The ongoing story of the digital age is still about [taking products apart](https://hbr.org/2014/06/how-to-succeed-in-business-by-bundling-and-unbundling). The wave of [bundling](https://towardsdatascience.com/the-great-data-debate-unbundling-or-bundling-7d7721ee8514), [unbundling](https://blog.fal.ai/the-unbundling-of-airflow-2/), and [rebundling](https://dagster.io/blog/rebundling-the-data-platform) happening in the data space has subsided, but the point remains. Generations of tools in the modern data stack, whatever it is, succeed each other primarily for two reasons:

- Emerging tools provide more flexibility and a lower entry barrier for practitioners.
- Alternatively, teams escape into a safe harbor of a well-known integrated solution that promises big returns on even bigger investments. Indeed, has anyone ever got fired for buying Looker?

Well, one can only so much joke about Looker. Things like Looker’s $2.6B acquisition by Google in 2019, Snowflake’s $3.4B IPO in 2020, or dbt Labs’ [four consecutive venture rounds](https://pedram.substack.com/i/54634871/the-devils-bargain-of-venture-funding) in 2020–2022 leading to a $4.2B valuation fuel the data industry and help put more tools into a data practitioner’s toolbox.

Today, we’re going to talk about Cube and Apache Superset—a bundle of tools representing a finer alternative to Looker in many use cases.

> We've had a webinar on replacing Looker with Cube and Superset. [Check the recording and slides.](https://cube.dev/events/replacing-looker-with-cube-and-superset)

## Free as in “open source”

[Cube](https://cube.dev) and [Superset](https://superset.apache.org) belong to different categories in the data stack. The former is the [headless business intelligence](https://cube.dev/blog/headless-bi) platform, and the latter serves as the data visualization and exploration platform. However, being open source and standing on the shoulders of giants are among many things that they have in common.

Both tools are licensed under Apache License 2.0 and actively developed on GitHub. Over the last 3 years, Cube’s [repository](https://github.com/cube-js/cube.js) has acquired almost 14,000 stars (an unmatched [leader in the category](https://twitter.com/sspaeti/status/1570044606338990081)). Written mostly in Rust, Cube’s data processing and storage are based on the [Arrow DataFusion](https://github.com/apache/arrow-datafusion) query execution framework which uses [Apache Arrow](https://arrow.apache.org/) as its in-memory format. Superset has a longer history (born at Airbnb in 2015 where it replaced Tableau, joined the Apache Incubator program in 2017), has a [repository](https://github.com/apache/superset) with 48,000 stars, and is powered by [Apache ECharts](https://echarts.apache.org/en/index.html), one of the most mature and popular charting libraries. 

![Cube and Superset](https://ucarecdn.com/5f3f2313-c055-4b9e-b345-56568c53aca2/)

You’re definitely free to take and use Cube and Superset at your own discretion, including self-hosting them in your private or public cloud. You’re also free to explore and get started with [Cube Cloud](https://cube.dev/cloud) and [Preset Cloud](https://preset.io/product/), fully managed platforms with freemium pricing models and plenty of quality of life features on top of open source products. Compare that to Looker’s “[request a demo](https://cloud.google.com/resources/looker-demo/?_ga=2.157282873.347220352.1663670208-344442256.1663670208)” paywall that would cost your team a few weeks of qualification calls on Google Meet before you start building.

## Simple as in “separation of concerns”

Cube is intentionally [headless](https://cube.dev/blog/headless-bi) and agnostic to the data presentation layer. However, it does a number of things and does them well:

- connects to a plethora of [data sources](https://cube.dev/docs/config/databases), from cloud data warehouses like BigQuery, Snowflake, and Redshift to streaming platforms like Kafka and Redpanda;
- provides a [data modeling](https://cube.dev/docs/schema/getting-started) layer as the centralized source of truth for metrics definitions;
- secures data with its [access control](https://cube.dev/docs/security) layer;
- makes sure any query runs well under a second with its [caching](https://cube.dev/docs/caching) layer;
- delivers data to any downstream tool, including BI tools like Superset, via its API layer that provides [SQL API, REST API, and GraphQL API](https://cube.dev/docs/config/downstream#ap-is-references).

