Metrics nobody agrees on
Data is spread across the CRM, the ERP, finance tools, and spreadsheets. Each team defines revenue or churn a little differently, so meetings start with an argument about whose number is right.
Data platform · Databricks and Salesforce Data 360
A Databricks data foundation for analytics, AI, and Salesforce
We design the data strategy and architecture that fit how your business runs, then build it on Databricks. Salesforce Data 360 puts that same governed data in front of the sales, service, and marketing teams who act on it.
DatabricksPartner
- Data 360 and Agentforce delivery
- 4.97 / 5 across 110+ AppExchange reviews
Databricks bronze, silver, and gold lakehouse layers share Unity Catalog governance. A two-way Zero Copy connection links that foundation with Salesforce Data 360 for sales, service, marketing, and Agentforce.
Foundation and activation
Governed lakehouse
Databricks
- BronzeRaw data as it arrives
- SilverCleaned and matched
- GoldBusiness-ready data products
Unity Catalog
Access, lineage, and quality
Zero Copy
Business activation
Salesforce
Data 360
Customer context for front-office teams
- Sales
- Service
- Marketing
- Agentforce
What this means
SOLVD.cloud is a Databricks partner and Salesforce consulting firm. We design data strategy and architecture that fit the business, build governed lakehouse foundations on Databricks with Unity Catalog, and connect them to Salesforce Data 360 so sales, service, and marketing teams act on trusted enterprise data. Engagements start with an intro meeting.
Analytics and AI only pay off when the business trusts the data
Most companies already have a warehouse, a stack of dashboards, and an AI pilot or two. The trouble starts when a leader asks for a number to act on and gets a different answer from every system.
Pilots that stay pilots
A model works in a demo. Without governed data, clear access rules, and someone who owns it after launch, it never becomes part of how the work gets done.
Customer data and enterprise data kept apart
Salesforce holds the customer relationship. Orders, usage, and finance data sit in a lakehouse or warehouse. Moving files between them by hand is slow, expensive, and out of date by the time anyone uses it.
The answer starts with the foundation: one governed home for enterprise data, connected to the systems where your people already work.
Databricks and Salesforce Data 360 have different jobs. We design them to work together.
The enterprise data foundation
Stores, cleans, governs, and analyzes data from every system you run. Data engineering, BI, data science, machine learning, and AI all work from it.
Customer context and activation
Brings customer data together inside Salesforce so sellers, service teams, marketers, and Agentforce can act on it in the flow of work.
Zero Copy between them
Salesforce and Databricks support Zero Copy integration in both directions. Data 360 can use governed Databricks tables without copying them, and Databricks can work with Data 360 customer data for analysis and modeling.
Databricks and Salesforce Data 360 shown as equal platforms with different jobs, linked in both directions through Zero Copy.
Complementary jobs
Databricks
- Governed enterprise data
- BI, data science, and machine learning
- Unity Catalog governance
Zero Copy in both directions
Salesforce Data 360
- Unified customer profiles
- Segments and calculated insights
- Action in sales, service, and Agentforce
Salesforce Data 360, formerly called Data Cloud, is where front-office teams act on customer data. Databricks is where the whole business keeps, governs, and learns from its data. Each is stronger when the other is set up well.
Few firms work on both sides of that connection. We are a Salesforce Summit Partner and a Databricks partner, so one team owns the design from the source system to the screen a seller or service agent sees.
Every engagement starts with strategy. Before anything gets built, we write down why the architecture fits your operating model, your systems, and the outcomes you need, and what it will cost to run.
Reference architecture: what we build
This example uses Databricks, our preferred platform for new foundations. Keeping and consolidating your existing Snowflake setup is also an option.
Reference architecture, shown as a Databricks example. Salesforce, ERP, files, events, and an existing Snowflake setup that can stay in place feed a Databricks lakehouse with bronze, silver, and gold layers. Unity Catalog governs access, lineage, and quality across it. BI, machine learning, and AI read from the gold layer, and Salesforce Data 360 uses the same data through Zero Copy for sales, service, marketing, and Agentforce.
