# Databricks - Marketing Research Report Generated on: April 6, 2026 **Industry:** AI & Machine Learning **Website:** https://www.databricks.com ## The Takeaway Databricks wins by making consumption-based pricing the default for enterprises locked into Spark ecosystems. Yet the lakehouse abstraction only sticks if customers actually unifyβ€”most still run parallel warehouses, leaving the core moat incomplete. --- # Company Research ## Company Summary Databricks is a data and AI platform company that provides a unified lakehouse architecture combining data lakes and data warehouses for analytics and machine learning workloads [6] **Founded:** Founded in 2013 [5] **Founders:** Ali Ghodsi, Andy Konwinski, Ion Stoica, Matei Zaharia, Patrick Wendell, Reynold Xin, and Scott Shenker [5] **Employees:** Over 7,000 employees as of 2024 [1] **Headquarters:** San Francisco, California [1] **Funding:** Valued at $62 billion following a $10 billion Series J funding round in December 2024 [4] **Mission:** To help data teams solve the world's toughest problems by providing a unified platform for data engineering, analytics, and machine learning [7] **Strengths:** The company's strengths rely on the combination of unified lakehouse architecture, enterprise-scale customer adoption, and comprehensive data-to-AI capabilities. [6] β€’ **Unified Lakehouse Platform**: Combines the best of data lakes and data warehouses into a single architecture that reduces costs and supports any AI use case [6] β€’ **Fortune 500 Penetration**: Over 40% of Fortune 500 companies and more than 5,000 organizations worldwide rely on the platform [17] β€’ **Multi-Cloud Integration**: Available across AWS, Microsoft Azure, and Google Cloud with deep integrations into existing enterprise infrastructure [8] ## Business Model Analysis ### 🚨 Problem ****Organizations struggle with fragmented data infrastructure that creates silos between data lakes and warehouses** [6]** β€’ Data teams face challenges managing separate systems for storage, processing, and analytics [6] β€’ Traditional architectures create bottlenecks between data engineering and data science workflows [7] β€’ Companies need unified governance across batch and streaming data pipelines [7] β€’ Organizations require scalable solutions that support both traditional analytics and modern AI workloads [6] ### πŸ’‘ Solution ****Databricks provides a unified lakehouse platform that combines data storage, processing, and AI capabilities in one solution** [6]** β€’ Data Intelligence Platform unifies data engineering, analytics, BI, data science, and machine learning workloads [9] β€’ Lakeflow enables reliable ETL pipelines for both batch and streaming data at scale [7] β€’ Unity Catalog provides centralized governance and security across all data assets [16] β€’ Serverless compute options reduce infrastructure management overhead [7] β€’ Built-in collaborative notebooks and MLOps capabilities accelerate time-to-value [7] ### ⭐ Unique Value Proposition ****First unified lakehouse architecture that eliminates data silos while providing enterprise-grade governance and AI capabilities** [6]** β€’ Open lakehouse foundation supports Delta Lake format for ACID transactions and time travel [9] β€’ Native integration with major cloud providers without vendor lock-in [8] β€’ Single platform handles everything from real-time dashboards to advanced machine learning [8] β€’ Consumption-based pricing model that scales with actual usage [12] ### πŸ‘₯ Customer Segments ****Primarily serves large enterprises and Fortune 500 companies across multiple industries** [14]** β€’ Fortune 500 companies including Comcast, CondΓ© Nast, and H&M [17] β€’ Government organizations and federal agencies like Navy Federal [16] β€’ Research institutions such as Westat supporting hundreds of projects [16] β€’ Retail and consumer goods companies requiring real-time analytics [17] β€’ Financial services firms needing regulatory compliance and governance [13] ### 🏒 Existing Alternatives ****Competes primarily with Snowflake, Palantir, and Microsoft in the data platform space** [10]** β€’ Snowflake: Focused on cloud data warehouse architecture with strong SQL performance [10] β€’ Palantir: Specializes in complex data fusion and security for government and enterprise [12] β€’ Microsoft Fabric: Integrated within broader Microsoft ecosystem with bundled pricing [12] β€’ Amazon Redshift and Google BigQuery: Cloud-native data warehouse solutions [10] β€’ Traditional vendors like Oracle, IBM, and Teradata in legacy enterprise markets [11] ### πŸ“Š Key Metrics ****Databricks achieved $3 billion in annual recurring revenue and serves over 