What is Data Studio and what are its top alternatives?
Top Alternatives to Data Studio
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. ...
Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. ...
You can pull your data together using out-of-the-box, hassle-free connectors for hundreds of data sources, including spreadsheets, files, databases, and web services applications. ...
Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...
It is a cross-platform database tool for data professionals using the Microsoft family of on-premises and cloud data platforms on Windows, MacOS, and Linux. ...
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. ...
It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...
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. ...
Data Studio alternatives & related posts
- Database visualisation51
- Open Source41
- Easy setup38
- Dashboard out of the box32
- Support for many dbs8
- Easy embedding7
- It's good6
- AGPL : wont help with adoption but depends on your goal5
- BI doesn't get easier than that5
- Multiple integrations4
- Easy set up3
- Google analytics integration2
- Harder to setup than similar tools3
related Metabase posts
- Super cheap1
- Only works in AWS environments (not GCP, Azure)1
related Amazon Quicksight posts
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.
related Klipfolio posts
- Easy setup926
- Data visualization886
- Real-time stats696
- Comprehensive feature set403
- Goals tracking180
- Powerful funnel conversion reporting153
- Customizable reports136
- Custom events try83
- Elastic api53
- Updated regulary13
- Interactive Documentation8
- Google play3
- Advanced ecommerce2
- Walkman music video playlist2
- Medium / Channel data split1
- Easy to integrate1
- Financial Management Challenges -2015h1
- Industry Standard1
- Confusing UX/UI7
- Super complex5
- Very hard to build out funnels3
- Poor web performance metrics2
- Time spent on page isn't accurate out of the box1
- Very easy to confuse the user of the analytics1
related Google Analytics posts
This is my stack in Application & Data
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).
Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.
related AzureDataStudio posts
Which tools are preferred if I choose to work on more data side? Which one is good if I decide to work on web development? I'm using DBeaver and am now considering a move to AzureDataStudio to break the monotony while working. I would like to hear your opinion. Which one are you using, and what are the things you are missing in dbeaver or data studio.
- Capable of visualising billions of rows1
related Tableau posts
related Power BI posts
- Real time in app customer chat support4
- GitHub integration4
- Reduces the barrier of entry to utilizing data1
related Looker posts
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.