Sigma on Databricks: The ultimate guide

Sigma on Databricks is a modern way to deliver self-service analytics directly on your Databricks data without extracts, copies, or delays.

Many teams already use Databricks to store and process large volumes of data. The challenge comes when business users need fast, simple access to that data. Traditional BI tools often require data extracts, scheduled refreshes, or complex semantic layers. This leads to stale dashboards and slow insights.

Sigma computing solves this problem by acting as a cloud-native analytics layer that connects directly to Databricks. Sigma runs live queries on your Databricks SQL warehouses and turns them into interactive tables, charts, and dashboards using a spreadsheet-style interface. There is no data duplication and no separate analytics database.

This guide explains how to deploy Sigma on Databricks step by step. You will learn how the Sigma and Databricks integration works, how to configure compute and security, and how to prepare your data so Sigma performs well at scale. The focus is practical and technical, not marketing.

By the end of this guide, you will understand:

How Sigma connects directly to Databricks
What you need to configure before deployment
How to avoid common setup mistakes
How to get fast, governed analytics on Databricks data

Let's get started…

What is Databricks?

Databricks is a cloud-based data platform used to store, process, and analyze large volumes of data. It brings data engineering, analytics, and AI together in one system so teams do not need separate tools for each task.

Databricks is often described as a Lakehouse platform. This means it combines the flexibility of a data lake with the performance of a data warehouse. Raw data, cleaned data, and business-ready data can all live in the same platform while still supporting fast SQL queries.

For analytics, Databricks provides Databricks SQL. This includes SQL Warehouses, which are the compute engines that run SQL queries. Business intelligence tools connect to these SQL warehouses to query data. When you deploy Sigma on Databricks, Sigma sends all queries to these warehouses and Databricks handles the processing.

Databricks is widely used for analytics because it scales easily from small datasets to billions of rows. It separates storage from compute, which helps control cost as usage grows. It also uses Delta Lake, a storage format that improves reliability and query performance. Governance is handled through Unity Catalog, which controls who can access which data.

In a Sigma and Databricks integration, Databricks stays responsible for running queries, enforcing security, and managing data. Sigma computing then sits on top as the analytics layer, giving users an easy way to explore and analyze Databricks data without moving it.

What is Sigma Computing?

Sigma computing is a cloud-native analytics platform built to help people explore data without writing code. It uses a spreadsheet-style interface that feels familiar, but runs directly on cloud data platforms like Databricks.

Sigma does not copy or extract data. Instead, it connects straight to Databricks and sends live SQL queries to Databricks SQL Warehouses. Every table, chart, or filter you create in Sigma is translated into optimized SQL behind the scenes. This is a key part of the Sigma and Databricks integration.

Sigma is designed for both technical and non-technical users. Data teams can model and govern data, while business users can explore it safely. Everyone works on the same live data, which removes delays and version conflicts.

When you run Sigma on Databricks, Sigma:

Generates SQL automatically based on user actions
Pushes computation down to Databricks
Reads data directly from Delta tables
Respects Databricks security and governance rules

Databricks remains responsible for compute performance and data security. Sigma focuses only on analytics and usability. This clear split makes Sigma computing fast, scalable, and easier to manage than traditional BI tools.

Because Sigma queries data in real time, users always see the latest data. There are no scheduled extracts, no cached data copies, and no separate semantic layer to maintain. This is why Sigma works especially well for analytics on fast-changing Databricks data.

Sigma & Databricks integration: how does it work?

The Sigma and Databricks integration is built around a clear separation of responsibilities. Databricks handles data storage, security, and compute. Sigma on Databricks focuses on analytics and user interaction.

Sigma connects directly to Databricks SQL Warehouses using a native connector. When a user opens a table, applies a filter, or builds a chart in Sigma computing, Sigma generates SQL automatically. That SQL is sent to Databricks, where it runs on the warehouse compute. The results are then returned to Sigma and shown to the user.

There is no data extract, cache copy, or intermediate analytics database. All queries run on live Databricks data. This ensures that dashboards and reports always reflect the most current data available.

Governance is shared through Unity Catalog. Sigma respects the same permissions defined in Databricks. If a user does not have access to a table or column in Databricks, they will not see it in Sigma. This keeps security consistent across the platform.

