Surveymonkey to Databricks: How to Move Your Data

Moving SurveyMonkey into Databricks makes sense when you intend to do something with what people wrote. Scores and multiple choice answers are easy to report anywhere; the open-ended responses are where the useful material sits and where almost nobody looks.

This guide covers the managed path with Airbyte. Two things shape the build: free text is the half this destination unlocks, and every survey has a different shape, which a lakehouse absorbs more gracefully than most.

Surveymonkey to Databricks at a glance:

Why move data from Surveymonkey to Databricks?

One situation genuinely suits this, and it is worth checking before building.

The good case is processing the written answers. Classifying complaints by theme, extracting product mentions, scoring sentiment or embedding responses so similar ones cluster are all language tasks, and a lakehouse is where that work happens beside the data.

The weaker case is counting scores, because survey volumes are small and a warehouse does that with far less machinery. If governed reporting rather than text processing is the aim, Surveymonkey to Snowflake suits it better.

What do you need before you start?

Four things, and the first decides whether this destination is the right one:

Confirmation that the text is the point. If nobody intends to process written answers, this is more platform than the job needs and a warehouse will serve better at a fraction of the effort.

An access token and your origin datacenter. Airbyte needs the datacenter because API addresses depend on where your account is hosted. The SurveyMonkey source documentation covers registration and the fields.

Permission to create Volumes in Unity Catalog. Staging goes through Avro files written into a Volume, which is separate from creating tables and worth requesting early.

A plan for the first load. SurveyMonkey applies a daily request cap as well as a per-minute limit, so a large history spans several days and is worth scheduling rather than starting hopefully.

If your workspace restricts traffic by IP, add the Airbyte Cloud IP addresses to the allow list before you begin.

How do you build a Surveymonkey to Databricks pipeline in Airbyte?

Step 1: Check somebody intends to read the answers

Ask what will happen to the open-ended responses, because that answer justifies this destination and nothing else does. Classification, theme extraction and embedding are real reasons. Reporting an average score is not, and neither is a dashboard of response counts, both of which a warehouse handles with considerably less to operate.

Step 2: Configure the SurveyMonkey source

Click Sources in the left navigation, then New Source, and select SurveyMonkey, following adding a source. Supply the access token, origin datacenter and start date, naming specific survey identifiers rather than leaving the field blank, since blank means every survey in the account.

Step 3: Configure the Databricks destination

Click Destinations, then New Destination, and select Databricks, following adding a destination. Supply the workspace details, warehouse or cluster, catalogue and schema. Let responses land with their nested question and answer structures intact, because Spark reads them and flattening early throws away the shape you need.

Step 4: Create the connection and schedule around the cap

Click Connections, then New connection, select your streams and a sync mode. Daily is right for survey data, and with a daily request cap it also leaves headroom for a backfill to progress rather than competing with routine syncs.

Then build the silver layer, because raw responses are shaped for SurveyMonkey rather than for anybody analysing them.

What happens to the free-text answers?

Usually nothing, which is the gap this destination closes. Most organisations report the multiple choice questions diligently and leave the open-ended ones unread beyond a spot check, because reading several thousand comments is nobody's job and summarising them by hand is worse.

That material is usually the most informative part of the survey. A score tells you somebody was dissatisfied; the sentence underneath tells you why, in their words, which is what anybody acting on the result actually needs. Classifying those into themes, extracting the products or features mentioned, or embedding them so similar complaints group together are all ordinary notebook work.

Two cautions while you do it. Free-text answers contain whatever respondents chose to type, including names, contact details and occasionally complaints about identifiable colleagues, so treat the column as sensitive rather than as data. And keep the original text alongside whatever your models produced, because a theme label is only trustworthy while somebody can check the sentence it came from.

Why does every survey look different?

Because each one has its own questions, and the response structure follows them. A response to a customer satisfaction survey and a response to an employee engagement survey share a respondent and a timestamp and almost nothing else, so there is no common flat shape waiting to be discovered.

A lakehouse handles that better than a typed destination does. The nested question and answer structures land as they are, Spark reads them natively, and bronze stays faithful to what SurveyMonkey returned without anybody deciding in advance which questions matter. That is the right default for a dataset whose shape changes every time somebody designs a new survey.

The modelling then happens per survey or per family of surveys sharing a template, in silver tables naming columns after what the questions actually asked. Keep the bronze layer, because next quarter's survey will ask something different and you will want the original when somebody asks whether a question changed wording between waves.

Frequently asked questions

Is a lakehouse overkill for survey data?

For scores and counts, yes. It earns its place when you intend to process the written answers, which is notebook work best done beside the data.

Why is my backfill taking days?

SurveyMonkey applies a daily request cap alongside a per-minute limit, so a large initial load spans several days. Ask them about a temporary increase.

Should I flatten responses on the way in?

No. Spark reads the nested structures natively, and flattening early forces you to decide which questions matter before anybody has asked.

The connection fails but my token is right.

Check the origin datacenter, since API addresses depend on where your account is hosted and the wrong one fails like a credentials problem.

Can I do this without writing code?

The pipeline, yes. The models that read the free text are the reason to be here, and they are code by definition.

Get your Surveymonkey data into Databricks

Confirm somebody will process the written answers, because that is what justifies this destination over a warehouse. Set the origin datacenter and plan the first load around a daily cap. Then let nested responses land intact, model per survey in silver while keeping bronze faithful, and treat free text as sensitive since respondents type whatever they like into it.

Airbyte's connector catalog includes 600+ pre-built connectors, so what customers wrote can be read at scale rather than sampled. For the same source into a warehouse, see Surveymonkey to Snowflake, and for published text into the same destination, Rss to Databricks.

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