Looker Reviews

I have been using Looker to help the data team create some models. I have also created views for reports and dashboards.

We can centralize all our data models. I can bring data from different products and use it. It's easy because I can create views using SQL. I can make the reports available to different teams and create dashboards. I like that we can centralize different databases.

The response time can be a little bit better. I know that it depends on my data ingestion and the kind of database I am using. The integration with different databases must be improved.

I have been using the solution for the last three years. I am using the latest version of the solution.

I rate the tool’s stability a nine out of ten. We did not have any problems.

I rate the tool’s scalability an eight out of ten. I'm constantly working and learning with it. It's a powerful tool. Around 300 people from the product and business intelligence teams use the solution in our organization.

I never had to contact the support team, but I know that they are available to us. It was never necessary in the last three years.

The initial setup was pretty hard. Now, I know how to do it. When I face some issues, I can work around them. Now, it is pretty easy for me. The solution has good documentation. We can use the documentation to solve the different issues we face. We use CI/CD. We open a branch there and collect reviews. Normally, the deployment takes one day because we have to wait for the reviews to ensure everything is fine.

I have a good experience with the solution. I tell my friends from other companies to migrate to Looker. People who want to use the solution must get a demo and play around with it. It is very intuitive. We must have some technical skills and knowledge of the data and architecture. When we bring data from different teams, we must know how they are using it. Overall, I rate the product a nine out of ten.

I use Looker to deal mostly with simple visuals because it doesn't have much capability to clean data. I use Looker to manage simple visualization once I finish cleaning the data.

With Looker, I have experienced benefits in terms of usability and shareability.

The most valuable feature of the solution is that it is a user-friendly tool that is easy to integrate. I can easily share Looker with somebody who has a Google email address. Those who don't have a Google email address can easily see what I have been working on since I can share it easily with them.

The visualization capability of the product is limited. From an improvement perspective, the product should have more visualization capability. I can't clean data in Looker, and if I try to do it, then it is a really big process, which contributes to the tool's limiting factors related to the area of pre-processing of data. The tool should offer more visualization capabilities and processing abilities.

I have been using Looker for a year.

It is a stable solution most of the time.

I have not used the solution's scalability part.

I have experience with Tableau and Power BI. With Looker, it is easy to sign up, and its onboarding process is straightforward. There is no need for me to download any app for Looker if I use it online, but for the other tools, most of the time, I have to download the app if I want to access more features or capabilities, and at times, I may have no idea about it. The spread of features and how much Looker offers is much less compared to what the other BI tools offer, which I feel are more advanced.

The product's initial setup phase was straightforward.

The product requires a PnP deployment. I already had a Google account, so I had to just sign up with my Google email address, and I was able to access the features of Looker.

I do not have to make any payments to use the solution. In the beginning, Looker may work fine for its users. If advanced users who have experience with BI tools use Looker, then they may find it to be a product with a few limitations.

I am a student who uses Looker for certain projects, but I use another solution at work. In short, I choose Looker for my personal use.

I do not need to do anything to keep the product up and running.

I rate the overall tool a six out of ten.

We cannot disclose our use cases.

I did not, yet. Our organization first needs to be transformed.

Looker matched the requirements. Very professional sales tiger team.

For any tailoring, we would contact our sales reps.

I have used Looker within the last 12-36 months.

Again, the stability of Looker depends on what components are being used. 

The Looker solution itself did perform as expected in the test environment.

Looker can be scalable, but it depends on what components are being used. If the components are scalable and performant, then the solution can be highly scalable.

I have had a good experience with technical support. They are very good.

Positive

Our current (still in use) solution did not meet basic requirements.

We cannot disclose the cloud provider. The cloud provider offered extensive, professional support for everyone willing to deploy the solution.

Both options are available. The vendor team is the best option, though.

Multi-million

With respect to buy solutions, cost is relevant. 

However, the Looker solution would have outmatched the ongoing multi-million budget spending with respect to cost-efficiency.

Yes. We cannot disclose any competitors.

Looker depends on the database components.

For example, if you have a underperforming database, then you likely will have a poor performance experience.

We're able to surface data very quickly and easily. Once Looker is connected to your database, it's very easy to produce reports, dashboards, charts, etc., that expose the data. The tool makes it easy for non-technical people to explore data in ways that would not have been possible before without significant SQL experience. Now, literally anybody can do it.