From source systems to business action
Sources
- Salesforce
- ERP and operations systems
- Files and event streams
- An existing Snowflake setup you keep
Ingest
Governed lakehouse
Databricks
- BronzeRaw data as it arrives
- SilverCleaned and matched
- GoldBusiness-ready data products
Governance across every layer
Unity Catalog
- Access by role
- Lineage
- Quality checks
- Domain owners
Serve and activate
Analytics and AI
- BI and dashboards
- Data science and machine learning
- AI applications and agents
Business activation
Salesforce Data 360
- Sales
- Service
- Marketing
- Agentforce
Through Zero Copy
- Sources
- We connect the systems that hold the facts the business runs on, and we keep the systems you already use.
- Databricks lakehouse
- Data moves through medallion layers and ends up as curated data products organized by business domain, such as finance, sales, and operations.
- Unity Catalog governance
- Rules sit across every layer, so anyone can see where a number came from and who is allowed to use it.
- Analytics and AI
- Reports, models, and agents read from the same gold tables, so everyone works from one set of numbers.
- Activation in Salesforce Data 360
- Data 360 uses governed Databricks data through Zero Copy and turns it into profiles, segments, and actions inside Salesforce.
What changes for the business
We judge the work by what people can do afterward, and we agree on those measures before the build starts.
One set of numbers
Finance, sales, and operations work from the same governed definitions, so meetings move from debating the data to deciding what to do.
Usable data products sooner
Modernization is phased by business domain, so teams start using curated data while the rest of the migration continues.
AI that reaches production
Models and agents run on governed data, with monitoring and a named owner, which is what it takes to move past the pilot.
Salesforce actions informed by enterprise data
Sellers and service teams see usage, orders, and risk signals in Salesforce without a second copy of the data to maintain.
Get your data in shape for the work ahead.
Start with the records, reports, or systems holding your team back. We agree the cleanup or migration scope with you and define how to check the result.
- Source fields
- Agreed definitions
Spend less time reconciling your reports.
Conflicting records make routine decisions harder. We help clean up duplicates and inconsistent fields, with shared definitions and checks your team can use.
OutcomeWork toward clearer reports and less manual reconciliation.
Talk through your data cleanupBring scattered reporting data together.
Move data into Databricks, or keep and consolidate the Snowflake setup you already run. We plan the work around the reports and business processes your team depends on.
OutcomeWork toward fewer disconnected datasets and a shared basis for reporting.
Discuss your migration or consolidation- Accounts and contacts
- Review matches
- Approve merges
Clean up customer records before Data 360 or AI.
We help deduplicate Salesforce accounts and contacts, agree matching rules, and review proposed merges with your team. Start with the records your customer workflow needs.
OutcomeWork toward a clearer customer view for sales and service, with human review of AI output built into the plan.
Plan your Salesforce data cleanup- Map
- Test
- Review
- Cut over
Move off a legacy system with a plan for the data.
We map the records and relationships your next system needs, then test the migration with your team. Agree what to move, what to retain, and the checks for each cutover.
OutcomeWork toward usable history in the next system and a clear path to retiring the old one.
Talk through your legacy migrationWays to engage
Five scoped engagements, each with a defined result. Start with the one that matches where you are today, and add the next when the first has paid off.
Offer 01
Data Strategy and Architecture
Best for: Before a build, or when a data program has stalled
- A review of your current systems, data, and team
- A target architecture across Databricks and Salesforce, with the reasons it fits
- A phased roadmap with costs, owners, and the first use case
Offer 02
Lakehouse Foundation
Best for: New Databricks platforms, or platforms that need a restart
- Ingestion pipelines and bronze, silver, and gold layers
- Unity Catalog access, lineage, and quality checks from day one
- Environments, deployment, and cost controls your team can run
Offer 03
Data 360 and Databricks Activation
Best for: Companies that run sales, service, or marketing on Salesforce
- Zero Copy design between Databricks and Data 360
- Unified customer profiles, segments, and calculated insights
- Use cases for sales, service, marketing, and Agentforce
Offer 04
Modernization and Migration
Best for: Legacy warehouses and platform consolidation, including estates that run Snowflake
- A workload inventory and a migration plan by business domain
- Parallel runs that confirm results match before each cutover
- A retirement plan for the systems you no longer need
Offer 05
Production AI Sprint
Best for: Teams with a governed foundation and a use case ready to build
- One scoped analytics or AI use case on governed data
- Evaluation, monitoring, and an owner in place before launch
- We can help you design toward a Model Context Protocol (MCP) server you own, with scope, access rules, and human review agreed with your team.
