Alternatives to Stackdriver logo

Alternatives to Stackdriver

Datadog, New Relic, Splunk, ELK, and Prometheus are the most popular alternatives and competitors to Stackdriver.
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What is Stackdriver and what are its top alternatives?

Google Stackdriver provides powerful monitoring, logging, and diagnostics. It equips you with insight into the health, performance, and availability of cloud-powered applications, enabling you to find and fix issues faster.
Stackdriver is a tool in the Cloud Monitoring category of a tech stack.

Top Alternatives to Stackdriver

  • Datadog

    Datadog

    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...

  • New Relic

    New Relic

    The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too. ...

  • Splunk

    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • ELK

    ELK

    It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch. ...

  • Prometheus

    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • Sentry

    Sentry

    Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. ...

  • 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. ...

  • 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. ...

Stackdriver alternatives & related posts

Datadog logo

Datadog

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Unify logs, metrics, and traces from across your distributed infrastructure.
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PROS OF DATADOG
  • 133
    Monitoring for many apps (databases, web servers, etc)
  • 104
    Easy setup
  • 84
    Powerful ui
  • 81
    Powerful integrations
  • 68
    Great value
  • 52
    Great visualization
  • 44
    Events + metrics = clarity
  • 40
    Custom metrics
  • 39
    Notifications
  • 37
    Flexibility
  • 17
    Free & paid plans
  • 14
    Great customer support
  • 13
    Makes my life easier
  • 8
    Adapts automatically as i scale up
  • 8
    Easy setup and plugins
  • 6
    Super easy and powerful
  • 5
    In-context collaboration
  • 5
    AWS support
  • 4
    Rich in features
  • 3
    Docker support
  • 3
    Cost
  • 3
    Best than others
  • 2
    Source control and bug tracking
  • 2
    Easy to Analyze
  • 2
    Expensive
  • 2
    Cute logo
  • 2
    Good for Startups
  • 2
    Free setup
  • 2
    Monitor almost everything
  • 2
    Automation tools
  • 2
    Simple, powerful, great for infra
  • 2
    Full visibility of applications
  • 1
    Best in the field
CONS OF DATADOG
  • 16
    Expensive
  • 3
    No errors exception tracking
  • 2
    External Network Goes Down You Wont Be Logging
  • 1
    Complicated

related Datadog posts

Robert Zuber

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

See more

We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

See more
New Relic logo

New Relic

18.5K
6.7K
1.9K
New Relic is the industry’s largest and most comprehensive cloud-based observability platform.
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PROS OF NEW RELIC
  • 414
    Easy setup
  • 344
    Really powerful
  • 244
    Awesome visualization
  • 194
    Ease of use
  • 151
    Great ui
  • 107
    Free tier
  • 81
    Great tool for insights
  • 66
    Heroku Integration
  • 55
    Market leader
  • 49
    Peace of mind
  • 21
    Push notifications
  • 20
    Email notifications
  • 17
    Heroku Add-on
  • 16
    Error Detection and Alerting
  • 12
    Multiple language support
  • 11
    SQL Analysis
  • 11
    Server Resources Monitoring
  • 9
    Transaction Tracing
  • 8
    Apdex Scores
  • 8
    Azure Add-on
  • 7
    Analysis of CPU, Disk, Memory, and Network
  • 6
    Detailed reports
  • 6
    Performance of External Services
  • 6
    Error Analysis
  • 6
    Application Availability Monitoring and Alerting
  • 6
    Application Response Times
  • 5
    JVM Performance Analyzer (Java)
  • 5
    Most Time Consuming Transactions
  • 4
    Top Database Operations
  • 4
    Easy to use
  • 4
    Browser Transaction Tracing
  • 3
    Application Map
  • 3
    Pagoda Box integration
  • 3
    Custom Dashboards
  • 3
    Weekly Performance Email
  • 2
    Easy visibility
  • 2
    App Speed Index
  • 2
    Easy to setup
  • 1
    Real User Monitoring Analysis and Breakdown
  • 1
    Incident Detection and Alerting
  • 1
    Real User Monitoring Overview
  • 1
    Worst Transactions by User Dissatisfaction
  • 1
    Metric Data Resolution
  • 1
    Metric Data Retention
  • 1
    Team Collaboration Tools
  • 1
    Super Expensive
  • 1
    Time Comparisons
  • 1
    Access to Performance Data API
  • 1
    Background Jobs Transaction Analysis
  • 1
    Free
  • 1
    Best of the best, what more can you ask for
  • 1
    Best monitoring on the market
  • 1
    Rails integration
  • 0
    Exceptions
  • 0
    Ddd
CONS OF NEW RELIC
  • 19
    Pricing model doesn't suit microservices
  • 10
    UI isn't great
  • 7
    Visualizations aren't very helpful
  • 7
    Expensive
  • 5
    Hard to understand why things in your app are breaking

related New Relic posts

Farzeem Diamond Jiwani
Software Engineer at IVP · | 5 upvotes · 659.2K views

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

See more
Sebastian Gębski

Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).