5,000 organizations worldwide** [2]** β€’ Annual Recurring Revenue: $3 billion as of 2024 [2] β€’ Customer base: Over 5,000 organizations globally [17] β€’ Fortune 500 penetration: Over 40% of Fortune 500 companies [17] β€’ Valuation: $62 billion following December 2024 funding round [4] β€’ Revenue multiple: 20.6x forward revenue based on 2024 ARR [2] ### 🎯 High-Level Product Concepts ****Core platform consists of lakehouse storage, compute engines, and collaborative analytics tools** [7]** β€’ Data Intelligence Platform: Unified environment for all data and AI workloads [9] β€’ Delta Lake: Open-source storage layer providing ACID transactions [6] β€’ MLflow: End-to-end machine learning lifecycle management [7] β€’ Unity Catalog: Centralized data governance and security [16] β€’ Collaborative notebooks: Interactive environment for data science and analytics [7] ### πŸ“’ Channels ****Multi-channel go-to-market strategy combining direct sales, cloud marketplace partnerships, and partner ecosystem** [8]** β€’ Direct enterprise sales team targeting Fortune 500 accounts [14] β€’ Cloud marketplace presence on AWS, Microsoft Azure, and Google Cloud [8] β€’ Partner channel through system integrators and consulting firms [13] β€’ Developer community engagement through open-source contributions [6] β€’ Industry-specific solution marketing for retail, financial services, and government [17] ### πŸš€ Early Adopters ****Data-driven enterprises with complex analytics requirements and existing big data investments** [14]** β€’ Large enterprises already invested in Spark and big data technologies [5] β€’ Organizations with dedicated data science and engineering teams [16] β€’ Companies requiring real-time analytics and machine learning at scale [8] β€’ Businesses needing unified governance across multiple data sources [16] ### πŸ’° Fees ****Consumption-based pricing model that charges based on actual compute and storage usage** [12]** β€’ Pay-per-use compute pricing based on Databricks Units (DBUs) consumed [12] β€’ Storage costs separate and based on cloud provider rates [12] β€’ Premium features like Unity Catalog available in higher-tier plans [12] β€’ Enterprise packages with volume discounts for large deployments [12] β€’ No upfront licensing fees, unlike traditional enterprise software [12] ### πŸ’΅ Revenue ****Primary revenue from consumption-based platform fees with additional services and support** [2]** β€’ Platform subscription fees: Core revenue stream from compute and storage usage [2] β€’ Professional services: Implementation and consulting revenue [13] β€’ Training and certification programs: Educational services revenue [13] β€’ Premium support tiers: Enhanced SLA and dedicated support options [13] β€’ Partner revenue sharing: Commissions from cloud marketplace sales [8] ### πŸ“… History ****Founded in 2013 by Apache Spark creators, evolved from open-source project to enterprise platform** [5]** β€’ 2013: Company founded by Apache Spark creators at UC Berkeley [5] β€’ 2014: Launched first cloud-based Spark platform [5] β€’ 2016: Introduced MLflow for machine learning lifecycle management [5] β€’ 2019: Announced Delta Lake open-source storage layer [5] β€’ 2020: Launched Unity Catalog for data governance [5] β€’ 2021: Went public consideration, remained private [5] β€’ 2024: Achieved $3 billion ARR and $62 billion valuation [2] ### 🀝 Recent Big Deals ****Completed $10 billion Series J funding round at $62 billion valuation in December 2024** [4]** β€’ $10 billion Series J led by Thrive Capital with co-leads Andreessen Horowitz, DST Global, and Insight Partners [4] β€’ Partnership expansion with Microsoft for deeper Azure integration [8] β€’ Strategic alliance with AWS for enhanced marketplace presence [9] β€’ Launch of industry-specific solutions for retail and consumer goods [17] ### ℹ️ Other Important Factors ****Strong focus on open-source contributions and avoiding vendor lock-in through multi-cloud strategy** [6]** β€’ Open-source foundation with Delta Lake and MLflow reduces customer concerns about proprietary lock-in [6] β€’ Multi-cloud deployment capability across AWS, Azure, and Google Cloud [8] β€’ Strong Apache Spark community leadership and contribution [5] β€’ Regulatory compliance features for government and financial services customers [16] --- # ICP Analysis ## Ideal Customer Profile Databricks' ideal customer is a **Fortune 500 enterprise** with **1,000+ employees** and mature data organizations requiring unified governance across complex analytics and AI workloads [14], [17]. These companies have **dedicated data science and engineering teams** that need collaborative platforms for