1. [Home](https://www.g2.com/) 2. ... 3. [Database Software](https://www.g2.com/categories/database-software) 4. [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) 5. [Databricks](https://www.g2.com/products/databricks/reviews) 6. [Databricks Claims vs Evidence](https://www.g2.com/products/databricks/claims-vs-evidence) # Databricks Claims vs Evidence G2 compares Databricks marketing claims from the sources linked below with real user reviews, then assesses how strongly the review evidence supports each claim. Last updated Sep 1, 2026 ## Claim: “Delta Sharing](https://www.databricks.com/product/delta-sharing) provides an open solution to securely share live data from your lakehouse to any computing platform without replication and complicated ETL.” Supported by reviews. Most reviewers report that Delta Sharing enables sharing data at scale without copying it or requiring complicated integrations, though a small number note that recipients may need Databricks installed. - “I use Databricks to manage my enterprise data, and it solves the issue of sharing data at scale for reporting and analytics with my customers. I value Delta sharing, now called open sharing, because it allows me to share data at scale without requiring customers to set up specific APIs or virtual pipelines.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12968392) - “Native connectors to Azure, Unity Catalog, and Delta Sharing mean we spend less time on plumbing. Lakehouse Federation lets us query external sources without moving data, which was an unexpected win.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12553474) - “Databricksで気に入っているのは、Delta Sharingを使うことでゼロコピーでデータを集められる点です。これにより、SFTP連携やAPI連携の構築のための基盤設定が不要になり、時間がかかるプロセスを省略できるところがとても助かります。また、Databricksの初期設定も非常に楽でした。” [Read Full Review](https://www.g2.com/products/databricks/reviews/12968181) Source: [https://databricks.com/product/data-lakehouse](https://databricks.com/product/data-lakehouse) ## Claim: “The [Databricks Data + AI Platform](https://www.databricks.com/product/data-intelligence-platform) is built on lakehouse architecture, which combines the best elements of data lakes and data warehouses to help you reduce costs and deliver on your data and AI initiatives faster.” Mixed support from reviews. Reviewers are split — many report that the lakehouse unifies data lakes and warehouses, reduces operational costs, and speeds delivery, while a comparable number describe costs rising quickly with usage and cluster management. - “The Lakehouse concept of bringing warehousing and lake environments under one roof won me over.” [Read Full Review](https://www.g2.com/products/databricks/reviews/13213715) - “It's been great for helping my client with their ROI and reducing operational costs by about 20%.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12972737) - “Now it takes minutes, rather than hours to run SQL queries or PySpark jobs to hunt for indicators of compromise (IOCs) across months of historical data.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12912387) - “The DBUs cost model can scale rapidly and become quite expensive if cluster policies and auto-scaling boundaries are not strictly monitored and optimized.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12973553) Source: [https://databricks.com/product/data-lakehouse](https://databricks.com/product/data-lakehouse) ## Claim: “Automatic optimization for performance and storage ensures the lowest TCO of any data platform together with [world-record-setting performance](https://www.databricks.com/blog/2021/11/02/databricks-sets-official-data-warehousing-performance-record.html) for both data warehousing and AI use cases — including generative techniques like large language models (LLMs).” Mixed support from reviews. Reviewers are split — many report strong performance, scalable processing, and optimization that reduces processing time, while a comparable number describe high or unpredictable costs, performance slowdowns, and the need for manual tuning. - “the Photon engine delivers real performance gains on SQL workloads without requiring any code changes.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12555876) - “Cost management can be tricky—DBUs add up quickly if you’re not careful with cluster sizing and warehouse auto-scaling.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12555876) - “The performance of Photon engine is very fast. Now it takes minutes, rather than hours to run SQL queries or PySpark jobs” [Read Full Review](https://www.g2.com/products/databricks/reviews/12912387) - “Databricks delivers excellent performance when working with large volumes of data.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12904379) Source: [https://databricks.com/product/data-lakehouse](https://databricks.com/product/data-lakehouse) ## Claim: “With Databricks, your data is always under your control, free from proprietary formats and closed ecosystems.” Mixed support from reviews. Reviewers are split — many report open standards, file-format flexibility, and reduced vendor lock-in, while a comparable number describe ecosystem dependency, rising switching costs, or interoperability limitations. - “I like that Databricks is fairly straightforward for nontechnical users to get up and running, yet it includes open standards. For more advanced users, you can plug in your favorite open source data tooling.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12969167) - “I use Databricks for data engineering and analytics. It allows for quick onboarding of data pipelines and is file format agnostic.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12968563) - “The open source nature of technologies and eliminates vendor lockin” [Read Full Review](https://www.g2.com/products/databricks/reviews/12968528) - “Cost management is a watchout. Photon, Job Clusters, and Instance Pools can lower costs dramatically, but only when workloads are well-designed. Poorly tuned jobs, idle clusters, oversized compute, or duplicated pipelines can still burn budget fast. Finally, the platform creates some ecosystem dependency: once reconciliation, ingestion, orchestration, governance, and reporting patterns are Databricks-native, switching costs rise. In short, Databricks is a strong enterprise platform, but it rewards disciplined engineering and punishes casual adoption.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12969107) Source: [https://databricks.com/product/data-lakehouse](https://databricks.com/product/data-lakehouse) ## Claim: “Built on open source and open standards, a lakehouse simplifies your data estate by eliminating the silos that historically complicate data and AI.” Supported by reviews. Most reviewers report that Databricks unifies data and AI work, reduces silos, and supports open source and open standards, though a small number note interoperability gaps and vendor lock-in risks. - “I use Databricks to transform and analyze large volumes of data, and it allows me to unify data from various SQL functions to cloud storage systems, which makes analysis easier. I find eliminating information silos vital for team collaboration and appreciate the optimized Spark engine for its performance with large volumes of data. I appreciate its compatibility with languages like Python, Scala, and SQL, making it comfortable for every team member to work with Databricks. It fosters smoother communication and allows for making immediate adjustments, which is something I value....” [Read Full Review](https://www.g2.com/products/databricks/reviews/13374764) - “I like that Databricks is fairly straightforward for nontechnical users to get up and running, yet it includes open standards. For more advanced users, you can plug in your favorite open source data tooling.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12969167) - “The open source nature of technologies and eliminates vendor lockin” [Read Full Review](https://www.g2.com/products/databricks/reviews/12968528) - “As a proprietary platform built on open-source foundations, it can still introduce vendor lock-in risks, particularly through components such as Unity Catalog and its custom APIs.” [Read Full Review](https://www.g2.com/products/databricks/reviews/12578794) Source: [https://databricks.com/product/data-lakehouse](https://databricks.com/product/data-lakehouse)