Superset works by connecting to a data source and querying the data where it lives. The ideal way to use Superset is with a [dataset-centric approach](https://preset.io/blog/dataset-centric-visualization/) to modeling and visualization. 

A dataset-centric approach implies that a well-normalized dataset is used to “derive” richer datasets with extra semantics, making them valuable for analysis and visualization. By creating derived datasets that incorporate aggregate metric calculations earlier in the ETL/ELT process *before* surfacing for visualization, many of the issues with change management, maintenance, logic reuse within dashboards, etc. are significantly reduced for a better experience. 

Cube complements Superset in this way, and this allows for Superset-generated SQL queries to work with no issues when connected to a Cube environment (which is treated as a PostgreSQL database connection). 

![Looker vs Cube + Superset](https://ucarecdn.com/59031c52-890f-402f-9400-0d0d7034c887/)

You’re free to configure and fine-tune each tool and each layer within every tool when building with Cube and Superset. Want to build and schedule a report? Check Superset dashboards. Need to expose new metrics in that report? Cube’s data modeling layer is at your service.

## Compatible as in “no vendor lock-in”

Speaking of data modeling, Cube provides a [data modeling language](https://cube.dev/blog/open-source-data-modeling) that is not tied to any specific interface, visualization, or user experience. Unlike proprietary LookML definitions that require Looker IDE for syntax highlighting and linting, Cube’s data model is written in JavaScript (and [soon-to-be-written](https://forms.gle/4CZRRKK8nD4PvtxZA) in YAML) and feels native in your editor of choice. On top of that, you are able to generate a data model dynamically or even fetch it from a remote endpoint.

Consider an excerpt of Looker view from the recent [deep dive](https://pedram.substack.com/p/what-is-the-metrics-layer) by Pedram:

```yaml
view: workspace_activation {
  sql_table_name: "METRICS"."WORKSPACE_ACTIVATION" ;;

  dimension_group: date {
    type: time
    timeframes: [
      raw,
      time,
      date,
      week,
      month,
      quarter,
      year
    ]
    sql: ${TABLE}."REPORTING_DATE" ;;
  }

  dimension: is_active_workspace {
    type: yesno
    sql: ${TABLE}."IS_ACTIVE_WORKSPACE" ;;
  }

  dimension: workspace_id {
    type: string
    primary_key: yes
    sql: ${TABLE}."WORKSPACE_ID" ;;
  }

  measure: count_workspaces {
    type: count_distinct
    description: "# of Workspaces"
    sql: ${workspace_id} ;;
  }

  measure: count_active_workspaces {
    type: count_distinct
    description: "# of Unique Workspaces Active within 1 Day"
    sql: ${workspace_id} ;;
    filters: [is_active_workspace: "yes"]
  }

  measure: activation_rate {
    type: number
    sql: ${count_active_workspaces} / ${count_workspaces} ;;
    value_format_name: percent_1
  }
}
```

Compare with the equivalent Cube data model (way less of `;;`, to say the least):

```javascript
cube(`WorkspaceActivation`, {
  sql: `SELECT * FROM public.active_workspace_details`,

  measures: {
    count: {
      type: `count`
    },

    n_workspaces: {
      sql: `workspace_id`,
      type: `countDistinct`,
    },

    n_active_workspaces: {
      sql: `workspace_id`,
      type: `countDistinct`,
      filters: [ {
        sql: `${is_active_workspace} IS TRUE`
      } ],
    },

    activation_rate: {
      sql: `ROUND(100 * (1.0 * ${n_active_workspaces} / ${n_workspaces}), 2)`,
      type: `number`
    }
  },

  dimensions: {
    workspace_id: {
      sql: `workspace_id`,
      type: `number`,
      primaryKey: true
    },

    reporting_day: {
      sql: `reporting_day`,
      type: `time`
    },

    is_active_workspace: {
      sql: `is_active`,
      type: `boolean`
    }
  },

  preAggregations: {
    main: {
      measures: [ activation_rate ],
      timeDimension: reporting_day,
      granularity: `week`,
      refreshKey: {
        every: `1 day`
      }
    }
  }
});
```

In a similar fashion, Cube’s API layer is compatible with practically any data consumer. When the time comes, and you realize that, alongside business users using dashboards and reports in Superset, you’d like to have data analysts crunching numbers and seeking insights in data notebooks, Cube will be there to help liberate your data.