See more
Splunk logo

Splunk

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728
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Search, monitor, analyze and visualize machine data
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PROS OF SPLUNK
  • 2
    API for searching logs, running reports
  • 1
    Query engine supports joining, aggregation, stats, etc
  • 1
    Query any log as key-value pairs
  • 1
    Splunk language supports string, date manip, math, etc
  • 1
    Granular scheduling and time window support
  • 1
    Alert system based on custom query results
  • 1
    Custom log parsing as well as automatic parsing
  • 1
    Dashboarding on any log contents
  • 1
    Ability to style search results into reports
  • 1
    Rich GUI for searching live logs
CONS OF SPLUNK
  • 1
    Splunk query language rich so lots to learn

related Splunk posts

Shared insights
on
KibanaKibanaSplunkSplunkGrafanaGrafana

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

See more
ELK logo

ELK

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The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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PROS OF ELK
  • 13
    Open source
  • 3
    Good for startups with monetary limitations
  • 2
    Can run locally
  • 1
    Easy to setup
  • 1
    External Network Goes Down You Aren't Without Logging
  • 0
    Json log supprt
  • 0
    Live logging
CONS OF ELK
  • 4
    Elastic Search is a resource hog
  • 3
    Logstash configuration is a pain
  • 1
    Bad for startups with personal limitations

related ELK posts

Wallace Alves
Cyber Security Analyst · | 1 upvote · 585.1K views

Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

See more
Prometheus logo

Prometheus

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An open-source service monitoring system and time series database, developed by SoundCloud
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PROS OF PROMETHEUS
  • 46
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 22
    Active and responsive community
  • 21
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring
CONS OF PROMETHEUS
  • 11
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 3
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    Requires multiple applications and tools
  • 2
    TLS is quite difficult to understand

related Prometheus 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 · | 12 upvotes · 82.9K 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
Sentry logo

Sentry

10.3K
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See performance issues, fix errors faster, and optimize code health.
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PROS OF SENTRY
  • 234
    Consolidates similar errors and makes resolution easy
  • 120
    Email Notifications
  • 107
    Open source
  • 83
    Slack integration
  • 70
    Github integration
  • 48
    Easy
  • 43
    User-friendly interface
  • 28
    The most important tool we use in production
  • 18
    Hipchat integration
  • 17
    Heroku Integration
  • 15
    Good documentation
  • 13
    Free tier
  • 9
    Self-hosted
  • 8
    Easy setup
  • 7
    Realiable
  • 6
    Provides context, and great stack trace
  • 4
    Feedback form on error pages
  • 4
    Love it baby
  • 3
    Easy Integration
  • 3
    Gitlab integration
  • 3
    Super user friendly
  • 3
    Filter by custom tags
  • 3
    Captures local variables at each frame in backtraces
CONS OF SENTRY
  • 10
    Confusing UI

related Sentry posts

Johnny Bell

For my portfolio websites and my personal OpenSource projects I had started exclusively using React and JavaScript so I needed a way to track any errors that we're happening for my users that I didn't uncover during my personal UAT.

I had narrowed it down to two tools LogRocket and Sentry (I also tried Bugsnag but it did not make the final two). Before I get into this I want to say that both of these tools are amazing and whichever you choose will suit your needs well.

I firstly decided to go with LogRocket the fact that they had a recorded screen capture of what the user was doing when the bug happened was amazing... I could go back and rewatch what the user did to replicate that error, this was fantastic. It was also very easy to setup and get going. They had options for React and Redux.js so you can track all your Redux.js actions. I had a fairly large Redux.js store, this was ended up being a issue, it killed the processing power on my machine, Chrome ended up using 2-4gb of ram, so I quickly disabled the Redux.js option.

After using LogRocket for a month or so I decided to switch to Sentry. I noticed that Sentry was openSorce and everyone was talking about Sentry so I thought I may as well give it a test drive. Setting it up was so easy, I had everything up and running within seconds. It also gives you the option to wrap an errorBoundry in React so get more specific errors. The simplicity of Sentry was a breath of fresh air, it allowed me find the bug that was shown to the user and fix that very simply. The UI for Sentry is beautiful and just really clean to look at, and their emails are also just perfect.

I have decided to stick with Sentry for the long run, I tested pretty much all the JS error loggers and I find Sentry the best.

See more
Grafana logo

Grafana

10.9K
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Open source Graphite & InfluxDB Dashboard and Graph Editor
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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 · | 12 upvotes · 82.9K 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
    Kibana logo

    Kibana

    14.9K
    11.5K
    255
    Visualize your Elasticsearch data and navigate the Elastic Stack
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    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 · 468.7K 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