--- description: Detaillierte Informationen über Databricks zu Benutzerfreundlichkeit, Funktionen, Kosten, Vor- und Nachteilen aus verifizierten Nutzer-Erfahrungen. Lies Ratings & Bewertungen und entdecke ähnliche Tools. image: https://gdm-localsites-assets-gfprod.imgix.net/images/capterra/og_logo-e5a8c001ed0bd1bb922639230fcea71a.png?auto=format%2Cenhance%2Ccompress title: Databricks Erfahrungen, Vor- und Nachteile – Capterra Deutschland 2026 --- Breadcrumb: [Startseite](/) > [Big Data Tools](/directory/30851/big-data/software) > [Databricks](/software/148499/databricks) # Databricks Canonical: https://www.capterra.com.de/software/148499/databricks Page: 1 / 2\ Weiter: [Next page](https://www.capterra.com.de/software/148499/databricks?page=2) > Datenwissenschaft leicht gemacht, von der Eingabe bis zur Produktion. > > Verdict: Rated **4.5/5** by 22 users. Top-rated for **Weiterempfehlungsquote**. ----- ## Quick Stats & Ratings | Metric | Bewertung | Detail | | **Gesamt** | **4.5/5** | 22 Nutzerbewertungen | | Bedienkomfort | 4.7/5 | Based on overall reviews | | Kundenbetreuung | 4.4/5 | Based on overall reviews | | Preis-Leistungs-Verhältnis | 4.2/5 | Based on overall reviews | | Funktionen | 4.6/5 | Based on overall reviews | | Recommendation percentage | 90% | (9/10 Weiterempfehlungsquote) | ## Über den Anbieter - **Unternehmen**: Databricks - **Founded**: 2013 ## Commercial Context - **Startpreis**: 99,00 $ - **Preismodell**: (Free version available) (Kostenlose Testversion) - **Target Audience**: Selbstständig, 2–10, 11–50, 51–200, 201–500, 501–1.000, 1.001–5.000 - **Deployment & Platforms**: Cloud, SaaS, webbasiert - **Unterstützte Sprachen**: Englisch - **Available Countries**: Vereinigte Staaten ## Funktionen - Anpassbares Dashboard - Berichterstattung / Analyse - Dashboard - Data Warehousing (Datenlager) - Datenerfassung und Übertragung - Datenmanagement - Datensicherheit - Datenvisualisierung - Datenvorbereitung in Selbstbedienung - Information Governance - Visuelle Analytik - Werkzeuge zur Zusammenarbeit ## Optionen für Kundensupport - Chat ## Category - [Big Data Tools](https://www.capterra.com.de/directory/30851/big-data/software) ## Ähnliche Kategorien - [Big Data Tools](https://www.capterra.com.de/directory/30851/big-data/software) - [Datenanalyse Tools](https://www.capterra.com.de/directory/31077/data-analysis/software) - [Datenmanagement Software](https://www.capterra.com.de/directory/31003/data-management/software) - [KI Tools](https://www.capterra.com.de/directory/30938/artificial-intelligence/software) - [BI-Tools](https://www.capterra.com.de/directory/23/business-intelligence/software) ## Alternativen 1. [Google Analytics 360](https://www.capterra.com.de/software/177042/google-analytics) — 4.7/5 (8088 reviews) 2. [Microsoft Power BI](https://www.capterra.com.de/software/176586/power-bi) — 4.6/5 (1865 reviews) 3. [Tableau](https://www.capterra.com.de/software/77260/tableau) — 4.6/5 (2347 reviews) 4. [Google Cloud](https://www.capterra.com.de/software/170983/google-cloud-platform) — 4.7/5 (2242 reviews) 5. [Zoho Analytics](https://www.capterra.com.de/software/129749/zoho-analytics) — 4.4/5 (360 reviews) ## Nutzerbewertungen ### "A fantastic all-in-one-place platform for big data analytics" — 5.0/5 > **Filippo** | *21. September 2023* | Marketing & Werbung | Recommendation rating: 10.0/10 > > **Vorteile**: Databricks can be integrated with the most used cloud services (Amazon, Google, Microsoft) and you can create and manages computation clusters, write code and see results in a single platform instead of having multiple separate services. Its dashboard is really easy to use and allows you to focus on code development. It also supports notebooks versioning with GitLab. > > **Nachteile**: The data visualization part could really be improved. > > We are moving all our big data computation on the Databricks platform. One of the main advantages is the possibility to plan notebooks execution through