This architecture works well because:

Compute stays centralized in Databricks
Analytics logic lives in Sigma
Security rules are enforced once, in Databricks
Performance scales as Databricks warehouses scale

By running Sigma on Databricks, teams avoid data duplication, reduce maintenance overhead, and deliver fast, governed analytics to business users.

Sigma on Databricks: Prerequisites before deployment

Before you deploy Sigma on Databricks, both platforms must be set up correctly. Skipping these basics often leads to slow performance, failed connections, or security issues later.

On the Databricks side, you need an active workspace with Databricks SQL enabled. You must have access to at least one SQL Warehouse, or the ability to create one. Unity Catalog should be enabled so governance and permissions can be shared cleanly with Sigma computing.

On the Sigma computing side, you must have an account with Admin access. Admin access is required to create connections, manage authentication, and control dataset permissions. Without admin rights, Sigma cannot be properly connected to Databricks.

You also need to decide how Sigma will authenticate with Databricks. There are two supported options:

Personal Access Tokens (PATs), often used by smaller teams or early deployments
OAuth, typically used by larger organizations with single sign-on

This decision affects how users are authenticated and how access is managed over time. For scalable deployments, Sigma and Databricks integration works best when authentication and permissions are planned before connecting the tools.

Having these prerequisites in place ensures that Sigma can connect securely, query data reliably, and inherit Databricks governance from day one.

How to integrate Sigma and Databricks: Step by step guide

Configure Databricks SQL warehouses for Sigma

Configuring Databricks SQL Warehouses correctly is the most important step when running Sigma on Databricks. These warehouses provide the compute power that Sigma computing uses to run all queries.

Databricks offers three types of SQL warehouses: Serverless, Pro, and Classic. For the Sigma and Databricks integration, Serverless SQL Warehouses are strongly recommended. Serverless warehouses start instantly, scale automatically, and remove the delays that occur when compute needs to spin up. This results in faster dashboards and a better user experience.

Warehouse size should be chosen based on the size of the data being queried. Smaller tables with around one million rows can usually start with a small warehouse. As tables grow into hundreds of millions or billions of rows, larger warehouse sizes are required. This sizing can be adjusted over time as usage grows.

Autoscaling should be enabled so the warehouse can handle multiple users at once. Autoscaling increases the number of clusters when query demand rises and reduces them again when demand drops. This balances performance and cost. Autostop should also be enabled so warehouses shut down when idle and restart automatically when Sigma sends a query.

For best results, Sigma recommends using dedicated SQL warehouses. This means the warehouse is used only by Sigma and not shared with other tools. Dedicated warehouses make performance more predictable and simplify cost monitoring.

Correct warehouse configuration ensures that Sigma on Databricks remains fast, reliable, and cost-efficient as more users start exploring data.

Sigma and Databricks integration – authentication setup

Authentication controls how Sigma computing connects securely to Databricks. A correct setup ensures users only see data they are allowed to access and that queries run reliably at scale.

Sigma connects to Databricks using its native connector. Every connection requires the Databricks Host Name and HTTP Path of the SQL Warehouse. On top of this, you must choose an authentication method. The two supported options are Personal Access Tokens (PATs) and OAuth.

Personal Access Tokens are commonly used for smaller teams or early deployments. A best practice is to create a Databricks service principal, assign it to the correct groups, and generate a token for that service principal. Sigma then uses this token to query Databricks. This approach avoids tying the connection to a single person and simplifies long-term management.

OAuth is typically used by larger organizations with single sign-on. Each user authenticates individually through the identity provider. This provides stronger security but requires that every Sigma user also has permission to access the Databricks SQL warehouse and data objects.

For most teams, planning authentication early is critical. The wrong choice can lead to access issues, failed queries, or high admin overhead later. When designed correctly, the Sigma and Databricks integration inherits Databricks security while keeping analytics simple for end users.

Grant permissions with Databricks Unity Catalog

Permissions control what Sigma computing can see and do inside Databricks. The recommended way to manage this for Sigma on Databricks is through Unity Catalog.

Unity Catalog is Databricks’ centralized governance layer. It manages access to SQL Warehouses, catalogs, schemas, tables, and views in one place. Sigma integrates directly with Unity Catalog and inherits its security rules. This means you only need to define permissions once, in Databricks.