![Data consumers](https://ucarecdn.com/b35f44ef-12a5-42d7-a618-74637fc3e0cd/)

You can expect any SQL query generated by your data exploration tool like Superset to work with Cube’s SQL API. Needless to say, equivalent queries to Cube’s REST API and GraphQL API will yield the same data:

```sql
SELECT
  DATE_TRUNC('WEEK', reporting_day) AS reporting_week,
  activation_rate
FROM WorkspaceActivation
ORDER BY 1 DESC;
```

Let’s have a bird’s-eye view here.

The generation of BI tools like Looker encouraged end-users to invest heavily in building out tons of LookML models to populate the semantic layer. While this established Looker as a source of truth in an organization for business metrics, it also created an immense amount of lock-in in your BI tool. You couldn’t take your LookML models with you and use them with another tool if you decided to switch BI tools. Google’s acquisition of Looker accelerated organizations’ anxieties around this lock-in, to the point that Google was actually [forced to integrate LookML with Tableau](https://roundup.getdbt.com/p/look-ml-tableau-metrics-layer). This way, organizations could use LookML for transformation (Looker’s strength) and Tableau for visualization (Tableau’s strength).

The future however is looking to be very different from the past:

- Open-source headless BI platforms like Cube provide unified semantic layers to sit between the database and BI later, eliminating the need, or providing alternate implementations, for expensive solutions like Looker that lock you in.
- Open-source BI tools like Superset with an intentionally thin semantic layer complement Cube and enable *last-mile data transformation* for the explicit purpose of data visualization in your BI tool.

When the need for a complex yet flexible metrics/semantic layer arises, Cube and Superset is a winning open source-based combo!

## Ready-to-go as in “today”

Now, you’re very much welcome to try Cube and Superset today and see them in action. Please join the Slack communities of [Cube](https://slack.cube.dev) and [Superset](https://join.slack.com/t/apache-superset/shared_invite/zt-16jvzmoi8-sI7jKWp~xc2zYRe~NqiY9Q) to share your feedback.

Please tune in to the [webinar on October 4](https://cube.dev/events/replacing-looker-with-cube-and-superset) with [Shreesham Mukherjee](https://www.linkedin.com/in/shreesham/), Developer Relations Engineer at Preset, and [Igor Lukanin](https://www.linkedin.com/in/igorlukanin/), Head of Developer Relations at Cube. We’ll discuss Cube and Superset as an open source alternative to Looker in popular use cases, demo both tools, and take your questions.

See you!

Shreesham Mukherjee

Looker

Respectfully unbundling the elephant in the BI room.

Open source Looker alternative with Cube and Apache Superset

Igor Lukanin

Apache Superset

Building dashboards over a semantic layer with Superset and Cube

In this tutorial, we'll learn how to build a metrics dashboard with Apache Superset, a modern and open-source data exploration and visualization platform. We'll also use Cube, an open-source metrics store, as the data source for Superset that will enable our dashboards to load in under a second — quite the opposite to what you'd usually expect from a BI tool, right?

Here's how the end result will look like:

![Screenshot 2021-11-24 at 05.04.36.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/11d780fa-b9b3-45d2-af61-e1f3ce270731.png)

Now we're all set. Let's see what's on the shelves of this metrics store 🏪

## What is Apache Superset?

[Apache Superset](https://superset.apache.org) is a data exploration and visualization platform or, in layman's terms, a tool that you can use to build dashboards with charts for internal users. Born at a hackathon at Airbnb back in 2015, with more than 41,000 stars on GitHub, now it's a leading open-source business intelligence tool.