the workflow section. ----- ### "Databricks: Unleashing Data/AI Potential for Next-Level Analytics and Collaboration" — 4.0/5 > **Verifizierter Rezensent** | *2. Oktober 2023* | Informationstechnologie & -dienste | Recommendation rating: 9.0/10 > > **Vorteile**: unified lakehouse architecture platform with good collaborationmanaged infrastructure with autoscaling \& serverless functionalityautoloader, schema evolution \& delta live tablesunit catalog \& delta sharing for unified governanceMLflow \& integrations with partner connectVersion control databricks repos, secret with security \& complianceReal time processing > > **Nachteile**: Cost \& capacity planning for clusters if you don't use DLT/serverless pipelinesPerformance variabilitySome vendor lock-in if you use "DELTA" format with delta live table > > Very good , able to created end-end lakehouse platform for leading healthcare client ----- ### "Very good to handle very big data" — 3.0/5 > **Verifizierter Rezensent** | *29. September 2020* | Bankwesen | Recommendation rating: 8.0/10 > > **Vorteile**: - Enables simultaneous collaborative work with colleagues - Easy to mix spark queries and python for extra analyses and plots - Handful visualization modes for query results (tables and plots with aggregations) > > **Nachteile**: - Hard to manage notebook workspace - Sometimes it gets really slow to run queries - AFAIK, there aren't visualization options for datasets (without running queries) > > While it supports python, when I need to use it, I ultimately prefer to sample or aggregate and export data to work in another environment. For this end, it works very well. ----- ### "Powerful tool for dev ops of machine learning models" — 5.0/5 > **Rayla** | *1. September 2019* | Hochschulbildung | Recommendation rating: 8.0/10 > > **Vorteile**: I love how easy it is to deploy auto-scaling machine learning models. After a machine learning model is trained, you can just click a button to deploy the model, I believe in a container, and have it auto scale as needed. You can also specify the minimum and maximum size of the deployment to reduce costs but to keep up with the workload as necessary. It is also built around Spark, so tasks involving "big data" aren't an issue. > > **Nachteile**: Some of the cons are that the primary language is Java/Scala, whereas many data scientists are using python or R, which run slower on Databricks than Java and Scala. Also, the main interface via coding, which can limit a lot of citizen data scientists. > > Overall, my experience with Databricks has been very positive. It is a powerful tool to enable data scientists without a lot of data engineering skills. However, you need to be a data scientist or machine learning engineer to be able to take advantage of its power for machine learning. ----- ### "Best Big Data Analysis Platform" — 5.0/5 > **Verifizierter Rezensent** | *22. Januar 2023* | Informationstechnologie & -dienste | Recommendation rating: 9.0/10 > > **Vorteile**: Databricks is a great platform for working with a huge amount of data. But the most interesting feature is the ability to use Magic query. Magic query allows users to write code in multiple languages in the same notebook. > > **Nachteile**: Databricks is a go-to platform for most of the analysis and processing workload, but when it comes to the financials, it becomes a little more expensive. And as a result, not a lot of the projects are reliable to be developed in Databricks. ----- Page: 1 / 2\ Weiter: [Next page](https://www.capterra.com.de/software/148499/databricks?page=2) ## Links - [View on Capterra](https://www.capterra.com.de/software/148499/databricks) ## This page is available in the following languages | Locale | URL | | de | | | de-AT | | | de-CH | | | en | | | en-AE | | | en-AU | | | en-CA | | | en-GB | | | en-IE | | | en-IL | | | en-IN | | | en-NZ | | | en-SG | | | en-ZA | | | fr | | | fr-BE | | | fr-CA | | | fr-LU | | | ko | | ----- ## Structured Data