real-time dashboards, predictive modeling, and machine learning [8], [16]. They typically possess **existing big data investments** in Apache Spark technologies and face **regulatory compliance requirements** in government or financial services sectors [5], [16]. The ideal customer demonstrates **budget authority for enterprise-scale deployments** and willingness to adopt **consumption-based pricing models** that scale with usage rather than traditional upfront licensing [12]. ## ICP Identification Framework | No. | Question | Answer | References | |-----|----------|--------|------------| | 1 | Which of our current customers makes the most out of our products and services? Who uses it the most? Who are your best users? | Best customers are **Fortune 500 enterprises** with dedicated data science and engineering teams requiring **unified governance across multiple data sources** [14], [16]. These organizations typically have **over 1,000 employees** and complex analytics requirements that span real-time dashboards to advanced machine learning [8]. Companies like **Comcast, CondΓ© Nast, and H&M** represent this segment, leveraging the platform for **enterprise-scale data and AI workloads** [17]. | [14], [16], [8], [17] | | 2 | What traits do those great customers have in common? | Common traits include **existing big data investments** and familiarity with Apache Spark technologies [5]. They have **mature data organizations** with separate data engineering, data science, and analytics teams that need collaboration [16]. These customers prioritize **regulatory compliance features** for government and financial services requirements [16]. They also demonstrate **willingness to adopt consumption-based pricing models** that scale with actual usage rather than traditional upfront licensing [12]. | [5], [16], [12] | | 3 | Why do some people decide not to buy or stop using our product? | Primary obstacles include **significant upfront investment concerns** compared to competitors offering bundled pricing within existing enterprise agreements [12]. Some organizations prefer **fully managed cloud data warehouse solutions** like Snowflake for simpler SQL-focused use cases [10]. Technical teams sometimes choose alternatives that provide **better integration with existing Microsoft ecosystems** or require less complex implementation processes [12]. Cost-conscious customers may find the **consumption-based model unpredictable** for budget planning purposes. | [12], [10] | | 4 | Who is easiest to sell more to, and why? | Easiest expansion comes from **existing large enterprises scaling their data teams** from initial pilot projects to organization-wide deployments [14]. Companies already invested in **Apache Spark and big data technologies** have natural migration paths and reduced learning curves [5]. **Government organizations and research institutions** like Navy Federal and Westat show strong expansion patterns when supporting **hundreds of projects requiring centralized governance** [16]. These customers understand the platform value and have **budget authority for enterprise-scale deployments**. | [14], [5], [16] | | 5 | What do our competitors' best customers have in common? | Competitor customers often prioritize **simpler, single-purpose solutions** over comprehensive platforms [10]. **Palantir customers** focus heavily on complex data fusion and security features for specialized government and defense applications [12]. **Snowflake users** prefer fully managed cloud data warehouse experiences with predictable SQL performance [10]. **Microsoft Fabric adopters** benefit from bundled pricing within broader enterprise agreements and existing Microsoft tool ecosystems [12]. Opportunity exists with customers frustrated by **vendor lock-in and seeking open-source foundations** [6]. | [10], [12], [6] | ## Target Segmentation ### πŸ₯‡ Primary Fortune 500 Data-Driven Enterprises **Industry:** Technology, Financial Services, Retail, Media **Company Size:** 1,000-50,000+ employees **Key Characteristics:** β€’ **Mature data organizations**: Dedicated data science, engineering, and analytics teams requiring unified collaboration [16] β€’ **Complex compliance requirements**: Government and financial services needing regulatory features and governance [16] β€’ **Existing big data investments**: Organizations already using Apache Spark and similar technologies [5] **Rationale:** Highest revenue potential with $3B ARR from this segment. Proven willingness to adopt consumption-based enterprise pricing. ### πŸ₯ˆ Secondary High-Growth Mid-Market Technology Companies **Industry:** Technology, SaaS, E-commerce **Company Size:** 