Superset has connectors for numerous databases, from Amazon Athena to Databricks to Google BigQuery to Postgres. It provides a web-based SQL IDE and no-code tools for building charts and dashboards.

**Running Superset.** Now let's run Superset to explore these features. To keep things simple, we'll run a fully managed Superset in [Preset Cloud](https://preset.io), where you can use it forever for free on the Starter plan. (If you'd like to run Superset locally with Docker, please see [these instructions](https://github.com/cube-js/cube.js/blob/master/examples/superset/README.md#running-superset-locally).)

First, please proceed to the [sign up page](https://preset.io/registration/) and fill in your details. Note that Preset Cloud supports signing up with your Google account. Within a few seconds you will be taken to your account with a readily available workspace:

![Screenshot 2021-11-24 at 02.50.55.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/8cc1e93b-3cd2-4232-b723-8f3b99f42324.png)

Switching to that workspace will reveal a few example dashboards that you can review later.

![Screenshot 2021-11-24 at 02.54.51.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/91134cc7-13e7-46de-b924-75c606c9fbbd.png)

Now, let's navigate to `Data / Databases` via the top menu and... Oops! We need a metrics store to connect to. Let's see how Cube can help us build one.

## What is Cube?

[Cube](https://cube.dev/) is an open-source [metrics store](https://cube.dev/blog/introducing-cube-sql/) with nearly 12,000 stars on [GitHub](https://github.com/cube-js/cube.js) to date. It serves as a single source of truth for all metrics and provides APIs for powering BI tools and building data apps. You can configure Cube to connect to any database, define your metrics via a declarative data schema, and instantly get an API that you can use with Superset or many other BI tools.

**Running Cube.** Similarly to Superset, let's run a fully managed Cube in [Cube Cloud](http://cube.dev/cloud) that has a free plan as well. (If you'd like to run Cube locally with Docker, please see [these instructions](https://github.com/cube-js/cube.js/blob/master/examples/superset/README.md#running-cube-locally).)

First, please proceed to the [sign up page](https://cubecloud.dev/auth/signup) and fill in your details. Note that Cube Cloud supports signing up with your GitHub account. Within a few seconds you will be taken to your account where you can create your first Cube deployment:

![Screenshot 2021-11-24 at 03.06.16.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/1ccba445-9cbf-4bb6-acfe-1e3c1d2fa325.png)

Proceed with providing a name for your deployment, selecting a cloud provider and a region:

![Screenshot 2021-11-24 at 03.21.33.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/28ad847d-10f6-47ed-8bf5-66a2b441eef3.png)

At the next step, choose `Create` to start a new Cube project from scratch. Then, pick `Postgres` to proceed to the screen where you can enter the following credentials:

```
Hostname:  demo-db-examples.cube.dev
Port:      5432
Database:  ecom
Username:  cube
Password:  12345
```

![Screenshot 2021-11-24 at 03.22.40.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/835c5600-7f79-44b3-81c1-3726a787c440.png)

Cube will connect to a publicly available Postgres database that I've already set up.

The last part of configuration is the [data schema](https://cube.dev/docs/schema/getting-started) which declaratively describes the metrics we'll be putting on the dashboard. Actually, Cube can generate it for us! Pick the top-level `public` database from the list:

![Screenshot 2021-11-24 at 03.22.55.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/c721028f-e20c-425c-900d-75b9f294a3bc.png)

In a while, your Cube deployment will be up and running:

![Screenshot 2021-11-24 at 03.24.31.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/5c39e57e-3311-4f40-9b89-8bc40d49df00.png)

**Defining metrics.** Please navigate to the "Schema" tab. You will see files like `LineItems.js`, `Orders.js`, `Users.js`, etc. under the "schema" folder.

