Man­age Data Where It Lives with Dat­ab­ricks Apps

As organ­isa­tions scale their data estates, more and more of the day-to-day work shifts from build­ing new pipelines to man­aging, main­tain­ing, and gov­ern­ing the ones that already exist. Someone has to check that yesterday’s load ran cleanly and that a schema change didn’t silently break a down­stream report. Dat­ab­ricks Apps address this need by provid­ing light­weight, gov­erned applic­a­tions that run dir­ectly inside the Dat­ab­ricks work­space. They turn data products and mod­els into inter­act­ive tools without export­ing data to external sys­tems, while main­tain­ing strong gov­ernance guardrails.

Dat­ab­ricks apps are designed for oper­a­tional work­flows: mon­it­or­ing pipeline health, val­id­at­ing data qual­ity, hand­ling schema changes, coordin­at­ing approvals, and provid­ing safe inter­faces for busi­ness users to inter­act with data. They are espe­cially use­ful for self‑service tasks, such as cur­ated access and approval flows tied to gov­ernance con­trols. These apps provide a gov­erned, in-work­space inter­face to per­form  oper­a­tional tasks. Teams can also use Dat­ab­ricks Apps to mon­itor pipeline health, run self-ser­vice data qual­ity checks, detect anom­alies, track cost and usage, sup­port metadata stew­ard­ship, and give busi­ness users safe, cur­ated access to data, all while pre­serving the secur­ity, lin­eage, and per­mis­sions the plat­form already provides.

    What Dat­ab­ricks Apps Are and Why They Matter

    Dat­ab­ricks Apps are web applic­a­tions that are deployed dir­ectly to the Dat­ab­ricks envir­on­ment. They allow teams to trans­form repeat­able data work­flows like gov­ernance decisions, qual­ity gates, and oper­a­tional tasks into user-friendly inter­faces that min­im­ise manual inter­ven­tions and reduce risk. Instead of ad-hoc note­books or SQL quer­ies, you can provide struc­tured “front doors” for busi­ness users, data stew­ards, and engin­eers: a way to run con­trolled approval flows, gov­erned data edits, and self-ser­vice oper­a­tions that fully respect Unity Cata­log per­mis­sions and auditability.

    Dat­ab­ricks pos­i­tions Apps as a way to quickly build secure data and AI applic­a­tions using famil­iar Python frame­works like Dash, while keep­ing deploy­ment and main­ten­ance over­head min­imal. For Dat­ab­ricks, they are the nat­ural next step towards an end-to-end data plat­form that cov­ers every step from data inges­tion through trans­form­a­tion, data qual­ity, and gov­ernance to BI and operations.

    What These Apps Look Like in Practice 

    Apps serve as con­trolled, gov­erned inter­faces for pro­cesses that would oth­er­wise require dir­ect data access or informal approvals. Com­mon examples include: 

    • Approval work­flows for gov­ernance decisions: 
      • Self-ser­vice access con­trol portal
      • PII (Per­son­ally Iden­ti­fi­able Inform­a­tion) data clas­si­fic­a­tion, tag­ging and anonymisa­tion based on engin­eer­ing, busi­ness and GDPR requirements
    • Qual­ity gate dash­boards that val­id­ate data before promotion
    • Self‑service oper­a­tional tools enabling busi­ness users to inter­act with cur­ated pro­cesses without using note­books or SQL 
    • Gov­ernance toolkits like Ontos, a data con­tract and data product gov­ernance plat­form built and main­tained by Dat­ab­ricks, which fol­lows the open source ODPS and ODCS stand­ards to unify how busi­nesses buy, man­age, and scale data products.

    A Key Archi­tec­tural Detail: The Read/Write Access Pat­tern 

    Effect­ive gov­ernance hinges on how the app inter­acts with data. Dat­ab­ricks Apps allow you to imple­ment a hybrid access pattern:

    Both the access to the app and the read oper­a­tions inside the app occur “on behalf of” the authen­tic­ated user, lever­aging their indi­vidual per­mis­sions. Unity Cata­log per­mis­sions apply auto­mat­ic­ally, ensur­ing data access is con­sist­ent and trace­able. This also ensures that data mask­ing and row-level secur­ity rules work con­sist­ently across the platform.

    Writes are per­formed via a ded­ic­ated ser­vice prin­cipal, allow­ing updates only through the app’s val­id­ated busi­ness logic, while record­ing who ini­ti­ated the change for full auditability.

    A Few Examples

    Let’s explore some examples of how Dat­ab­ricks Apps can enhance com­mon data oper­a­tion pat­terns that would oth­er­wise require elev­ated priv­ileges and addi­tional audit trails.

    Manual Data Adjust­ments with Approval Process

    Wherever approval pro­cesses or manual data adjust­ments are needed, apps can provide an inter­face for non-tech­nical users.