It's now very easy for non-technical users to "self-serve" and produce the information and gain the insight they need. For example, account managers often deal with customers who have unique needs. These AMs are able to quickly produce individualized "Looks" or dashboards, save them, and send them to internal users along with customers. Of course, we can produce a variety of "standard" reports and dashboards, but giving people close to the business access to what they need without involving data engineers or analysts is a huge time-saver and convenience.

Some of the visualizations are a bit limited on how much you can customize them. That said, Looker pushes frequent releases, and they typically contain improvements to visualization options.

We've been using Looker for a little more than two years.

We've never (that I recall) had any significant Looker stability issues. It's always up when we need it.

We're not a huge organization, but we've not had any trouble here. We have around 300 users, and around 100 unique users per day (peak), and that's never been a problem.

Technical support is one of my favorite things about Looker. They are always very helpful when we have technical questions. Typically, I hate "chat support", but it's really good with Looker. The people who help are always very knowledgeable and if they don't know an answer right away, they are able to find it quickly.

We've previously used at least two other solutions. Looker was better for several reasons. Number one, it makes it easy for 'non-techie' people to do "techie" things. LookML is powerful and intuitive. Looks and dashboards are easy to complete on the fly even if you don't use LookML. Number two, we really like the API stuff and the ability to use "Looker data" in other apps lends itself to solving the "one-source-of-the-truth" puzzle BI folks often face. Number three, their support is fantastic. It was pretty useless with others, and is really great with Looker.

I can't speak to the entire initial setup, but once Looker is connected to the DW, it's extremely easy to get Looks and dashboards built. And, from what I understand, connecting it to the DW is very straightforward – but another engineer handles that.

Depending on what you want to do with Looker, you might see some price barriers. For example, embedding "reporting" in a customer portal might sound great and, depending on the customer, $50 per month might be very reasonable. But, if you have numerous "small" customers, that price point does not scale well.

Before choosing this product, I evaluated Domo, Pentaho, Jaspersoft, Birst, Tableau, and a few others too.

It's easy to build too much too fast. What I mean by that is that it's really easy to "build stuff" in Looker. If I had it to do over again, I might take a more measured approach to determine exactly what I build, and document it better. Looker opens up the data world to a lot of new people, and it's so easy to spin up a quick dashboard, it's easy to forget what you've done (or somebody else did) six months down the road. Also, make sure you truly understand all your data. Once you open it up, people will use it. Sometimes, users make assumptions about the data that might not be true.

We LOVE Looker here at Snagajob. No single solution checks all the boxes on any BI team's wish-list, but Looker comes very close. The product itself is great, and meets nearly all our needs. The support is fantastic, and the user community is very collaborative and eager to give advice within the Discourse forum (the Looker user forum).

I like many parts of Looker. The Developer Experience is top notch. To me, Looker leads the way in the modern BI trend. If you know SQL, you can be a data modeler with very little training. This allows SMEs to drive analytics instead of engineers. Additionally, I can develop on production data sets and not impact production workflows.

I also love LookML. LookML is Looker's data modeling language, which is a derivative of YAML and easily translates to SQL. SMEs can easily model business logic for all other users to share. Also, LookML modeling and its extension abilities allow Knewton to use one code base for multi-data center data warehousing. Example: We have the same data model in Europe and the US that are separate for legal reasons and we can share the same data model code base. Build it once, deploy to production; realize the value everywhere.

The data sharing experience is also quick and useful. I can generate a saved query (a.k.a. a Look) and share it with a user in seconds.

I have multiple product managers and an implementation specialist who build whole dashboards with filtering widgets and multiple visualizations. These are non-technical people implementing new dashboards with new fields or calculations on billions of records, and I don't have to have any data engineering resources involved.

I want to have temporary derived tables so that users can prepare data sets before exploring them and committing code. I am looking forward to the upcoming improvements in workspace security. This will allow me to remove a lot of visual noise from my c-level customers, by reducing what they see in the UI to just what matters to them.

I have used it for three years.

There have a been a few minor bugs, including a security bug that did not affect us. I have seen a little bit of caching issues with the data-driven drop-down filters. E.g., If you have a State/Province filter field and you add a new state to a lookup table. There is no recourse to repopulating the cached data sans waiting for the cache to expire or rebooting the service.