Permissions should be assigned to groups, not individual users. Groups can include Databricks users and service principals. This makes the Sigma and Databricks integration easier to scale as more users are added.

You must grant permissions in three main areas. First, Sigma needs permission to use the Databricks SQL Warehouse. At minimum, the relevant group must have Can Use access on the warehouse so Sigma can run queries.

Second, Sigma needs access to data objects. Depending on your use case, this access can be granted at the catalog, schema, or table level. Common read permissions include USE CATALOG, USE SCHEMA, and SELECT. Granting access higher in the hierarchy automatically passes permissions down to lower objects.

Third, if you plan to use write features such as materialization, input tables, or CSV uploads, additional write permissions are required. These include MODIFY, CREATE TABLE, and CREATE MATERIALIZED VIEW. These permissions should be tightly controlled to avoid unintended data changes.

By using Unity Catalog with group-based permissions, Sigma respects Databricks governance while still giving users flexible access to analytics. This ensures that Sigma on Databricks remains secure, auditable, and easy to manage.

Prepare Databricks data for analytics with Sigma

Data structure has a direct impact on how well Sigma on Databricks performs. Even with the right warehouse setup, poor data modeling can lead to slow dashboards and expensive queries.

Databricks recommends using the Medallion Architecture. This approach organizes data into three layers. Bronze tables store raw ingested data. Silver tables contain cleaned and transformed data. Gold tables hold curated, business-ready data designed for analytics.

Sigma computing should query Gold-level tables only. These tables are smaller, cleaner, and optimized for reporting. Querying raw or Silver data increases compute cost and slows down the Sigma and Databricks integration.

All analytics tables should be stored as Delta tables. Delta Lake adds transaction support, schema enforcement, and performance optimizations on top of cloud storage. Compared to CSV or non-Delta formats, Delta tables return query results much faster, especially at scale.

For large or frequently queried tables, Databricks provides additional optimization tools. OPTIMIZE compacts small files into larger ones, reducing the amount of data scanned per query. Z-ORDER physically reorganizes data based on commonly filtered columns, which helps Databricks skip irrelevant data during queries.

Preparing data this way ensures Sigma generates efficient SQL and Databricks executes it quickly. This is one of the most important steps for making Sigma on Databricks fast and reliable for business users.

Writeback and materialization options with Sigma

Sigma computing includes optional write features that allow data to be written back into Databricks. These features are not required for every deployment, but they can improve performance and enable advanced workflows when used correctly with Sigma on Databricks.

Sigma supports several writeback use cases. Users can upload CSV files directly from Sigma, create input tables for manual data entry, and materialize datasets. All of these actions write data back into Databricks and make it available for querying.

To enable writeback, a dedicated writeback schema must be created in Databricks. This schema is separate from analytical schemas and has its own permissions. Only users or service principals that need write access should be granted permissions such as CREATE TABLE, MODIFY, and CREATE MATERIALIZED VIEW. This keeps the Sigma and Databricks integration secure and controlled.

Materialization is especially useful for performance. When a Sigma dataset becomes complex or slow to run, it can be materialized. This means Sigma runs the query on a schedule and stores the result as a table in Databricks. Future queries then read from this table instead of re-running the full logic every time.

Materialization works best when:

The underlying data does not change constantly
Users query the same logic many times
Query execution time is high

As materialized datasets grow larger or more complex, Databricks-native pipelines may be a better long-term solution. In that case, Sigma is used for analytics and Databricks handles transformations upstream.

Used carefully, writeback and materialization make Sigma on Databricks faster and more flexible without increasing operational complexity.

Performance and cost optimization

Performance and cost are tightly linked when running Sigma on Databricks. Every action in Sigma computing triggers SQL that runs on Databricks, so optimizing both sides is essential.

On the Databricks side, performance starts with well-modeled Delta tables and correctly sized SQL Warehouses. Features like OPTIMIZE and Z-ORDER reduce the amount of data scanned per query, which lowers execution time and compute cost. Autoscaling ensures enough compute is available during peak usage without paying for unused resources.

