How to Connect Databricks to Google Sheets (Without CSV Exports)

Why CSV exports from Databricks don't scale

Before diving into solutions, it's worth understanding exactly why the CSV workflow breaks down – because the pain isn't just the export itself.

The manual export trap

The typical process looks like this: an analyst receives a Slack message asking for "the latest campaign numbers in a Sheet." They open Databricks, run or re-run a SQL query, click Export, download a CSV, open Google Sheets, paste the data, fix column formats, add any calculated columns, and share the link. Total time: 15–30 minutes for a single report refresh.

That time compounds fast. If the same analyst maintains 8–10 regularly updated Sheets for different stakeholders, they're spending several hours per week on what is essentially a copy-paste operation. And every manual step introduces error risk – wrong date range, stale filter, misaligned columns.

What breaks when your team grows

The real problem isn't one analyst refreshing one Sheet. It's when 15 stakeholders across marketing, finance, and operations all need different views of Databricks data in Google Sheets – refreshed daily or weekly. Versions start to diverge. People build downstream formulas on data that was last pasted three days ago. Nobody knows which sheet has the "right" numbers.

Worse, there's zero governance. Anyone with the Google Sheets link can see everything the analyst pasted, regardless of whether they should have access to that specific data. The analyst has no way to control which columns or rows each stakeholder sees.

Three ways to connect Databricks to Google Sheets

The CSV export problem is solvable. The question is which solution fits your team's needs – and whether it covers both the analyst who builds the reports and the business user who consumes them.

Manual CSV export (the baseline)

You already know this one. Run a query in Databricks SQL, export to CSV, paste into Google Sheets. It works for one-off analysis. It fails for anything recurring because there's no automation, no refresh cycle, and no governance. Every update requires manual intervention.

Databricks native connector for Google Sheets

Databricks released its native Google Sheets connector (generally available as of May 2026). It lets users write SQL queries directly in Google Sheets, schedule data refreshes, and create pivot tables from query results.

It's a solid option for SQL-proficient users who want to pull data into Sheets themselves. 

But it has limitations: every user needs to write their own SQL, there's no reusable data layer, no governed self-service for non-SQL users, and no way to join multiple datasets without writing more complex SQL queries. 

So, if your marketing manager wants to pull campaign data blended with revenue metrics, they'll need an analyst to write that SQL for them – or learn SQL themselves. And this is a nightmare for the data team, just trust me, I know it. I feel for all the data folks when getting my hands dirty and breaking something very important. 

OWOX Data Marts – two workflows in one platform

OWOX Data Marts takes a different approach. Instead of giving every user direct SQL access to Databricks, it adds a governed data mart layer between the warehouse and the spreadsheet.

This unlocks two workflows:

• The analyst way (Push): Build reusable data marts from SQL, tables, or views → schedule automated exports to Google Sheets → share the Sheets with business users. The analyst controls the logic, schedule, and access.
• The business user way (Pull): The analyst creates joinable data marts → business users install the OWOX Data Marts Google Sheets Extension → browse available data marts → filter and pull data on demand. No SQL required. Cloud editions only.

The key difference: OWOX is the only solution where business users can self-serve without the data team giving up control over what data is accessible and how it's structured.

‍

The analyst way – scheduled exports via OWOX Data Marts

If you're the person who writes the SQL, owns the data logic, and fields Slack requests for "updated numbers" – this is your workflow. You'll build a reusable data mart on Databricks, connect Google Sheets as a destination, and schedule automated exports. Business users get fresh data in their Sheets without touching the warehouse.

Step 1 – Connect DataBricks

The first step is telling OWOX where to get your data. You'll register your Databricks workspace as a storage so we know where you data lives and you’re sure that we never see them.

To connect one, log into your OWOX project and navigate to Data Storages.

‍

Click Add Storage and select Databricks from the list of supported warehouses.

Enter your Databricks connection details

Fill in the three values you gathered in the prerequisites:

‍

‍

Click Save to verify everything works. OWOX will now use this Databricks workspace as the target for all connectors you configure.

