Databricks Financial Services Global

Addepar

Data Lake / Data Warehouse Modernization · Real-time Analytics

Addepar needed a better way to support investment data unification, client onboarding, and advisor analytics because rapid growth in assets under management made disparate datasets and long pipeline lead times too slow for large institutional onboarding.

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$2M faster pipeline development, accelerating client onboarding

200% saved with data infrastructure optimization

5x connecting the global investment ecosystem

The challenge of scaling data for $7 trillion in assets

Reported impact

Business outcome

Addepar improved investment data unification, client onboarding, and advisor analytics with measurable results including 5x connecting the global investment ecosystem, $2M faster pipeline development, accelerating client onboarding, and 200% saved with data infrastructure optimization, while giving teams a faster path from governed data to production decisions.

Solution

Addepar uses Databricks Data + AI Platform to support investment data unification, client onboarding, and advisor analytics. The implementation replaced a legacy state where rapid growth in assets under management made disparate datasets and long pipeline lead times too slow for large institutional onboarding. Portfolio data and market data feeds move through Spark Declarative Pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog and Delta Sharing governs access, lineage, and compliance across sensitive datasets. Databricks SQL, Genie, and Agent Bricks operationalize models, agents, and analytics that deliver client onboarding pipelines and investment analytics.

Data flow

Portfolio data and market data feeds land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver client onboarding pipelines and investment analytics.

Solution architecture

8 components · 5 layers

Ingest
- Spark Declarative Pipelines Spark Declarative Pipelines standardizes portfolio data and market data feeds into production-grade tables and features.
Compute
- Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for investment data unification, client onboarding, and advisor analytics.
Storage
- Delta Lake Delta Lake stores curated portfolio data and market data feeds so every team works from one trusted data foundation.
Serving
- Databricks SQL Databricks SQL serves curated dashboards and ad hoc analysis to client onboarding pipelines and investment analytics.
- Genie Genie gives business users natural-language access to governed data for faster decisions around investment data unification, client onboarding, and advisor analytics.
- Agent Bricks Agent Bricks packages domain-specific agents that automate parts of investment data unification, client onboarding, and advisor analytics.
Governance
- Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.
- Delta Sharing Delta Sharing securely distributes governed data to client onboarding pipelines and investment analytics and partner workflows.

Architecture clues

5x connecting the global investment ecosystem
Automated ingestion and transformation pipelines keep source data synchronized for downstream analytics and AI.
Centralized governance, lineage, and access controls are enforced through Unity Catalog.
Delta Lake provides the shared storage layer for curated lakehouse datasets.
Operational AI workflows are deployed directly beside governed data and analytics assets.

Evidence from the source

The flexibility Databricks gives us has redefined how our teams collaborate and innovate at scale,

The integration of these capabilities continues to elevate our operations, automating manual processes in real time,

What previously took weeks to align between engineering and analytics can now be achieved in minutes thanks to Notebooks. This integration has changed how our teams work together,

With Databricks, we’ve established a unified data foundation that enables us to scale our platform globally and securely while continuously delivering value to our clients,

Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
3 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence89%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
4 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Databricks
Addepar customer story

First-party
https://www.databricks.com/customers/addepar

Source type
Vendor-published customer story

Published
Mar 19, 2025
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date

Date evidence

Published on source page Mar 19, 2025 · Databricks · First-party
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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