200-1,000 employees **Key Characteristics:** β€’ **Rapid scaling needs**: Companies transitioning from basic analytics to advanced AI capabilities [8] β€’ **Cloud-first architecture**: Organizations building modern data stacks without legacy constraints [8] β€’ **Real-time analytics requirements**: Businesses needing dashboards and predictive modeling for growth [8] **Rationale:** Strong growth segment with increasing data complexity. Easier implementation than enterprise but smaller initial contract values. ### πŸ₯‰ Tertiary Government & Research Institutions **Industry:** Government, Defense, Healthcare, Academia **Company Size:** 500-10,000+ employees **Key Characteristics:** β€’ **Multi-project governance**: Organizations like Westat supporting hundreds of research projects [16] β€’ **Strict security requirements**: Government agencies needing advanced data fusion and compliance [16] β€’ **Long-term research initiatives**: Institutions requiring stable platforms for ongoing studies [16] **Rationale:** Strategic value with strong expansion potential. Longer sales cycles but high customer lifetime value and reference potential. ## Target Personas ### Persona 1: Marcus, The Enterprise Data Platform Director *Segment: πŸ₯‡ Primary* **Demographics:** - Name: **Marcus, The Enterprise Data Platform Director** - Age: **πŸ‘€ Age**: 38-45 - Job Title: **πŸ’Ό Job Title/Role**: Director of Data Platform Engineering - Industry: **🏒 Industry**: Financial Services, Technology, Retail - Company Size: **πŸ‘₯ Company Size**: 5,000-25,000 employees - Education: **πŸŽ“ Education Degree**: MS in Computer Science or Data Science - Location: **πŸ“ Location**: Major metropolitan areas (SF Bay, NYC, Seattle) - Years of Experience: **⏱️ Years of Experience**: 12-18 years **πŸ’­ Motivation:** Seeks to **modernize fragmented data infrastructure** and eliminate silos between data lakes and warehouses. Frustrated by **complex governance challenges** across multiple analytics teams and compliance requirements. Needs to demonstrate **ROI on enterprise data investments** while enabling faster AI adoption. **🎯 Goals:** - Unify data governance across 200+ data science and engineering team members - Achieve 40% cost reduction by consolidating multiple data platforms - Enable real-time analytics and ML deployment within 6 months **😀 Pain Points:** - Managing separate systems for data storage, processing, and analytics creates bottlenecks - Regulatory compliance requirements demand centralized governance that current tools lack - Unpredictable costs from multiple vendor relationships strain enterprise budgets ### Persona 2: Sarah, The Scale-Up Head of Analytics *Segment: πŸ₯ˆ Secondary* **Demographics:** - Name: **Sarah, The Scale-Up Head of Analytics** - Age: **πŸ‘€ Age**: 32-38 - Job Title: **πŸ’Ό Job Title/Role**: Head of Data & Analytics - Industry: **🏒 Industry**: SaaS, E-commerce, FinTech - Company Size: **πŸ‘₯ Company Size**: 300-800 employees - Education: **πŸŽ“ Education Degree**: MS in Statistics or Analytics - Location: **πŸ“ Location**: Tech hubs (Austin, Denver, Boston) - Years of Experience: **⏱️ Years of Experience**: 8-12 years **πŸ’­ Motivation:** Needs to **scale analytics capabilities** as company grows from startup to enterprise without rebuilding infrastructure. Wants **cloud-first solutions** that eliminate legacy constraints and support rapid iteration. Seeks to **prove analytics value** to executive team through faster insights. **🎯 Goals:** - Build scalable data platform supporting 50+ person growth in next 18 months - Implement real-time customer analytics driving 25% engagement improvement - Establish ML-driven product features generating measurable revenue impact **😀 Pain Points:** - Current analytics stack cannot handle rapid data volume growth from business expansion - Limited budget requires careful vendor selection and predictable pricing models - Lack of real-time capabilities prevents competitive customer experience features ### Persona 3: David, The Government Research IT Director *Segment: πŸ₯‰ Tertiary* **Demographics:** - Name: **David, The Government Research IT Director** - Age: **πŸ‘€ Age**: 42-50 - Job Title: **πŸ’Ό Job Title/Role**: IT Director for Research Operations - Industry: **🏒 Industry**: Government, Defense, Healthcare Research - Company Size: **πŸ‘₯ Company Size**: 2,000-8,000 employees - Education: **πŸŽ“ Education Degree**: MS in Information Systems or Engineering - Location: **πŸ“ Location**: DC Metro, government contracting hubs - Years of Experience: **⏱️ Years of Experience**: 15-22 years **πŸ’­ Motivation:** Requires **centralized platform supporting hundreds of research