![Screenshot 2021-11-24 at 03.25.51.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/8fb089b2-f33a-4795-9450-9f564dbd40d4.png)

Let's review `LineItems.js` which defines the metrics within the "LineItems" cube. This file is different from the one in Cube Cloud, but we'll take care of that later.

```jsx
cube(`LineItems`, {
  sql: `SELECT * FROM public.line_items`,

  measures: {
    count: {
      type: `count`
    },

    price: {
      sql: `price`,
      type: `sum`
    },

    quantity: {
      sql: `quantity`,
      type: `sum`
    },

    // A calculated measure that reference other measures.
    // See https://cube.dev/docs/schema/reference/measures#calculated-measures
    avgPrice: {
      sql: `${CUBE.price} / ${CUBE.quantity}`,
      type: `number`
    },

    // A rolling window measure.
    // See https://cube.dev/docs/schema/reference/measures#rolling-window
    revenue: {
      sql: `price`,
      type: `sum`,
      rollingWindow: {
        trailing: `unbounded`,
      },
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primaryKey: true
    },

    createdAt: {
      sql: `created_at`,
      type: `time`
    }
  },

  dataSource: `default`
});
```

Key learnings here:

- the cube is a logical entity that groups measures and dimensions together
- using the `sql` statement, this cube is defined over the entire `public.line_items` table; actually, cube can be defined over an arbitrary SQL statement that selects data
- measures (quantitative data) are defined as aggregations (e.g., `count`, `sum`, etc.) over columns in the dataset
- dimensions (qualitative data) are defined over textual, numeric, or temporal columns in the dataset
- you can define complex measures and dimensions with custom `sql` statements or references to other measures

**Development mode.** Now, let's update the schema file in Cube Cloud to match the contents above. First, click `Enter Development Mode` to unlock the schema files for editing. This essentially creates a "fork" of the Cube API that tracks your changes in the data schema.

Navigate to `LineItems.js` and replace its contents with the code above. Then, save your changes by clicking `Save All` to apply the changes to the development version of your API. You can apply as many changes as you wish, but we're done for now. Click `Commit & Push` to merge your changes back to the main branch:

![Screenshot 2021-11-24 at 03.33.47.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/85eeca4a-e85f-4109-8459-4dbde074d005.png)

On the "Overview" tab you will see your changes deployed:

![Screenshot 2021-11-24 at 03.37.57.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/5e7746a3-8839-47e3-bddc-a8b67460207a.png)

Now you can explore the metrics on the "Playground" tab:

![Screenshot 2021-11-24 at 03.41.38.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/df9a8062-4026-4476-8c65-b10e1360075e.png)

Good! We've built a metrics store that we can connect to Superset. How?

Please go back to the "Overview" tab and click `How to connect`. The "SQL API" tab will have a toggle that enables the API for Superset and other BI tools. Turning it on will provide you with all necessary credentials:

![Screenshot 2021-11-24 at 03.46.54.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/e2751eeb-c7bb-4f8d-9ff9-d6184097e9ce.png)

Now, let's build a dashboard!

## Building a dashboard in Superset

We'll need to go through a few steps:

- [connect Superset to Cube](https://cube.dev/docs/recipes/using-apache-superset-with-cube-sql)
- define the datasets
- create charts and add them to a dashboard

Let's go!

![Screenshot 2021-11-24 at 03.44.32.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/dee5d4cc-df2a-47a8-a812-9e54aef57e5d.png)

**Connect Superset to Cube.** Switch back to the workspace we've created earlier. Then, navigate to `Data / Databases` via the top menu, click `+ Database`, select `MySQL`, and fill in the credentials from your Cube Cloud instance — or use the credentials below:

- Host: `aquamarine-moth.sql.aws-us-east-2.cubecloudapp.dev`
- Port: `3306`
- Database name: `db`
- Username: `cube@aquamarine-moth`
- Password: `6300005f8da3ef74fa64a5bf9b1b6fcd`
- Display name: `Cube Cloud` (it's important)

![Screenshot 2021-11-24 at 03.53.07.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/42812095-82d4-4a65-a918-ab173c45689e.png)

You can press `Connect` now.

