Looker is a BI application with an analytics-oriented application server that sits on top of relational data stores. It includes an end-user interface for exploring data, a reusable development paradigm for data discovery, and an API for supporting data in other systems.
Looker's user-friendly interface and pre-built visualizations resonated with us. While other tools offered similar features, Looker felt smoother and more intuitive, especially for non-technical users. This was crucial for our goal of empowering widespread data exploration …
Looker is a great fit for our company because we have collaborative analytics workflows and complicated data ecosystems and because of its strengths in data modeling, integration, and collaboration. Brand name and peer recommendations also helps us to select Looker against …
Our organization is going all in on Google products so switching out of the Microsoft suite of tools is a no brainer. All the Google tools work incredibly well together and once you transition it’s incredibly hard to be half in half out between Google and Microsoft. If you’re a …
Looker is more mailable; it allows more dynamic visualisations, filters, drilldowns etc. It's sharing capabilities are also more streamlined than AWS Quicksight. However in contrast to this AWS Quicksight can leverage AWS IAM roles and policies which can be quite scalable. …
Looker is free, so it's certainly better bang for your buck. It's a good platform for someone who just needs a quick way to look at the data they have. It doesn't have some of the advanced functionality that Tableau has, but it integrates well with the Google ecosystem, so it's …
In terms of reporting specifically Looker Studio allows to integrate way more sources into a single report. If needed sources can be blended and parameters can be created with calculations etc
We haven't had a proper benchmarking done. We might consider looking at other options, but this thing is deeply integrated into our platform, so not anytime soon.
I choose Looker when I need quick charts. It is easier to start and configure, browser-based, and easy to connect with Google Sheets. This gives it a good competitive advantage when comparing pricing—other similar tools have expensive licenses. In a corporate Google …
Looker and Tableau are similar products with benefits and drawbacks according to which software you choose to employ. Looker is a Google product whereas Tableau is a Salesforce product. Depending on your existing tech stack it is recommended to leverage the integrated, native …
Looker is web based app and much easier to use.¨ON the othe hand Power BI offers robust integration with Microsoft products (like Azure, SQL Server, and Excel) and a wide range of other data sources. Power BI's ability to seamlessly connect and import data from these sources is …
We use both Looker and Tableau. It depends on the specific team. However, there is a clear correlation that we use Tableau more often when there are more data sources, including financial data.
Looker is more advanced than any other tools which our organization used so far for analytical & translational databases. Looker database points out more detailed information & can be managed & automated by adding some codes & queries. it is much way better to manage all cloud …
Looker works and exists in the Google ecosystem. If you are a Google Cloud Platform user, Looker is a no brainer. There is also Looker Studio, the small (free) brother with less features which is basically a report only viewer/creator. The reason we choose Looker is because can …
Compared to Tableau I much prefer Looker. While they both have similar features (Tableau actually has more ability to drill down and edit data from various sources), for a mildly technical user, they're able to set up a report rather quickly and customize it to look a certain …
The dev/production environment distinction is constructive. Before, in Data Studio, all changes had to be made, which was very inefficient and risky as far as version control goes. Having a more centralized and organized space for all our data visualization is more helpful than …
Senior Manager, Digital Advertising & E-Commerce Team
Chose Looker
Google Looker Studio is an online tool for converting data into customizable, informative reports and dashboards. It is a free tool that turns performance data into informative, easy-to-read, easy-to-share, and fully customizable dashboards & reports. Google Looker Studio turns …
Actually, I use both power BI and Looker. Power BI has more data connectors and power query, one of the most important things that Looker does not have. You can customize the visuals in power bi in a higher level than Looker as well. Besides, Looker is much more advanced when …
Verified User
Analyst
Chose Looker
There are some specific use cases where other tools are not useful in our organization. Some software solutions are shared with other organizations inside the company and Looker is the only tool where we can collaborate good enough.
Looker is an off-the-shelf, free tool for Google business users. Other than the internal cost of time to build, we had no costs to set up what we needed to do. Knowledge sharing internally and using templates greatly reduced this cost, making the overall cost very low.
Legitimately, every other business solution is superior to Microsoft's product offerings. They are a business that doesn't listen to their customers because they know that they don't have to and that someone will always be a client. That being said, Looker does care about your …
Quick dashboards from Google Sheets - Easier to do the graphs than in Google Sheets - Operational dashboards to be used in the day-to-day work - It is good both for retrospective data and to do a pulse check of the current status - Better for not giant amounts of data and not multiple data sources. - If you need a lot of graphs to be displayed on the same page, it can be a bit glitchy during configuration (then the use works fine).
Filtering - you can filter across different dimensions and metrics to get a more specific "cut" of data
Refreshing - data automatically ingests into Looker which allows reports to be updated and backfilled in real time
Conditional Reporting - you can leverage Looker's reporting features to flag when a given metric or KPI falls below or above a specified threshold. For example, if you had a daily sales benchmark in a SAAS organization, you could use Looker to flag whenever daily sales falls above or below the benchmark
Looker is less graphical or pictorial which makes it less attractive
Consumes a lot of memory when there are multiple rows and columns, impacts performance too
At times when we download huge chunks of raw data from Looker dashbords, the time taken to prepare the file is enormous - The user fails to understand if Looker has frozen or if the data is getting prepared in the background for downloading. In turn, user ends up triggering multiple downloads
We've been very happy with Looker so far, and all teams in the organization are starting to see its value, and use it on a frequent basis. It has quickly become our accessible "source of truth" for all data/metrics.
Looker is relatively easy to use, even as it is set up. The customers for the front-end only have issues with the initial setup for looker ml creations. Other "looks" are relatively easy to set up, depending on the ETL and the data which is coming into Looker on a regular basis.
Never had to work with support for issues. Any questions we had, they would respond promptly and clearly. The one-time setup was easy, by reading documentation. If the feature is not supported, they will add a feature request. In this case, LDAP support was requested over OKTA. They are looking into it.
Looker is an off-the-shelf, free tool for Google business users. Other than the internal cost of time to build, we had no costs to set up what we needed to do. Knowledge sharing internally and using templates greatly reduced this cost, making the overall cost very low.
Allowing others to self-serve their own analytics and connect it to Looker simply and easily has helped unblock the central data team so they can instead focus on validated dashboards whilst stakeholders manage their day-to-day analysis themselves. Countless engineering hours have been freed up by not having to manage every user permission for each BI tool; we have a BYOBI approach; Bring Your Own BI
Creation and management of a semantic layer (LookML =Looker Modeling Language ) allows peoples sandboxes and production databases to become clutter free. Minor adjustments, conditional fields, and even some modelling can all be done in LookML which doesn't need oversight or governance from the central data team.
LookML, specifying drilldown fields and their sub-queries, as well as generally creating dynamic parameters with Liquid are all great features, but can have a steep learning curve. it may take some time to understand how to create this middle layer correctly, or even pose a risk of inheriting complex code from another source which can be unmaintainable if it becomes too big. Some level of governance is recommended if Looker is used by a large number of editors.