Databricks pricing is based on Databricks Units (DBUs). DBUs measure how much compute a workload consumes. SQL Warehouses consume DBUs based on their size and how long they run. Using autostop prevents idle warehouses from continuing to generate cost when Sigma is not actively querying data.

On the Sigma computing side, several built-in optimizations reduce Databricks load. Sigma uses multiple layers of caching so repeated queries do not always hit Databricks. When possible, Sigma also evaluates simple calculations in the browser instead of issuing a new SQL query. This reduces query volume and improves response time.

Materialized datasets also play a key role in cost control. Instead of running expensive queries repeatedly, Sigma runs them on a schedule and stores the result in Databricks. Users then query the smaller, precomputed table, which uses fewer DBUs.

Monitoring is critical once the Sigma and Databricks integration is live. Databricks provides usage reports that show DBU consumption by warehouse, cluster, and workload. Cloud provider tools can be used alongside Databricks to set budgets and alerts when spend increases.

By combining Databricks optimization techniques with Sigma’s caching and materialization features, teams can scale Sigma on Databricks to hundreds of users while keeping performance high and costs predictable.

Sigma on Databricks: common pitfalls to avoid

Many issues with Sigma on Databricks come from small configuration mistakes made early. Avoiding these problems makes the Sigma and Databricks integration more stable, faster, and easier to scale.

One common mistake is using Pro or Classic SQL Warehouses instead of Serverless. These warehouses need time to start up, which causes slow dashboards and poor user experience in Sigma computing. Serverless warehouses start instantly and handle concurrency much better.

Another frequent issue is allowing Sigma to query raw or Silver-layer data. These tables are often large, unclean, and not designed for analytics. Querying them increases cost and slows performance. Sigma should always query Gold-level Delta tables built specifically for reporting.

Teams also run into problems by overusing custom SQL inside Sigma. While custom SQL is supported, Sigma’s auto-generated SQL is already optimized for Databricks. Heavy use of custom SQL makes models harder to maintain and can reduce performance.

Sharing SQL Warehouses across multiple tools is another mistake. When Sigma competes with other workloads for the same warehouse, performance becomes unpredictable. Dedicated warehouses give Sigma on Databricks consistent response times and clearer cost tracking.

Finally, many teams skip concurrency planning. As more users access Sigma computing, queries run at the same time. Without autoscaling enabled, performance degrades quickly. Planning for concurrent usage upfront prevents future bottlenecks.

Avoiding these mistakes helps ensure Sigma delivers fast, reliable analytics on top of Databricks from day one.

Sigma on Databricks: when does it make sense?

Sigma on Databricks is a strong choice when teams want fast analytics without copying or moving data. It works best in environments where Databricks is already the central data platform.

This architecture makes sense when business users need direct access to live Databricks data but do not want to write SQL. Sigma computing provides a familiar spreadsheet-style interface while still running queries directly on Databricks SQL Warehouses.

It is also a good fit when governance is important. Because the Sigma and Databricks integration relies on Unity Catalog, security rules are defined once in Databricks and enforced everywhere. This reduces risk and simplifies audits.

Sigma on Databricks is especially useful when:

Data changes frequently and dashboards must always be up to date
Teams want to avoid data extracts and scheduled refreshes
Multiple departments need self-service analytics on the same data
Performance must scale from a few users to hundreds

In these situations, Databricks provides the scale and compute, while Sigma focuses on analytics and usability. Together, they form a modern analytics stack that is easier to maintain than traditional BI architectures.

Conclusion

Deploying Sigma on Databricks gives teams a direct way to explore and analyze data without extracts, delays, or duplicated datasets. Databricks provides the scale, performance, and governance. Sigma computing adds an easy-to-use analytics layer that runs directly on top of that foundation.

A successful Sigma and Databricks integration depends on a few key decisions. SQL Warehouses must be configured correctly, with Serverless compute, autoscaling, and autostop enabled. Authentication and permissions should be planned early using Unity Catalog and group-based access. Data must be modeled into Gold-level Delta tables so analytics queries stay fast and cost-efficient.

When these best practices are followed, Sigma on Databricks scales smoothly as usage grows. Business users get real-time access to trusted data, and data teams spend less time maintaining BI infrastructure.

Used together, Sigma and Databricks form a modern analytics architecture that delivers governed self-service analytics on live data.