**Define the datasets.** Navigate to `Data / Datasets` via the top menu, click `+ Dataset`, and fill in the following credentials:

- Database: `Cube Cloud` (the one we've just created)
- Schema: `db`
- See table schema: `LineItems`

![Screenshot 2021-11-24 at 03.58.02.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/d8e50fc9-4df7-425b-b57b-f3904de984e0.png)

You can press `Add` now. Then, please repeat this for `Users` and `Orders`.

**Create charts and a dashboard.** We'll take a leap and create everything in a single step.

In Superset, you can export a dashboard with all charts as a JSON file and import it later. Navigate to `Dashboards` and click the link with an icon on the right:

![Screenshot 2021-11-24 at 04.01.31.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/e2ea0cbd-1cd0-439d-8c4d-a36d9e61b0c7.png)

Download [this file](https://raw.githubusercontent.com/cube-js/cube.js/master/examples/superset/acme-dashboard.json) to your machine, select it, and click `Import`:

![Screenshot 2021-11-24 at 04.02.38.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/5256114e-d4a4-4e4d-96b0-137aee522bcb.png)

Whoa! Now we have a complete dashboard for Acme, Inc. Click on it to view:

![Screenshot 2021-11-24 at 05.04.36.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/11d780fa-b9b3-45d2-af61-e1f3ce270731.png)

Looks nice, doesn't it? Let's explore what's under the hood and how you can build it on your own.

## Diving deep into Superset

**Anatomy of a dashboard.** You can see that charts on the dashboard are aligned by the grid. To rearrange them, click on the pencil icon in the top right corner. You can add tabs, headers, dividers, Markdown blocks, etc. Of course, you can also add charts.

![Screenshot 2021-11-24 at 05.06.41.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/f8905469-8f64-4e3d-85d0-e7ae283673f3.png)

**The simplest chart.** Navigate to `Charts` via the top menu and click on any chart with the `Big Number` visualization type, e.g., Customers. In my opinion, that's the simplest chart you can create in Superset. It contains a single metric, and I doubt a chart can get simpler than that.

![Screenshot 2021-11-24 at 05.07.29.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/afde957b-bdc9-4083-a704-11006c8805f1.png)

Let's dissect how a chart is defined:

- Visualization type: `Big Number` — that's where you can select or change the chart type
- Time column: `createdAt` — interestingly enough, any chart should have this time column defined even if the displayed data has no temporal components
- Metric: `COUNT(*)` — that's the most important part of any chart configuration; upon clicking on this metric, you'll see that you can either select a saved definition, "simply" select a column and an aggregation, or write a "custom SQL" expression

When all config options are set, press `Run` to fetch the data, then `Save` to persist a chart or add it on a dashboard (no need to do it now, it's already added).

**A less simple chart.** Navigate to `Charts` via the top menu and click on any chart with the `Big Number with Trendline` visualization type, e.g., Revenue. Still, it contains a single metric as well as a sketchy chart in the bottom.

![Screenshot 2021-11-24 at 05.08.31.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/103ea21b-ef9b-4e17-b9a9-580d24a271b1.png)

Let's dissect how this chart is defined (only new options):

- Time grain: `Month` — defines the temporal granularity for metrics calculations
- Time range: `Last year` — specifies the date range for this chart
- Metric: `revenue` — it's interesting; click on this metric to learn that it's defined using "custom SQL"; that's because the aggregation has already been performed by Cube, no need to aggregate aggregated values, right?

**Other charts.** Actually, now you know everything you need to explore and dissect other charts. Just keep in mind that Superset has plenty of customizations you can apply to charts — see the "Customize" tab for inspiration.