Step 2 - Create a data mart library

A data mart library in OWOX is a container that groups related data marts together. Think of it as a project folder for a specific domain – "Marketing Analytics," "Finance Reporting," or "Product Metrics."

From the home screen - click Import… 

‍

Select the tables & views that you want to use to create data marts.

‍

Click “create N data marts”. 

Step 3 – Add meta data

When data marts are created, you need to publish them, then you’ll see the Output Schema populated with all the column Names, Types. Then you can specify the Primary Keys, Aliases (column names for Google Sheets - that are more business friendly than a typical database field) as well as the field descriptions. You can also generate all of those with AI or enter manually. 

‍

This is a single control-area for you, as a data analyst. 

With you started with the bulk data mart import, you can now build individual data marts. 

OWOX supports several data mart types, each suited to different use cases:

• SQL-based data mart – write a custom SQL query against your Databricks tables. Best for complex transformations, aggregations, and joins.
• Table-based data mart – point directly to an existing Databricks table. Best when the data is already clean and structured. (those are the ones, we already used earlier)
• View-based data mart – reference a Databricks view. Best when you want to reuse existing view logic.
• Connector-based data mart – use a pre-built OWOX connectors to pull data from ad platforms, CRMs, or other sources directly into Databricks.

For most analyst workflows, the SQL-based data mart is the go-to. 

Here's how to create one:

1. Open your library and click + New Data Mart

2. Select SQL as the Input Source

3. Write your query – for example, a campaign performance summary:

1SELECT
2  campaign_name,
3  DATE_TRUNC('week', event_date) AS week,
4  SUM(impressions) AS impressions,
5  SUM(clicks) AS clicks,
6  SUM(cost) AS cost,
7  SUM(conversions) AS conversions,
8  ROUND(
9    SUM(cost) / NULLIF(SUM(conversions), 0),
10    2
11  ) AS cpa
12FROM marketing.campaign_events
13WHERE event_date >= date_sub(current_date(), 90)
14GROUP BY
15  campaign_name,
16  DATE_TRUNC('week', event_date)
17ORDER BY week DESC, cost DESC;

‍

Click Save. 

4. Define the output schema – add column aliases, descriptions, and the data mart level description, together with Business Users, Technical Users, sharing contexts and so much more..

Note: don’t miss the descriptions as they carry through to Google Sheets as header notes, so business users know exactly what each column means.

And once again, you can generate all of the aliases & descriptions with OWOX AI.

Step 4 – Configure Google Sheets as a destination

Now connect Google Sheets so the data mart output lands in a spreadsheet automatically.

1. In your data mart settings, go to Destinations and click Add New Destination

2. Select Google Sheets

3. Authenticate using one of two methods:

• Google OAuth – simpler setup for personal use. Authorize with your Google account directly.
• Service Account JSON key – recommended for team-shared Sheets. Upload the JSON key file from Google Cloud Console. Make sure the Google Sheets API is enabled in your GCP project.

4. Specify the target spreadsheet – either create a new one or select an existing Sheet

5. Choose the target sheet tab within the spreadsheet

6. Add refresh schedule. Run. Forget.

Step 5 – Manage Triggers

If you skipped the initial schedule, you can always get back to the Triggers Tab to manage the export runs automatically:

1. Go to the Triggers tab in your data mart 

2. Click “Add new trigger”.

3. Choose a frequency: hourly, daily, weekly, or set a custom

4. Select the time zone

5. Enable the schedule

On each run, OWOX executes the SQL query against Databricks, writes the results to the specified Google Sheet, preserves any row-2 formulas, and maintains column order. The entire process runs in the background – no manual intervention needed.

You can also check the results of all the runs in the Run History tab. 

Note: we built this for the self-managed option and for giving more control to data teams. However, we also built a safe self-service analytics option, where business users can PULL the data from Google Sheets themselves.

