Dataiku vs. Grafana

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

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Just about any organization with more than one server and more than one cluster as it scales very well. Configuration of the application takes time and finesse to fine tune to where the balance of load time and getting data quickly meets. The plugins add load time but fine tuning for the application to meet demand needs nailed down at implementation

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• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

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• Alerting through many different medium as Slack, Email, Webhook etc
• Beautiful and unlimited number of dashboards to view your metrics and tweak them as you please
• Log aggregation and powerful Logql to filter and view your logs
• Microservices monitoring
• Large number of plugins and data sources to collect your metrics from almost anywhere

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• End product deployment.

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• There are some settings which we can't configure from UI (Web Console).
• We've open[ed] up configuration files in command line text editors and manually do the settings e.g. LDAP/SSO configuration.
• In terms of visualization, it's best, but it doesn't support log analysis otherwise it could destroy business of all other visualization tools.

As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

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It is infinitely flexible. If you can imagine it, Grafana can almost certainly do it. Usability may be in the eye of the beholder however, as there is time needed to curate the experience and get the dashboards customized to how it makes sense to you. I know one thing they are working on are more templates, based on data sources

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The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

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Grafana blows Nagios out of the water when it comes to customization. The ability to feed almost any data source makes it very versatile and the cost is great.

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• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

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• Grafana has replaced many higher priced tools
• The integrations are seemless with multiple backends
• Combining graphs and dashboards from multiple data sources is a game changer

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