projects** with strict security and compliance standards. Seeks **long-term stable solution** that provides full financial visibility and governance. Needs to **enable research teams** while maintaining rigorous data protection protocols. **🎯 Goals:** - Implement unified data governance across 300+ concurrent research projects - Achieve FedRAMP compliance while enabling advanced analytics capabilities - Reduce project setup time from 6 weeks to 2 weeks through platform standardization **😀 Pain Points:** - Complex security requirements limit vendor options and slow implementation timelines - Multiple research teams require isolated environments with centralized oversight - Long procurement cycles demand vendors with proven government sector experience --- # Positioning & Messaging ## Positioning Statement **Databricks** is a **unified data and AI platform** for **Fortune 500 enterprises** that **eliminates data silos while enabling any AI use case** with/because of **lakehouse architecture that combines the best of data lakes and warehouses** ## Positioning Framework ### 1. Needs and Pain Points What are their customer's needs and pain points around the problem the product is trying to solve? β€’ Organizations struggle with fragmented data infrastructure that creates silos between data lakes and warehouses [6] β€’ Data teams face challenges managing separate systems for storage, processing, and analytics [6] β€’ Companies need unified governance across batch and streaming data pipelines [7] β€’ Traditional architectures create bottlenecks between data engineering and data science workflows [7] β€’ Organizations require scalable solutions that support both traditional analytics and modern AI workloads [6] ### 2. Product Features What product features will address these needs and solve these pain points? β€’ Data Intelligence Platform unifies data engineering, analytics, BI, data science, and machine learning workloads [9] β€’ Lakeflow enables reliable ETL pipelines for both batch and streaming data at scale [7] β€’ Unity Catalog provides centralized governance and security across all data assets [16] β€’ Serverless compute options reduce infrastructure management overhead [7] β€’ Built-in collaborative notebooks and MLOps capabilities accelerate time-to-value [7] ### 3. Key Benefits What are the key benefits (rational and emotional) of those product features? β€’ Unified lakehouse architecture eliminates data silos while reducing costs [6] β€’ Single platform handles everything from real-time dashboards to advanced machine learning [8] β€’ Open lakehouse foundation supports Delta Lake format for ACID transactions and time travel [9] β€’ Consumption-based pricing model that scales with actual usage [12] β€’ Native integration with major cloud providers without vendor lock-in [8] ### 4. Benefit Pillars Which of those benefits would be categorized as benefit pillars? πŸ—οΈ Unified Data Architecture, ⚑ Enterprise-Scale Performance, πŸ”’ Open Multi-Cloud Freedom ### 5. Emotional Benefits What emotional benefits would the user have when they engage with or use the product? Core Emotional Promise: Empowers data teams to solve the world's toughest problems with confidence through unified platform capabilities [7] Supporting Emotions: β€’ Relief from managing fragmented systems and complex vendor relationships [6] β€’ Confidence in enterprise-grade governance and compliance for critical business decisions [16] β€’ Pride in delivering faster insights and smarter decision-making across the organization [8] ### 6. Positioning Statement What are some positioning statements that could reflect its key benefits, product features, and value? Databricks is a unified data and AI platform for Fortune 500 enterprises that eliminates data silos while enabling any AI use case with lakehouse architecture that combines the best of data lakes and warehouses [6] [17] ### 7. Competitive Differentiation How do they differentiate from other competitors? Databricks uniquely combines unified lakehouse architecture with open-source foundations and consumption-based pricing [6] [12] vs. Snowflake: Databricks provides comprehensive data and AI platform while Snowflake focuses on fully managed cloud data warehouse [10] vs. Palantir: Databricks offers consumption-based pricing versus Palantir's significant upfront investment requirements [12] vs. Microsoft Fabric: Databricks maintains multi-cloud flexibility without vendor lock-in constraints [8] Key Differentiators: β€’ First unified lakehouse architecture eliminating traditional data lake/warehouse separation [6] β€’ Open-source foundation with Delta Lake and MLflow reduces proprietary