Step 6 – Share with business users

The final step is straightforward: share the Google Sheet with your stakeholders using standard Google Sheets sharing. 

They see fresh, governed data every time they open it. If they add their own formulas in row 2, those formulas auto-extend on the next refresh.

The key point: business users never touch the data mart itself. 

You – the analyst – control the SQL logic, the schedule, the columns exposed, and the refresh frequency. They get a clean, auto-updating spreadsheet.

The business user way – self-service via the OWOX Google Sheets Extension

The scheduled export workflow solves the refresh problem, but it's still analyst-driven. Every new report, every new slice of data, every new question requires the analyst to build another data mart or modify an existing one.

What if business users could pull Databricks data into Google Sheets themselves – without asking the data team and without writing SQL? 

That's what the OWOX Data Marts Google Sheets Extension enables. And critically, business users gain self-service without the data team losing control over what's accessible.

This workflow requires an OWOX Cloud edition. 

Before business users can self-serve, the analyst should make the data mart available for reporting.

How joinable data marts work

needs to set up joinable data marts – OWOX's lightweight semantic layer.

Here's the concept: instead of writing one massive SQL query that joins campaigns, revenue, and customer data into a single flat table, the analyst creates separate data marts for each domain and then declares join relationships between them on shared keys.

For example:

• Data mart A: campaign_performance – campaign name, impressions, clicks, cost (from the ads connector)
• Data mart B: revenue_by_campaign – campaign name, revenue, transactions (from a SQL query against Databricks)
• Join key: campaign_name

Once the analyst declares this join, business users see columns from both data marts in a single picker – as if they were one table. OWOX auto-generates the SQL with proper aggregation to handle one-to-many relationships, so the numbers stay accurate.

This is a game-changer for self-service. The analyst defines the data model once. Business users explore it without needing to understand the underlying joins.

In my case it’s Customers & Growth report.

Step 1 – Analyst creates and joins data marts

1. Create the individual data marts (SQL, table, or view-based) as described in the analyst workflow above

2. Open the Joins configuration inside the main data mart.

3. Select the second data mart to left join it.

4. Specify the join key (the shared column – e.g., campaign_name, customer_id, product_sku)

5. Set up the aggregation (but default ones should be correct)

6. Toggle "Available for Reports" to ON for each data mart you want business users to access

The "Available for Reports" toggle is the governance layer. Only data marts with this toggle enabled appear in the Google Sheets Extension. 

Everything else stays invisible to business users. This means the analyst can have dozens of data marts for internal use, and expose only the curated, validated ones to the rest of the team.

Step 2 – Business user installs the OWOX Data Marts extension

From the business user's side, the setup takes about two minutes:

1. Open Google Sheets

2. Go to Extensions → Add-ons → Get add-ons

3. Search for "OWOX Data Marts" in the Google Workspace Marketplace

4. Click Install 

Once installed, the extension appears under Extensions → OWOX Data Marts in the Sheets menu.

Step 3 – Browse, filter, and pull data on demand

This is where self-service happens:

1. Open the OWOX Data Marts extension sidebar in Google Sheets

2. Browse available data marts – only the ones the analyst toggled "Available for Reports" are visible

‍

3. Select columns – both native columns and columns from joined data marts appear in the same picker. The business user doesn't need to know which data mart each column comes from.

‍

4. Apply filters – narrow down by date range, campaign name, region, or any other dimension

‍

5. Click Create & Run – click to load the data into the current Sheet

The data loads directly from Databricks through the data mart layer. No CSV. No SQL. No waiting for an analyst. And the business user can refresh on demand whenever they need updated numbers.

‍

The governance equation: The analyst controls which data marts are available, which columns are exposed, how joins are structured, and what the column descriptions say. The business user gets the freedom to pull and explore data without filing a ticket. Neither side compromises – the data team maintains control, and the business team gains independence.

How OWOX compares to other Databricks-to-Sheets solutions

You've seen how OWOX handles both analyst-driven exports and business-user self-service. Here's how it stacks up against the alternatives.