How we work
Senior consultants lead the work from the first conversation through handoff. Every engagement is scoped before it starts, and your team learns the platform while we build it.
- Step 1
Intro meeting
We talk through scope, systems, outcomes, and constraints. If we are not the right partner, or the timing is wrong, we will tell you.
- Step 2
Architecture and plan
You get the target architecture diagram, the governance model, and a sequenced plan that starts with the first result we will deliver.
- Step 3
Build in the open
Regular demos, a written decision log, and a production readiness review before anything goes live. You see progress as it happens.
- Step 4
Hand off
Your team gets documentation, runbooks, and training on the platform it now owns, with optional hypercare after launch.
“We recently did two projects with them - an org migration and building a custom agentforce agent. … Not only do I appreciate how thorough their work and processes are, but where they really stand out to me is in education and setting you up for success even after the project has concluded.”
Governance designed in from the start
We set up Unity Catalog at the beginning of the build, so access, lineage, and quality checks are part of every table from the first pipeline. Retrofitting governance after data has spread across teams takes longer and costs more.
Your team approves access rules for each business domain, such as finance, sales, and operations. Within the agreed scope, we work with you to configure access in Salesforce Data 360 around what each person or agent’s role allows, then review the agreed access tests together. AI output needs human review before use.
- Access control by role and business domain
- Lineage from the source system to the dashboard
- Quality checks on the tables the business depends on
- A written owner for every domain and data product
Results from our data and systems work
Client names are withheld. These projects involved Salesforce data models, integrations, and automation.
- Challenge
- Sales teams needed lead routing and visibility across Salesforce and NetSuite.
- What we built
- Sales Cloud, a NetSuite connection, automatic assignment, and shared dashboards.
- Outcome
- Salesforce and NetSuite started telling the same sales story, with clearer routing and reporting.
- Challenge
- Customer work crossed several Salesforce clouds and an external database.
- What we built
- Rebuilt core processes and the data connections behind them.
- Outcome
- Better data integrity and reporting the team could trust.
3 days → 5 minutes
- Challenge
- A manual RFQ process tied experienced people up in repetitive quoting work.
- What we built
- Automation around the governed steps of the existing quoting process.
- Outcome
- RFQ turnaround fell from about three business days to about five minutes.
“We threw them some major curveballs, they adapted quickly and delivered on-time anyway.”
FAQs
Does Databricks replace Salesforce or Data 360?
No. Salesforce describes Data 360 and Databricks as complementary. Data 360 helps front-office teams act on unified customer data inside Salesforce. Databricks stores, processes, and analyzes enterprise data for analytics, machine learning, and AI. We design them to share data through Zero Copy, with each platform doing its own job.
Will this sideline our Salesforce investment?
It should do the opposite. The goal is to bring more enterprise data into Salesforce, so your teams get more from the platform they already use. We work alongside your Salesforce account team, and we are a Salesforce Summit Partner ourselves.
Do you only work on Databricks?
No. SOLVD.cloud is a Databricks partner with a deep Salesforce and Data 360 practice, so the same team can design the lakehouse and the Salesforce side. We can work with Snowflake or other lakehouse solutions if they are already deployed. We prefer Databricks for new deployments. Keeping and consolidating an existing Snowflake setup is also an option.
Can AI agents use this data?
We can help you plan how agents access the data, including designing toward a Model Context Protocol (MCP) server you own. We work with your team to agree scope, configure access under rules you approve, and review the agreed tests together. AI output needs human review before use.
How do we start?
Book a meeting with our team. It is a structured conversation about your goals, systems, and constraints. You don’t need a finished scope, and you will leave with a recommended next step.
Book a meetingThe next move
Get your data strategy, architecture, and delivery on one plan
Book a meeting with our team. It is a structured conversation about scope, with no pitch deck. Bring your goals, the systems involved, and what’s in the way. You don’t need a finished scope.
- Share the goalWhat the business needs from its data, and the systems involved.
- Weigh the optionsWhether Databricks, Data 360, or both belong in the answer, and whether we are the right partner.
- Agree the next stepA recommended engagement, scope, and investment before any build.