    In this example, an app lets users manu­ally adjust KPI factors applied dur­ing data pro­cessing. Without the app, these adjust­ment factors are provided via CSV or Excel files. While manual file trans­fer works, it is error-prone, espe­cially due to loc­al­isa­tion issues and user error.

    1. Request tab 
    The requester selects an attrib­ute, loads ref­er­ence data into a grid, edits only per­mit­ted fields, pre­views changes, and sub­mits a “Change Request”. The sys­tem records all rel­ev­ant metadata (requested_by, requested_on, com­ments) asso­ci­ated with the request. Only columns con­figured in a YAML file are open for changes, and these edit­able columns are high­lighted in the UI. Once a change request is approved, the changes flow back into the ETL pipelines based on their ori­ginal keys.

    2. Bulk Upload tab
    The user can upload a full CSV or Excel file to pop­u­late the plat­form with data manu­ally. The sys­tem checks the data struc­ture and com­pli­ance with the expec­ted schema, and the user instantly receives feed­back on any issues on the same page.

    3. My Requests tab 
    Requesters can track each sub­mis­sion, with its status derived from approval metadata.

    4. Approve tab 
    Approvers only see requests sub­mit­ted by oth­ers, enfor­cing the four-eyes prin­ciple. Approvals cap­ture approver metadata and may trig­ger down­stream pro­cessing or noti­fic­a­tions. Cur­rent and pro­posed entries are dis­played side by side for vis­ib­il­ity. Approvers can select indi­vidual rows to approve. On approval, repro­cessing of the down­stream pipeline starts automatically.

    PII Data Clas­si­fic­a­tion, Tag­ging and Anonymisa­tion App

    Strong data gov­ernance plays a key role in GDPR com­pli­ance and the hand­ling of PII, and it ties in closely with a business’s data strategy. The dis­cov­ery and clas­si­fic­a­tion of such data in par­tic­u­lar is tedi­ous and often manual.

    This example is another approval work­flow app. It stand­ard­ises how organ­isa­tions identify, val­id­ate, and approve sens­it­ive data in Dat­ab­ricks Unity Cata­log. The app ensures that data is tagged based on com­pli­ance needs before mask­ing, hash­ing, and related rules are applied. Admin­is­trat­ors con­fig­ure approvers at the pro­ject level, while review­ers pro­cess assigned approval cases via a guided work­flow. The work­flow cap­tures table con­text and enforces column-level clas­si­fic­a­tions, cat­egor­ies, and anonymisa­tion set­tings. Built-in guard­rails pre­vent invalid classification/category com­bin­a­tions and sup­port the dis­cov­ery of miss­ing tables or columns. Final­ising the pro­cess applies gov­erned tags to tables and columns. A gov­erned tag is an account-level tag set up by admin­is­trat­ors. Gov­erned tags can be used to enforce policies or cus­tom rules for attrib­ute-based access con­trol (ABAC) or to flag data based on GDPR requirements.

    Dat­ab­ricks Labs’ Ontos

    Ontos is a Dat­ab­ricks Labs pro­ject that adds a busi­ness gov­ernance and semantic layer on top of Unity Cata­log. Its goal is to help organ­isa­tions move bey­ond tech­nical metadata (tables, schemas, lin­eage, per­mis­sions) and man­age the busi­ness mean­ing, own­er­ship, qual­ity, and life­cycle of data products.

    Built in React and dir­ectly integ­rated with Dat­ab­ricks Unity Cata­log, with Dat­ab­ricks Lake­base as a fast stor­age layer, Ontos offers a range of data gov­ernance features:

    • AI-assisted data discovery
    • Data product management
    • Busi­ness glossaries
    • Data con­tracts
    • Semantic/ontology man­age­ment
    • Stew­ard­ship workflows
    • Data mar­ket­place capabilities
    Dat­ab­ricks Labs’ Ontos – Gov­ernance Suite – Land­ing Page

    Bot­tom Line 

    Dat­ab­ricks Apps extend the Lake­house bey­ond ana­lyt­ics by bring­ing gov­erned oper­a­tional pro­cesses dir­ectly into the Dat­ab­ricks plat­form. They enable organ­iz­a­tions to build secure, user-friendly inter­faces for approvals, data stew­ard­ship, qual­ity man­age­ment, access requests, and gov­ernance work­flows without mov­ing data out­side the plat­form. By com­bin­ing user-based read access with con­trolled write oper­a­tions, Dat­ab­ricks Apps help enforce com­pli­ance, improve audit­ab­il­ity, and reduce manual effort while pre­serving Unity Cata­log secur­ity, lin­eage, and gov­ernance stand­ards. Whether sup­port­ing approval pro­cesses, PII clas­si­fic­a­tion, or broader data product gov­ernance through solu­tions such as Ontos, Dat­ab­ricks Apps provide a prac­tical way to turn gov­ernance from a manual bur­den into an integ­rated, scal­able oper­at­ing model.