Until recently, it was not possible to alias a model, so in the past, we were stuck with poor naming convention choices. Looker has been very responsive to customer feedback.

Technical support is the best ever. Seriously, this is why we bought the service. The in-app technical support for the LookML developers has allowed me to scale to self-service exploration with a handful of data warehouse/data engineering staff. We have > 40 LookML developers and 200 users of the service.

At Knewton, we had our own custom-rolled solution with D3 before. It was great, but not agile. A REST API for data access and a framework for visualization. An engineer was always required for new dashboards. We wanted to empower self-service analytics.

We had our first dashboard and the majority of the data model that people use today up in < 4 hours from the time Looker spun up a dedicated server for us. We already adhered to industry-standard data warehousing practices and Looker leveraged our schema effortlessly, by generating data models and joins from fact tables to dimension tables with remarkable quality.

Not everyone would have this experience, but if your column names are consistent between tables for things like users and event identifiers, you will be quite pleased with the results.

What about the implementation team?

We implemented it in-house. Make your data models contain only a few explores. Use more data models with fewer explorable tables. Organize around business principles, not tables. Example: For one data mart we have, there are 100 tables but only three Models: People, Content, Events. Also, I recommend grouping explore definitions and view definitions in special views that are exposed in your models. This allows for the same explore in multiple models. Message me on twitter at @rexgibson for examples.

It's definitely worth it for us, but I can't get into details. We want as many people as possible at Knewton using Looker because we believe that decisions should be supported with data.

Start small. Don't try to build a whole complex warehouse. Pick an important and simple business problem and solve that first. Get clear on how LookML works in your company before building a lot of explorable tables.

For my company, the ability to embed professional dashboards directly into our multi-tenant SaaS was our top priority. "Powered by Looker" was the only product on the market that gave us this capability with row level access filtering out of the box.

In the past, adding a new dashboard to our product was a multi-week development effort. Now our nontechnical product management and client success teams can create, launch, and maintain high quality dashboards with no interruption to the development team. The time from inception to delivery is the same day. The results are much more clean, interactive, and professional.

User and group management in conjunction with dashboard organization used to be our only struggle, but Looker recently addressed this with a major upgrade.

We have used Looker in our product for 9 months.

We have had no stability issues whatsoever. We use the hosted version of Looker and have had no unexpected downtime or service interruptions.

We have not had any scale issues yet. Our Looker instance is backed by Amazon Redshift, which had performed amazingly for us. Any slowness we encountered was due to our own data warehouse configuration, which we have resolved with improved indexes.

Technical support has been superb. Most answers are easily found in the user forums. Online chat has been very responsive for other questions. Our account representative has been very responsive and helpful for higher level questions related to issues like billing and configuration.

We used to have an on-premise hosted solution. We switched because the embed functionality was lacking, user-based access filtering was painful, and the visualizations were not very clean or interactive. Looker addressed all of those issues for us.

Integrating with Looker was the most seamless integration we have implemented to date. We had the initial schema live in a matter of hours, and embedded the dashboards into our web application in days. We are a technical software development organization. Our data warehouse and ETLs were already functioning, so the results could vary depending on the team's readiness.

Make sure you shop around and understand the real costs of the competition. Looker does not host your data, so be sure to include the cost of any data warehouses you may use and any ETLs that need to be developed.

Before licensing Looker, we evaluated a lot of great products, including: Domo, Tableau, and TIBCO.

When implementing the product, make sure you already understand your data and your use cases very well. Involve a data architect from the start; and definitely take advantage of Looker's amazing customer success team.

Looker is very flexible for BI. Business users can create graphs, reports, etc. using Looker's Explore functionality.

Looker offers incredible business visualization and analytical tools: graphs, charts, tables, and single numbers. Each analysis can be saved in a ‘Look’ and added to a dashboard for reporting purposes. We use these dashboards for KPI reports which are emailed on a daily, weekly, or monthly basis. These are essential for users to monitor their performance, indicate progress, and catch issues.

For instance, if I had data on users who ran a marathon, their times for each mile, and final time, I could create a table that organizes the runner by completion time. I could also visualize in a time series chart how each runner progressed through the race. I could have reports emailed whenever the most recent race occurred so that I could see the updated metrics that mattered to me.

