Logi Info (or the Logi Analytics Platform) is a developer-grade analytics platform designed for application teams needing to rapidly build, deploy, and maintain mission-critical applications. Logi serves the embedded model, so companies increase the
likelihood of building valuable, long lasting applications. The vendor focuses on enriching embedded analytics
capabilities so that their customers' applications become more valuable, faster. According to the vendor, Logi allows customers to…
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Looker
Score 8.2 out of 10
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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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Pricing
Logi Info (Logi Analytics Platform)
Looker
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Logi Info (Logi Analytics Platform)
Looker
Free Trial
Yes
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
No setup fee
Required
Additional Details
Logi's pricing was developed with software vendors in mind and as such, we offer flexible, custom pricing aligned with your go-to-market approach and long-term growth plans. Our pricing objective is to ensure our partners can rapidly scale their analytics.
Logi Composer is the best of the product range that Logi Analytics offers; Logi Info is, in my mind, a terrible experience and should be discontinued. Logi Info is not a BI tool and many CEOs and product managers believe it is. Instead, it is a tool for web developers to create IIS apps with a library of BI-type components, and with everything else having to be hand-coded in JavaScript and CSS. Maybe this was cool back in the early 2000s when Logi Info came about, but today I'd recommend using Visual Studio, C#, .NET, and finding a NuGet package for your visualisations if you wanted to go the route of making your own web app. This said, where it is suited is if you have a burning desire to make a stand-alone IIS-based web application and you don't need or want to leverage any existing skills in .NET or PHP or other frameworks/languages. Instead, you want to use the Logi Info XML-based BI widgets and you're happy to make something quick without needing it to look really awesome.
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).
Clear/Concise view of data. Easily intuitive, amazing drill down capability, fully user configurable panels and dashboards.
Self-Service is such a powerful tool for facilities that want to create/design their own reports/dashboards, it's instantly recognized as being very powerful and agile.
Logi software makes the designer realize that they should think outside the box and not stick with "it's how we have always done it limitation". It allows you to be very creative.
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
It is not always intuitive to incorporate JavaScript into Logi Info. The tools are all available, but the process requires multiple resource in translating between Logi and JavaScript.
The toolbox is too vast for Logi to provide a 'quick implementation' or 'quick start guide'. There are so many tools that Logi expects the client to determine which fit their use case. There are purchasable 'Professional Services' options for implementation, but without these, the toolset is almost too big.
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
Logi Analytics is a great solution if you need just the user interface. However there are other tools stronger at the overall data flow which include a user interface as part of their product.
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.
I am giving 9 rating because the Logi Info still needs to improve on the tutorials part and make it easy for the beginners. Otherwise, it's a very good analytics tool which offers more than 20 types of visualization. It's predictive analysis feature and easy to embed with technologies make it stand out in the market.
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.
The support process is bit slow and has a good scope improvement but overall it's good as team is very supportive. They generally take 1-2 days time to respond emails sent to them but some times a delay is also expected. Overall, I did not face any major issues using the service.
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.
Logi gave us the flexibility we needed to meet the configurable nature of our product and for the need to create custom reports. Other products did not allow flexbility to generate reports via script as was one of our primary requirements.
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.
By embedding Logi in our solution and using the Logi Self-Service Module we can provide this flexibility to our users without requiring custom development work for each new request.
We succeeded in developing embedded self-service analytics at scale with a combination of Logi analytics as front-end and a Cassandra data lake with Spark aggregation algorithms as back-end.
We analyze the insurance industry and need to replicate different data formats across hundreds of databases to support multi-tenant (customer) BI reports and "ad hoc" data review on millions or hundreds of millions of records per customer.
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.