OWOX vs. Databricks native connector

The Databricks Connector for Google Sheets is free, built-in, and well-integrated. It lets users write SQL directly in Google Sheets and schedule refreshes. If your entire team is comfortable with SQL, it's a strong option.

Where OWOX differs: the native connector treats every user as a SQL author. There's no reusable data layer, no joinable marts, and no way for non-SQL users to access data independently. OWOX adds the governed self-service layer – business users browse curated data marts without SQL, while analysts maintain full control over the data logic and access.

OWOX vs. reverse ETL tools (Hightouch, Census)

Reverse ETL tools like Hightouch and Census sync warehouse data to downstream tools. They're flexible, support many destinations, and handle complex sync logic well.

The trade-off: reverse ETL requires engineering setup – defining sync models, configuring destinations, managing API credentials, and monitoring sync jobs. The data team owns an additional piece of infrastructure. And reverse ETL syncs are typically one-directional push jobs, not self-service pull.

OWOX's data marts are reusable analytics assets, not one-off sync jobs. The same data mart that powers a scheduled Sheet export can also serve the Google Sheets Extension for on-demand pulls. No separate sync infrastructure needed.

OWOX vs. spreadsheet connectors (Coefficient, CData)

Spreadsheet connector tools like Coefficient and CData offer quick setup and broad source coverage. They're good for connecting a single data source to a single Sheet.

Where OWOX goes further: connectors move raw data. OWOX adds a governed data mart layer – joinable data, reusable logic, analyst-controlled access, and structured output schemas with descriptions. It's the difference between "get the data into Sheets" and "get the right data into Sheets, governed and reusable."

‍

Beyond export – what you can do once data is in Sheets

Getting Databricks data into Google Sheets is step one. The real value comes from what your team can do once the pipeline is automated and governed.

Automated pivots and charts

Once data flows into Sheets on a schedule, you can layer OWOX's Pivots & Charts extension on top. It auto-generates pivot tables and visualizations from your data mart output – no manual chart building. The visuals update every time the data refreshes.

AI Insights delivered to Slack, Teams, or email

OWOX's AI Insights feature takes it further. Schedule AI-powered analysis of your data mart output and get insights delivered directly to Slack, Microsoft Teams, Google Chat, or email. 

Instead of opening a Sheet and scanning for anomalies, your team gets proactive alerts – "campaign CPA spiked 40% this week" or "revenue from organic search dropped 15% month-over-month."

But we’re spreadsheet first, so you get spreadsheet linked to the insight.

This turns your Databricks-to-Sheets pipeline from a passive reporting tool into an active intelligence system.

Governed data access across your team

The combination of data mart management, joinable data marts, and the "Available for Reports" toggle gives you a governance framework that scales with your team. 

New hire in marketing needs campaign data? Toggle a data mart on for their role. 

Intern shouldn't see revenue numbers? Keep that mart toggled off. 

The data team stays in control while the rest of the organization gets the data access they need.

Getting started – connect Databricks to Google Sheets today

If you're ready to move past CSV exports, here's what you need to get started.

Prerequisites

• A Databricks workspace with at least one SQL warehouse configured
• A Google account with access to Google Sheets
• An OWOX Data Marts account – get started free if you don't have one
• For the business user self-service workflow - brand new OWOX Data Marts Extension

Your first data mart in 2 minutes

1. Sign up for OWOX Data Marts

2. Connect your Databricks SQL warehouse as a storage

3. Build your first SQL data mart with a simple query

4. Add Google Sheets as a destination

5. Schedule the export – daily is a good starting point

6. Share the Sheet with a colleague and watch them react to auto-refreshing data

For the self-service workflow, add a second data mart, declare a join between them, toggle "Available for Reports," and have your business user install the extension. They'll be pulling governed Databricks data into Sheets within minutes.

Check OWOX pricing to find the plan that fits your team. The Cloud edition unlocks the full self-service experience including joinable data marts and the Google Sheets Extension.