Need advice about which tool to choose?Ask the StackShare community!
Looker vs Mode: What are the differences?
Introduction: Looker and Mode are both powerful data analysis tools that enable businesses to visualize and analyze their data. Despite having similar goals, there are some key differences between Looker and Mode.
Data Exploration Capabilities: Looker emphasizes on providing an interactive and user-friendly interface for data exploration. It offers features like auto-suggested visualizations, data filters, and drill-down capabilities, enabling users to explore and analyze data easily. On the other hand, Mode focuses on providing a versatile SQL editor, allowing users to perform complex queries, write custom SQL code, and create advanced data transformations and calculations.
Collaboration and Sharing: Looker puts a strong emphasis on collaboration and sharing within teams. It provides built-in tools for creating and sharing saved visualizations, dashboards, and reports with colleagues, enabling seamless collaboration and knowledge sharing. In contrast, Mode provides a collaborative environment through its collaborative SQL editor, where users can collaborate in real-time on SQL queries and share their analyses with others.
Embedding and Customization: Looker offers extensive embedding capabilities, allowing users to embed Looker-developed reports and dashboards directly into third-party websites and applications. It provides SDKs and APIs for customizing the look and feel of embedded content and integrating with other systems. In comparison, while Mode also offers embedding options, it provides less flexibility in terms of customization and integration compared to Looker.
Data Modeling and Transformation: Looker provides a robust data modeling layer, enabling users to transform and structure their data for analysis. It offers a visual interface for building data models, defining relationships and aggregations, and creating business logic. Mode, on the other hand, primarily focuses on SQL-based data analysis, allowing users to directly manipulate and transform data using SQL queries without the need for complex data modeling.
Data Source Connectivity: Looker supports a wide range of data sources, including popular databases, cloud storage services, and data warehouses. It offers connectors and pre-built integrations for seamless data connectivity and syncing. Mode also supports various data sources, but its list of supported data sources is comparatively smaller than Looker's, limiting the options for data connectivity.
Pricing and Licensing: Looker follows a subscription-based pricing model, where pricing is based on factors such as the number of users, data volume, and additional features required. It offers different pricing tiers based on the organization's needs. On the other hand, Mode offers both free and paid plans, with the free plan having limitations on features and functionalities. The paid plans offer additional features and more flexibility in terms of data volume and collaboration.
In summary, Looker emphasizes on data exploration, collaboration, and embedding capabilities, while Mode focuses on SQL-based analysis, data modeling, and customization. Looker offers a wider range of data source connectivity options and follows a subscription-based pricing model, whereas Mode has a more limited set of data sources and offers a free plan with paid options.
We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.
Hello Mohan,
To be honest, I don't have experience working with analytics on apps and also I don't have experience with Looker, so I cannot say I will suggest that one. I know that Amplitude is a known product analytics tool for apps. I know that in the #GoPractice course, Oleg (CEO GoPractice) was using Amplitude in all his experience with mobile game apps, so I guess apps could work well too. I have experience using Amplitude for SaaS solutions and it is great to create all kinds of analytics for the product. Then Google Datastudio is the classic solution to create dashboards and reports connect it with any data source. Also, some people, instead of Amplitude are using the new Google Analytics, @GoogleAnalytics #GA4 or Mixpanel. However, my suggestion is to use Amplitude and if there are reports that you cannot answer with Amplitude, use Google Data Studio.
I hope that could help you.
Cheers,
Looking for an environment to help with exploring behavioral data, and creating dashboards for an account-based marketing approach. As we dug into options, I learned of Looker Actions, which enabled us to send the results of queries to the Segment Track and Identify api's. This enabled me to easily send CRM data to marketing tools integrated via the Segment CDP. At the time, no other BI environment provided a similar capability to automatically activate data, rather than just visualize it.
Very easy-to-use UI. Good way to make data available inside the company for analysis.
Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.
Can be embedded into product to provide reporting functions.
Support team are helpful.
The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.
Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.
And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.
Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.