Codacy
SonarQube
CodeClimate
CodeFactor.io
ESLint
Coveralls
SensioLabs Insight
codebeat
Stagger
IntelliFront BI
Monte Carlo Data
Metaplane
Bigeye
Codacy automates code reviews and monitors code quality on every commit and pull request reporting back the impact of every commit or pull request, issues concerning code style, best practices, security, and many others. It monitors changes in code coverage, code duplication and code complexity. Saving developers time in code reviews thus efficiently tackling technical debt. JavaScript, Java, Ruby, Scala, PHP, Python, CoffeeScript and CSS are currently supported. Codacy is static analysis without the hassle.
Stagger is a Tableau Cloud optimization tool that manages extract refresh schedules and resolves clustering issues causing failures. It provides a centralized heatmap interface for administrators to view all schedules, identify bottlenecks, and detect hidden conflicts.
Instead of analyzing schedules individually, Stagger consolidates them into a single view, revealing clustering patterns across the refresh schedule. The tool follows a three-step workflow: the spot phase identifies overlapping extracts causing peak loads, the spread phase enables bulk rescheduling with impact previews, and the track phase monitors load balance improvements over time.
Stagger integrates seamlessly with Tableau Cloud without altering existing data pipelines or infrastructure, transforming refresh optimization from reactive troubleshooting into a proactive, measurable improvement process.
Codacy StaggerNo Stagger videos yet. You could help us improve this page by suggesting one.
Stagger's answer:
Stagger does one job and does it well: managing Tableau Cloud extract refresh schedules. It's the only tool built specifically to show your entire refresh load as a single 24-hour heatmap, reveal exactly which hours breach the concurrent-refresh limit, and let you batch-reschedule dozens of tasks at once with a live preview before you apply. Where broad governance suites bolt refresh checks onto a large platform, Stagger is a focused, self-serve tool that connects read-only in minutes and tracks your load-balance score improving over time.
Stagger's answer:
Tableau Cloud site administrators and BI/analytics teams running extract-heavy deployments โ organizations large enough that scheduled refreshes start colliding and hitting the concurrent-refresh limit. Typically mid-market to enterprise companies with a dedicated Tableau/BI admin managing dozens to hundreds of refresh tasks. It's industry-agnostic; the value scales with the number of extracts, not the type of business.
Stagger's answer:
Because it's purpose-built and priced for the person who actually feels the pain. Competitors are either the native Tableau scheduler (which only lets you edit one task at a time) or broad, enterprise-priced governance platforms that treat refresh as a footnote. Stagger is one flat plan ($59/mo, no per-seat or per-site metering, no "contact sales"), the free trial shows your own site's clustering on real data before you pay, and it fixes the problem in one batch instead of dozens of manual edits. You adopt it the moment you hit the problem - no procurement cycle.
Stagger's answer:
Stagger was built by a Tableau admin managing a heavily congested Cloud site - hundreds of workbooks, refreshes constantly clustering at the top of the hour, hitting the concurrent-refresh limit, and failing. The native scheduler only let them fix tasks one at a time, so they couldn't even see the full picture, let alone spread the load. After enough late nights rescheduling by hand, they built the tool they wished existed: one view of every refresh, and the ability to batch-fix the clustering. Stagger has run on that same site ever since.
Stagger's answer:
Stagger integrates with Tableau Cloud through a Connected App (JWT / Direct Trust) and the Tableau REST API.
Based on our record, Codacy seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I'm trying to use Codacy to review my code. One of the issues is regarding the use of the "setcookie" function. Source: over 4 years ago
Does anyone have an example on how to get this conversion done on github actions where I can convert the *.coverage file into a *.xml file for uploading to codacy.com. Source: about 5 years ago
Online analysisFinally, if you want a simple way to analyze your code without having to manually configure everything locally, you can use an online code review service such as Codacy (shameless plug here). We already integrate some of the mentioned detection tools in this article and we are working every day to improve the service. The other main benefit of using automated code review tools is to allow you to... - Source: dev.to / over 5 years ago
Because you care and because you always want to be better, automation is a great way to optimize your review workflow process. Go ahead and do a quick search on Google for automated code reviews and see who better fits your workflow. You'll find Codacy on your Google search and we hope you like what we do. - Source: dev.to / over 5 years ago
SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.
IntelliFront BI - IntelliFront BI is a data analytics and business intelligence solution.
CodeClimate - Code Climate provides automated code review for your apps, letting you fix quality and security issues before they hit production. We check every commit, branch and pull request for changes in quality and potential vulnerabilities.
Monte Carlo Data - Monte Carloโs Data Observability platform increases trust in data by eliminating data downtime, so engineers innovate more and fix less.
CodeFactor.io - Automated Code Review for GitHub & BitBucket
Metaplane - Metaplane is the Datadog for Data โ a data observability tool that continuously monitors your data stack, alerts you when something goes wrong, and provides relevant metadata to help you debug.
