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AtScale vs Looker: What are the differences?
AtScale vs. Looker: Key Differences
Data Source Connectivity: AtScale provides the ability to connect to a wide variety of data sources, including Hadoop, cloud storage, and traditional databases, enabling users to work with diverse data sets. On the other hand, Looker is more focused on connecting to relational databases and cloud data warehouses, providing seamless integration with these sources but offering limited connectivity options compared to AtScale.
Data Modeling Approach: AtScale uses a multi-dimensional OLAP (Online Analytical Processing) approach that empowers users to create complex data models and define hierarchies for in-depth analysis. In contrast, Looker employs a modeling layer that simplifies data exploration by creating a semantic layer on top of the raw data, allowing users to easily navigate and analyze information without the need for extensive modeling expertise.
Scalability and Performance: AtScale is designed to handle large-scale data sets and complex analytical queries efficiently, making it suitable for enterprise-level applications with high performance requirements. Looker, while capable of handling sizable data volumes, may face scalability challenges when dealing with extremely large datasets or complex analytical workloads due to its architecture and design limitations.
Customization and Extensibility: AtScale offers extensive customization options through the use of MDX (MultiDimensional eXpressions) and SQL for creating custom calculations and measures, giving users more flexibility in tailoring their analytics solutions. Looker, on the other hand, provides a robust platform for building custom data models and visualizations using LookML (Looker Modeling Language), allowing users to extend and enhance the functionality of the tool according to their specific requirements.
User Interface and Visualization Capabilities: Looker emphasizes user-friendly interfaces and intuitive visualization tools that enable business users to explore data and generate insights without extensive technical knowledge. While AtScale also offers visualization capabilities, its focus is more on empowering data analysts and IT professionals to work with complex data structures and perform advanced analytics, which may require a deeper level of technical expertise compared to Looker's user-friendly approach.
In Summary, AtScale and Looker differ in their data source connectivity, data modeling approaches, scalability and performance capabilities, customization options, and user interface designs, catering to distinct user needs and preferences in the realm of data analytics and business intelligence.
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.
Pros of AtScale
Pros of Looker
- Real time in app customer chat support4
- GitHub integration4
- Reduces the barrier of entry to utilizing data1
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Cons of AtScale
Cons of Looker
- Price3
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What is AtScale?
What is Looker?
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