lock-in concerns [6] β€’ Proven Fortune 500 adoption with over 40% penetration demonstrating enterprise-scale capability [17] ## Messaging Guide | # | Type | Message | Priority | |---|------|---------|----------| | 1 | 🎯 Top-Line Message | The unified data and AI platform that eliminates silos and empowers Fortune 500 enterprises to solve their toughest problems with lakehouse architecture [6] [17] | Primary | | 2 | πŸ—οΈ Unified Data Architecture | Break down data silos with the first lakehouse platform that combines data lakes and warehouses into one unified solution [6] | High | | 3 | πŸ—οΈ Unified Data Architecture | Unify data engineering, analytics, BI, data science, and machine learning workloads on a single platform [9] | High | | 4 | πŸ—οΈ Unified Data Architecture | Transform fragmented data infrastructure into a cohesive foundation that supports any AI use case [6] | Medium | | 5 | ⚑ Enterprise-Scale Performance | Trusted by over 40% of Fortune 500 companies including Comcast, CondΓ© Nast, and H&M for mission-critical workloads [17] | High | | 6 | ⚑ Enterprise-Scale Performance | Scale from real-time dashboards to advanced machine learning with serverless compute that reduces infrastructure overhead [7] | High | | 7 | ⚑ Enterprise-Scale Performance | Handle both batch and streaming data pipelines at enterprise scale with Lakeflow's unified orchestration [7] | Medium | | 8 | πŸ”’ Open Multi-Cloud Freedom | Avoid vendor lock-in with open-source foundations including Delta Lake and MLflow that work across any cloud [6] | High | | 9 | πŸ”’ Open Multi-Cloud Freedom | Deploy natively across AWS, Microsoft Azure, and Google Cloud with deep integrations but no proprietary constraints [8] | High | | 10 | πŸ”’ Open Multi-Cloud Freedom | Control costs with consumption-based pricing that scales with actual usage, not upfront enterprise licensing [12] | Medium | --- # References [1] Databricks - Wikipedia https://en.wikipedia.org/wiki/Databricks [2] Databricks revenue, valuation & funding | Sacra https://sacra.com/c/databricks/ [3] How Databricks hit $3.7B revenue and 10K customers in 2025. https://getlatka.com/companies/databricks [4] Databricks is Raising $10B Series J Investment at $62B Valuation - Databricks https://www.databricks.com/company/newsroom/press-releases/databricks-raising-10b-series-j-investment-62b-valuation [5] MicroVentures’ Portfolio Company: Databricks’ History and Milestones https://microventures.com/microventures-portfolio-company-databricks-history-and-milestones [6] Data Lakehouse Architecture | Databricks https://www.databricks.com/product/data-lakehouse [7] Databricks: Leading Data and AI Platform for Enterprises https://www.databricks.com/ [8] Azure Databricks | Microsoft Azure https://azure.microsoft.com/en-us/products/databricks [9] AWS Marketplace: Databricks Data Intelligence Platform https://aws.amazon.com/marketplace/pp/prodview-wtyi5lgtce6n6 [10] Databricks vs Snowflake: 5 key features compared (2026) https://www.flexera.com/blog/finops/snowflake-vs-databricks/ [11] Palantir vs. Snowflake vs. Databricks: Which one fits your Business? - i4C https://www.i4c.com/palantir-vs-snowflake-vs-databricks-which-one-fits-your-business/ [12] Top 5 Best Palantir Competitors in 2026 Led by Databricks Snowflake and Microsoft Fabric in Data AI Platforms https://www.ibtimes.com.au/top-5-best-palantir-competitors-2026-led-databricks-snowflake-microsoft-fabric-data-ai-platforms-1865435 [13] Customer Stories | Databricks https://www.databricks.com/customers [14] What is Customer Demographics and Target Market of Databricks Company? – CanvasBusinessModel.com https://canvasbusinessmodel.com/blogs/target-market/databricks-target-market [15] List of 1,000 Databricks Customers https://www.readycontacts.com/target-account-profiling/databricks/ [16] Data Intelligence in Action: 100+ Data and AI Use Cases from Databricks Customers | Databricks Blog https://www.databricks.com/blog/data-intelligence-action-100-data-and-ai-use-cases-databricks-customers [17] Databricks Launches Data Lakehouse for Retail and Consumer Goods Customers https://www.databricks.com/company/newsroom/press-releases/databricks-launches-data-lakehouse-for-retail-and-consumer-goods-customers [18] 1216 Databricks Customer Reviews & References | FeaturedCustomers https://www.featuredcustomers.com/vendor/databricks [19] 457 Databricks Case Studies, Success Stories, & Customer Stories | FeaturedCustomers https://www.featuredcustomers.com/vendor/databricks/case-studies [20] Featured Customers | Find B2B & SaaS Software & Services - Reviews, Testimonials & Case Studies https://www.featuredcustomers.com/vendor/databricks/testimonials