create the necessary mappings in a BI tool to data model objects. +layer or create the necessary mappings in a BI tool to data model objects. diff --git a/docs/docs-new/pages/product/workspace/semantic-layer-sync.mdx b/docs/docs-new/pages/product/workspace/semantic-layer-sync.mdx index da93605780cf1..48d3155dc54d8 100644 --- a/docs/docs-new/pages/product/workspace/semantic-layer-sync.mdx +++ b/docs/docs-new/pages/product/workspace/semantic-layer-sync.mdx @@ -5,6 +5,14 @@ redirect_from: # Semantic Layer Sync + + +Join our [next webinar on August 16 at 9am PST](https://event.on24.com/wcc/r/4303994/246D3B84D2FC1E29AA0038EDAEC0C2B7?partnerref=docs) +to gain insights into Cube’s semantic layer. Explore its key components and +find out how it enhances your modern data stack. + + + Semantic Layer Sync synchronizes the [data model][ref-data-model] of a semantic layer from Cube to BI tools. It's the easiest way to connect a BI tool to Cube.