Alternatives to Redash logo

Alternatives to Redash

Tableau, Periscope, Looker, Metabase, and Mode are the most popular alternatives and competitors to Redash.
284
388
+ 1
8

What is Redash and what are its top alternatives?

Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.
Redash is a tool in the Business Intelligence category of a tech stack.
Redash is an open source tool with 20K GitHub stars and 3.5K GitHub forks. Here’s a link to Redash's open source repository on GitHub

Top Alternatives to Redash

  • Tableau

    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • Periscope

    Periscope

    Periscope is a data analysis tool that uses pre-emptive in-memory caching and statistical sampling to run data analyses really, really fast. ...

  • Looker

    Looker

    We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way. ...

  • Metabase

    Metabase

    It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating. ...

  • Mode

    Mode

    Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects. ...

  • Grafana

    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

  • Superset

    Superset

    Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought. ...

  • Kibana

    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

Redash alternatives & related posts

Tableau logo

Tableau

915
952
4
Tableau helps people see and understand data.
915
952
+ 1
4
PROS OF TABLEAU
  • 3
    Capable of visualising billions of rows
  • 1
    Responsive
CONS OF TABLEAU
    Be the first to leave a con

    related Tableau posts

    Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

    See more
    Periscope logo

    Periscope

    40
    80
    10
    Periscope plugs directly into your database and lets you run, save and share analyses over billions of data...
    40
    80
    + 1
    10
    PROS OF PERISCOPE
    • 6
      Great for learning and teaching people SQL
    • 4
      Gorgeous "share-able" and "embeddable" dashboards
    CONS OF PERISCOPE
      Be the first to leave a con

      related Periscope posts

      Looker logo

      Looker

      410
      455
      9
      Pioneering the next generation of BI, data discovery & data analytics
      410
      455
      + 1
      9
      PROS OF LOOKER
      • 4
        Real time in app customer chat support
      • 4
        GitHub integration
      • 1
        Reduces the barrier of entry to utilizing data
      CONS OF LOOKER
      • 1
        Price

      related Looker posts

      Mohan Ramanujam

      We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.

      See more
      Metabase logo

      Metabase

      659
      944
      245
      An open-source business intelligence tool
      659
      944
      + 1
      245
      PROS OF METABASE
      • 54
        Database visualisation
      • 41
        Open Source
      • 39
        Easy setup
      • 33
        Dashboard out of the box
      • 18
        Free
      • 12
        Simple
      • 8
        Support for many dbs
      • 7
        Easy embedding
      • 6
        It's good
      • 6
        Easy
      • 5
        AGPL : wont help with adoption but depends on your goal
      • 5
        BI doesn't get easier than that
      • 4
        Multiple integrations
      • 4
        Google analytics integration
      • 3
        Easy set up
      CONS OF METABASE
      • 3
        Harder to setup than similar tools

      related Metabase posts

      Mode logo

      Mode

      104
      174
      13
      SQL-based analytics tool that helps analysts query, visualize, and share data.
      104
      174
      + 1
      13
      PROS OF MODE
      • 3
        Empowering for SQL-first analysts
      • 3
        Collaborative query building
      • 2
        Easy report building
      • 2
        Integrated IDE with SQL + Python for analysis
      • 1
        Auto SQL query to Python dataframe
      • 1
        Awesome online and chat support
      • 1
        In-app customer chat support
      CONS OF MODE
        Be the first to leave a con

        related Mode posts

        Grafana logo

        Grafana

        11K
        8.6K
        398
        Open source Graphite & InfluxDB Dashboard and Graph Editor
        11K
        8.6K
        + 1
        398
        PROS OF GRAFANA
        • 84
          Beautiful
        • 67
          Graphs are interactive
        • 56
          Free
        • 55
          Easy
        • 33
          Nicer than the Graphite web interface
        • 24
          Many integrations
        • 16
          Can build dashboards
        • 10
          Easy to specify time window
        • 9
          Dashboards contain number tiles
        • 8
          Can collaborate on dashboards
        • 5
          Integration with InfluxDB
        • 5
          Click and drag to zoom in
        • 5
          Open Source
        • 4
          Authentification and users management
        • 4
          Threshold limits in graphs
        • 3
          It is open to cloud watch and many database
        • 2
          You can visualize real time data to put alerts
        • 2
          Great community support
        • 2
          Alerts
        • 2
          Simple and native support to Prometheus
        • 2
          You can use this for development to check memcache
        • 0
          Plugin visualizationa
        • 0
          Grapsh as code
        CONS OF GRAFANA
          Be the first to leave a con

          related Grafana posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.9M views

          Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

          By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

          To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

          https://eng.uber.com/m3/

          (GitHub : https://github.com/m3db/m3)

          See more
          Matt Menzenski
          Senior Software Engineering Manager at PayIt · | 13 upvotes · 84.2K views

          Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

          See more
          Superset logo

          Superset

          277
          714
          32
          Data exploration and visualization platform, by Airbnb
          277
          714
          + 1
          32
          PROS OF SUPERSET
          • 9
            Awesome interactive filtering
          • 6
            Wide SQL database support
          • 5
            Free
          • 5
            Shareable & editable dashboards
          • 3
            Easy to share charts & dasboards
          • 2
            User & Role Management
          • 2
            Great for data collaborating on data exploration
          CONS OF SUPERSET
          • 3
            Ugly GUI
          • 2
            It is difficult to install on the server
          • 2
            Link diff db together "Data Modeling "

          related Superset posts

          Julien DeFrance
          Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

          Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

          I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

          For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

          Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

          Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

          Future improvements / technology decisions included:

          Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

          As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

          One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

          See more
          Kibana logo

          Kibana

          14.9K
          11.5K
          255
          Visualize your Elasticsearch data and navigate the Elastic Stack
          14.9K
          11.5K
          + 1
          255
          PROS OF KIBANA
          • 88
            Easy to setup
          • 61
            Free
          • 44
            Can search text
          • 21
            Has pie chart
          • 13
            X-axis is not restricted to timestamp
          • 8
            Easy queries and is a good way to view logs
          • 6
            Supports Plugins
          • 3
            More "user-friendly"
          • 3
            Can build dashboards
          • 3
            Dev Tools
          • 2
            Easy to drill-down
          • 2
            Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
          • 1
            Up and running
          CONS OF KIBANA
          • 5
            Unintuituve
          • 3
            Elasticsearch is huge
          • 3
            Works on top of elastic only
          • 2
            Hardweight UI

          related Kibana posts

          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 4.6M views

          Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

          It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

          I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

          We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

          If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

          The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

          Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

          See more
          Patrick Sun
          Software Engineer at Stitch Fix · | 11 upvotes · 469.1K views

          Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

          Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

          See more