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.
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Sigma
Score 9.2 out of 10
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Sigma Computing headquartered in San Francisco provides a suite of data services such as code free data modeling, data search and explorating, and related BI and data visualization services.
I prefer Google Sheets, Looker, and Tableau over Sigma Computing. I was not involved in the purchasing decision but if it were up to me, we would use a different tool.
I was not involved in the decision-making process, and we stopped using chartio because it was discontinued. Chartio was great for looking under the hood at individual queries. Sigma is superior in the visualization aspects - it looks more professional and clean-cut. I've …
With Looker, to be effective, a substantial amount of coding & modeling needs to happen in LookML. Being another language to learn, users have to context switch again from at a minimum either SQL or Python into LookML. The concept of being able to source control, code review, …
I'd rate Sigma to be extremely similar to Sisense except it looks not as nice. I would say that as a tool, Sigma is more user-friendly than Tableau, Power BI, Trevor, and Metabase.
I do feel that Looker is far more powerful and looks great, but I also recognize that Looker does …
Sigma has the capabilities of the other BI tools. I think it's pretty user friendly and easy to learn. Many of our stakeholders are used to using Excel so it's nice that it is a smooth onboarding process for them. We haven't looked into much of the visualization capabilities so …
flexibility, works really well with Snowflake, export capability, level of support, the fact that Sigma Computing is a start up and improving so quickly. Web based software
Google Data Studio is free, intuitive and easy to use but the analytics are limited. In-table calculations can't be done easily and it lacks a native connector for Snowflake. Moreover, table columns cannot be arranged in any order--they must be dimension first, and metrics …
Easy entry tool that can grow with you as the data expertise and literacy grows within the org. Start up effort and cost is too high in the tools that are built for large corporate solutions.
Sigma was chosen for three reasons. 1. Excel-like workflow. Many of our researchers have a background in excel, and the excel-like workflow Sigma offers enabled them to get up and running quickly. 2. Embeds. We can easily embed dashboards into our SAS platform 3. Price. Sigma …
Sigma has a good balance of affordability, self-service ability for technical teams, and ease of use. We chose it over the others basically based on ROI.
Sigma Computing was our top choice due to the ease of use of the platform for end users doing self-exploration of data. The structure of the platform for how datasets are created and access provisioned was much better than other products we considered. Sigma Computing may not …
Sigma is my least favorite BI tool I have used. Its unintuitive, takes longer to develop on, and has very limited functionality to re-use work (ie scripting, copying to a new project).
Sigma's combination of key features from each of these with native dB access under a UI that makes collaboration easy has made it an easy choice for us.
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).
If we have very huge data that has to be filtered based on the selection in the workbook filters, then passing the control IDs of the filters as the parameters directly in the SQL query is a great help. This way helps us in optimizing large SQL queries as well. If we want our front end application filters with the Sigma Computing dashboard filters to communicate with each other, we can certainly do that in embedding with the help of the control_ids.
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
Viewer level license is quite limited. These users can't download data or even add filters on datasets. Something to keep in mind.
Directly querying the underlying data warehouse will lead to increased usage. Not a big deal on something like Redshift, but your Snowflake consumption will increase, potentially by a lot.
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.
Sigma has helped us a lot and has become an integral part of our daily workflow. It would be difficult to switch to another platform and have to rebuild the numerous metrics and performance reports that we have already established
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.
It has a clean and modern interface. However, it is not completely intuitive. I think it would be better and easier to navigate with more Windows style drop down menus and/or tabls. There is a significant learning curve, but that may be due in part to the technical nature of this type of software tool.
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.
Support team is helpful in answering questions and providing help with using the UI. There are knowledgeable people within the support team. There are also good online support tools. There are significant community support resources available. There is however lack of a live support. It would be useful to have live phone number or chat to use.
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.
Sigma is by far the best. It is easiest to learn and easiest to use on a day to day basis. I never have to wait for dashboards to load and it's very easy to understand the variables that are going into my visualizations. Best of all I can manipulate the data within Sigma very easily. In these other platforms data manipulation is difficult or must be done in the data warehouse
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.
Monitoring health of cloud platform has allowed the company to anticipate issues before they affect customers – Sigma prompted us building a canary monitoring process that provides customer container health.
Customer success has used an activity report to discover customers running runaway processes that they were unaware of, creating an alert to contact the customer and prevent an embarrassing situation.
Customer success uses the activity report to prompt conversations regarding increases or declines in behavior that led to increasing contract limits or addressing churn concerns.