![Screenshot 2021-11-24 at 05.09.15.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/eec079db-5c09-42be-822e-5ab583ce016d.png)

**Viewing SQL.** For any chart, you can reveal the SQL query to Cube which is generated by Superset to fetch the data. Press the burger button (with triple horizontal lines) in the top right corner, then `View query`. Good ol' SQL, nice:

![Screenshot 2021-11-24 at 05.09.42.png](https://cubedev-blog-images.s3.us-east-2.amazonaws.com/68e680a9-3826-4597-8e21-934f901779c3.png)

Also, if you wanna use the aforementioned SQL IDE, navigate to `SQL Lab / SQL Editor` via the top menu.

## Making business intelligence fast ⚡

There's only one thing left to explore, but it's a huge one.

Let's navigate back to the Acme, Inc. dashboard. It takes 2-3 seconds to load, and the infinity-shaped spinners are clearly visible. They are not annoying, but honestly — wouldn't you like this dashboard to load instantly? Yep, well under a second.

Cube provies an out-of-the-box [caching layer](https://cube.dev/docs/caching) that allows to pre-compute and materialize the data required to serve the queries. All you need to do is define which queries should be accelerated. It's done declaratively in the data schema files. (Please also note that Superset has its own lightweight [caching layer](https://superset.apache.org/docs/installation/cache) that might be handy in cases when you need to push your Cube + Superset to the limit.)

Please go back to your Cube Cloud instance, enter the development mode, switch to the "Schema" tab, and update your data schema files with small snippets as follows.

First, `LineItems.js` should look like this:

```jsx
cube(`LineItems`, {
  sql: `SELECT * FROM public.line_items`,

  // Copy me ↓
  preAggregations: {
    main: {
      measures: [ CUBE.count, CUBE.revenue, CUBE.price, CUBE.quantity ],
      timeDimension: CUBE.createdAt,
      granularity: 'day'
    }
  },
  // Copy me ↑

  measures: {
    count: {
      type: `count`
    },

  ...
```

Second, `Orders.js` should look like this:

```jsx
cube(`Orders`, {
  sql: `SELECT * FROM public.orders`,

  // Copy me ↓
  preAggregations: {
    main: {
      measures: [ CUBE.count ],
      dimensions: [ CUBE.status ],
      timeDimension: CUBE.createdAt,
      granularity: 'day'
    }
  },
  // Copy me ↑

  measures: {
    count: {
      type: `count`
    },

  ...
```

Lastly, `Users.js` should look like this:

```jsx
cube(`Users`, {
  sql: `SELECT * FROM public.users`,

  // Copy me ↓
  preAggregations: {
    main: {
      measures: [ CUBE.count ],
      dimensions: [ CUBE.city, CUBE.gender ]
    }
  },
  // Copy me ↑

  measures: {
    count: {
      type: `count`
    }

  ...
```

Don't forget to click `Save All`, then `Commit & Push`, and check that your changes were deployed at the "Overview" tab. In the background, Cube will build the necessary caches.

It's time to get back to your dashboard and refresh it. Now refresh it one more time. See? The dashboard loads in under a second. ⚡

Of course, you have plenty of options to fine-tune the caching behavior, e.g., specify the [cache rebuilding schedule](https://cube.dev/docs/caching/using-pre-aggregations#refresh-strategy).

## Wrapping up

Thanks for following this tutorial. I encourage you to spend some time [in the docs](https://superset.apache.org/docs/intro) and explore other features of Apache Superset. Also, please check out Preset docs that are packed with great content, e.g., on [creating charts](https://docs.preset.io/docs/create-a-chart).

**Also, thanks for learning more about building a metrics store with [Cube](https://cube.dev/). Indeed, it's a very convenient tool to serve as a [single source of truth for all metrics](https://cube.dev/blog/introducing-cube-sql/).**

Please don't hesitate to like and bookmark this post, write a comment, and give a star to [Cube](https://github.com/cube-js/cube.js) and [Superset](https://github.com/apache/superset) on GitHub. I hope these tools would be a part of your toolkit when you decide to build a metrics store and a business intelligence application on top of it.

Good luck and have fun!

Apache Superset

Open source Looker alternative with Cube and Apache Superset

Building dashboards over a semantic layer with Superset and Cube

Building a metrics dashboard with Superset and Cube