Trevor.io Image source: Īn all-in-one self-service data analytics tool that empowers non-tech users to explore data and find answers to their business questions. So who are Looker’s competitors? Our top 5 Looker alternatives provide data visualisation and exploration for business intelligence but also offer lower price points, less of a learning curve, and more accessibility for your non-tech team. Larger or enterprise-scale organisations with a sizable data budget and a data team who can manage their BI tool. Typically, it’s estimated at around $3,000-$5,000 for 10 users, though, which makes it an expensive tool. Quotes vary according to the number of users and data you’ll use – Looker doesn’t publicly release their pricing information.
Sophisticated data modelling capacities for enterprise-scale deployment.Flexible customisation options, with a centralised DSL (domain-specific language) modelling layer designed to tailor data analytics to your needs.Attractive data visualisations including interactive dashboards that give your team an overview but also let them go deeper on metrics.Here’s our pick for the 5 best Looker alternatives that will superpower your team:
Metabase vs looker for free#
Want to see if Trevor.io would work for your team? TRY IT FOR FREE FOR 2 weeks! 5 Best Looker alternatives Finding the right Looker alternative can help you to truly empower your business users by putting sophisticated data at their fingertips. This article weighs up the options by comparing five alternatives to Looker.
It offers business intelligence to non-technical users through highly attractive data visualisations and customisable data analytics that let your team dive deep into the data.īut it’s not the right choice for every company – it’s really meant for larger-scale deployment and it uses its own proprietary language, which means your team will still rely on engineers or data people for setup and troubleshooting. Looker is one popular choice for companies looking to make data accessible across their entire workforce. As HBR say, “activating your analytics should be a cross-team effort”. They know that every single team member must be able to quickly, easily, and intuitively engage with the data they need for better decision-making and KPIs. Truly data-driven companies don’t just rely on their engineers or data analysts. Perhaps this isn't attributable to Metabase - but I also find the error messages when I run SQL through it to be utterly incomprehensible and they do not give me any real indication of what I've done wrong.According to the Harvard Business Review, getting a return from data analytics is all about “activation-readiness”. It can also be extremely slow when downloading data from Metabase into a CSV file (sometimes then failing after ages of waiting), and even re-ordering columns within Metabase. This can be so frustrating, because I occasionally need some data urgently and this really impacts my productivity. It can be so slow sometimes - quite often I have to use it late at night when fewer colleagues are also accessing it and it seems to go much faster then. It's really good and makes using SQL easy. Finally, I like that it comes up with suggestions when I'm writing SQL about data columns and tables. I also use Metabase to run SQL scripts created by colleague's as part of our employee payment process - it works really well with google sheets for this.
Metabase vs looker download#
I also like that I can then download it as a CSV with ease. It's integration with SQL is just fantastic, it makes it really easy for me to run a script to find virtually any data I need from our company's performance. Metabase is an amazing tool for storing vast quantities of data. I use Metabase to run SQL scripts to find data from our large database - which is itself stored in Metabase. And secondly, for me to collect any data I require from our company database in Metabase - with an SQL script, I can find everything I want and I cannot express with words how useful that is for me every day! Kommentare: It has two main functions for me - converting data entered into Google Sheets by colleagues into a suitable format for us to be able to upload it as payment data via an SQL script.
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