Looker Transforms SQL with it’s LookML syntax. Looker provides end-users with visualization tools, Saved as individual “Looks” or grouped into a dashboard, so that it is easy to gain insights about their data. The UI is designed so that these users can easily choose what parameters are relevant to their queries, without knowing SQL to perform analysis. Admin’s can spend more time with advanced queries or thoughtful data visualization because Looker simplifies SQL logic for most queries.

Features related to visualization should continue to be improved. While these are strong as they are now, there's always a unique way to visualize data to improve your understanding about an organization.

For our use case (business intelligence, data visualization), Looker is fantastic. I suppose that being the best within a focused, valuable segment of the market is the most important thing, but for those who demand being the best at all sorts of data analysis, Looker could continue to expand its capabilities. For instance, they could offer 3D charts for even deeper insights. However, this might be outside the scope of expectations for this product.

I think that their features are impressive as is, and their product team continues to excel at creating features for users to extract interesting and actionable information from their data.

I have been using Looker one year and 9 months.

We did not encounter any issues with stability.

So far we did not encounter any issues with scalability. I'd imagine that issues with company data architecture and unthoughtful organization of Looker could hamper its effectiveness.

They're on top of their game. They are knowledgeable about their product and proactive in resolving issues.

This was our first BI product.

Initial setup was fairly straightforward. Granting Looker write privileges to a replica database improves the ability to utilize advanced queries with reduced wait time via 'persisted derived tables'.

Review how you intend to use Looker. Is it purely internal? Will you have external clients viewing Looker reports? For both questions, how many people will need to have Looker access, and which reports are relevant to which people in the organization?

We did not evaluate other options before choosing this product.

You should know what it's good for. It's great for enabling business insights via its data visualization tools. Scheduled reports are fantastic for keeping teams updated on progress towards accomplishing goals or identifying issues before they become critical. Having someone who is experienced with SQL and intuition in UX write the LookML, so that it is easy for the familiar business user to explore the data to answer their questions. Looker's discourse offers deeper analysis opportunities that can guide analysts in better utilizing LookML as well.

The most valuable features are the ability to create visualizations for non-technical employees, as well as the ability to provide ways to dynamically slice the data. This is especially useful for keeping track of potential issues with our data warehouse, to which the product is connected, and for providing controlled access to potentially sensitive information.

Our BI team, and otherwise less-technical employees, are able to design and schedule their own reports without additional knowledge. They are now able to create a number of more-complicated visualizations, such as churn diagrams and acquisition funnels, which are used to track milestones and immediately spot anomalous behavior.

This product is useful for non-technical users, who are able to use the interface to easily slice data. But for the admin users who are responsible for maintaining the schemas and files behind the scenes, it can easily become a chore, as there is a lot of manual work involved in defining the schemas of the connected database(s), as well as any refactored tables; the restrictive syntax also makes maintenance difficult. A personal feature request I have is the ability to create reports in such a way that the end users can inject values into the query and dynamically change the results in this way.

I have been using it for approximately a year.

Stability is generally not an issue. We do sometimes have trouble with larger data sets, but Looker does not seem to be intended for such purposes. In fact, certain restrictions are placed automatically to limit the number of records available for viewing at once. One major issue we have discovered is that the computations required for more complicated metrics can often overload Looker, forcing us to create additional refactored tables in the data warehouse in order to reduce the load on Looker. This is particularly restrictive when users want to create reports from raw data.

Customer support has generally been helpful; they tend to be helpful, provide suggestions and be open to feature requests.

Initial setup was relatively simple. The main difficulty lay in picking up the syntax of the LookML files that determine what is shown in the UI, but the Looker team was always available to provide training over the phone.

An in-house team implemented it. The only setup required was to feed the connection details through Looker, and to later integrate with GitHub.

The alternative when decided to use Looker was Tableu, which was recommended by several employees who had used it in the past. The main reasons why we opted for Looker over Tableu were the existence of a web interface, simplicity of use, and the general look of the product.

While Looker is great for simple visualizations and is a great start in terms of providing a framework for dynamically generating graphics, don't expect it to do any heavy lifting. It struggles with large data sets, and the syntax will often require you to feed refactored data into Looker.