Notes moyennes

7 avis
  • Note globale 4.7 / 5
  • Facilité d'utilisation 4.4 / 5
  • Service client 4.7 / 5
  • Fonctionnalités 4.7 / 5
  • Rapport qualité-prix 4.2 / 5

Informations sur le produit

  • À partir de 99,00 $US/mois
  • Version gratuite Oui
  • Essai gratuit Oui
  • Déploiement Cloud, SaaS, web
  • Formation Webinaires
    Documentation
  • Ressources d'aide Support en horaire de bureau
    En ligne

Informations sur l'éditeur

  • Databricks
  • https://databricks.com/

En savoir plus sur Databricks

La science des données simplifiée, de l'insertion à la production.

Databricks - Fonctionnalités

  • Analyse de régression
  • Analyse de texte
  • Analyse des sentiments
  • Analyse prédictive
  • Data discovery
  • Modélisation statistique
  • Traitement des gros volumes
  • Visualisation de données
  • Analyse prédictive
  • Collaboration
  • Entreposage de données
  • Exploration de données (data mining)
  • Fusions de données
  • Modèles
  • Nettoyage de données
  • Sandbox sans code
  • Traitement des gros volumes
  • Visualisation de données

Logiciels équivalents

Avis les plus utiles sur Databricks

Powerful tool for dev ops of machine learning models

Publié le 01/09/2019
Rayla V.
Graduate Research Assistant
Enseignement supérieur, 51-200 employés
Temps d'utilisation du produit: plus d'un an
Provenance de l'utilisateur 
5/5
Note globale
4 / 5
Facilité d'utilisation
4 / 5
Fonctionnalités
Support client
Rapport qualité-prix
Probabilité de recommander le produit :
Très faible Très élevée

Commentaires: 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.

Avantages: 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.

Inconvénients: 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.

An unified platform to develop high quality analysis

Publié le 22/11/2019
Utilisateur vérifié
Business Analyst
Services financiers, 1 001-5 000 employés
Temps d'utilisation du produit: 6 à 12 mois
Provenance de l'utilisateur 
5/5
Note globale
5 / 5
Facilité d'utilisation
5 / 5
Fonctionnalités
4 / 5
Support client
4 / 5
Rapport qualité-prix
Probabilité de recommander le produit :
Très faible Très élevée

Commentaires: Databricks is allowing data analysis that other systems could not perform at the same performance because it is a platform that integrates huge amounts of cloud data with Scala, Python, SQL or R notebooks in a user-friendly interface. Due to the features of Databricks, daily work seems more efficient and less bureaucratic.

Avantages: What I like most about Databricks is the amount of integrations the platform provides to the user. With Databricks, you can create datasets, develop machine learning models, and analyze performance automatically by setting up a job periodically. Whether the user is an engineer, data scientist, or business analyst, Databricks can streamline everyone's work.

Inconvénients: What I least like about Databricks is the instability that usually occurs when there are too many users trying to run their notebooks on the same cluster at the same time.

Databricks Review

Publié le 31/10/2019
Utilisateur vérifié
Principal Consultant
Conseil en gestion, 10 001+ employés
Temps d'utilisation du produit: plus d'un an
Provenance de l'utilisateur 
5/5
Note globale
5 / 5
Facilité d'utilisation
5 / 5
Fonctionnalités
5 / 5
Support client
4 / 5
Rapport qualité-prix
Probabilité de recommander le produit :
Très faible Très élevée

Commentaires: Very good. It made analyzing big data a lot easier

Avantages: This product has democratized big data computation. Its very easy to move from any platform to this product as it supports most of the languages.

Inconvénients: Nothing so far- may be cost of computation can improve over time but still an economical product to build in-house big data capability.

Excellent for data analysis

Publié le 25/02/2019
Douglas F.
Senior business analyst
Services financiers, 1 001-5 000 employés
Temps d'utilisation du produit: 6 à 12 mois
Provenance de l'utilisateur 
5/5
Note globale
4 / 5
Facilité d'utilisation
5 / 5
Fonctionnalités
Support client
5 / 5
Rapport qualité-prix
Probabilité de recommander le produit :
Très faible Très élevée

Commentaires: Excellent. Very fast and easy to use. Also it is easy to get help in the documentation. No lags, and support a big number of users.

Avantages: The access and manipulation of data. The software is very fast and great to manipulate and treat data. Also it is possible to build models.

Inconvénients: The lack of options of visualization and creation of dashboards.
The creation of dashboards is possible, but is not intuitive.

Great tool for the toolbox

Publié le 23/07/2019
Robert G.
DB Architect
Construction, 5 001-10 000 employés
Temps d'utilisation du produit: 1 à 5 mois
Provenance de l'utilisateur 
4/5
Note globale
4 / 5
Facilité d'utilisation
5 / 5
Fonctionnalités
Support client
Rapport qualité-prix
Probabilité de recommander le produit :
Très faible Très élevée

Avantages: I'm a SQL person, so being able to run big data analytics in my preferred language was quite nice. Being able to (near) seamlessly swap between Scala, SQL, and python in the same script is quite powerful. If you don't know how to do something easily in one language, do it in another and then swap back. It's pretty performant and querying non-indexed data dumped from the source systems, even if those datasets aren;t quite "big data". I found it to be quicker to dump 100mil rows of staged date from our on-prem server to the data lake and crunch it in Databricks than it was to run in SQL.

Inconvénients: I wasn't involved in the pricing piece, but from what I understand it's fairly expensive. The clusters can be spun up or down as needed, and there's a nice inactivity shutdown feature if you forget to turn off a test cluster, or something. I also had a pretty rough time getting an Azure Gen 2 Data Lake connected, but after finding the not-so-well-documented bug, it wasn't a big deal.

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