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Snowflake and Looker are both leading business intelligence (BI), data analytics, and data management platforms with a strong following.
These data solutions are in heavy demand as organizations seek to harness the vast troves of data at their disposal. Instead of a small team of data scientists slicing and dicing data, today teams from management, marketing, sales, and IT are utilizing big data in their day-to-day activities.
As both Snowflake and Looker are well regarded data management and analytics platforms, users sometimes must choose between them. There are arguments for and against each data solution.
Which of these well-respected data platforms is best? Both provide the volume, speed, and quality demanded by business intelligence applications. But there are as many similarities as differences between them. They have different approaches to the data sector. Therefore, selection often boils down to data platform preference and suitability for the organization’s big data strategy.
Also see: Best Data Analytics Tools
Snowflake vs. Looker: Key Features
Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data. Offered via the Software-as-a-Service (SaaS) model, it uses an SQL database engine to manage how information is stored in the database. It processes queries against virtual warehouses within the overall warehouse, each one in its own cluster nodes, independent of others and not sharing compute resources.
Sitting on top of that are cloud services for authentication, infrastructure management, queries, and access controls. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.
Overall, Snowflake should be regarded more as a data lake or data warehouse that facilitates analytics than a full-featured analytics application. As such, it is particularly good at managing, processing, aggregating, and sharing large amounts of data across a business. Good archiving features are also present.
Late in 2022, Snowflake released some platform updates. These included performance advancements across its single elastic engine to make it faster while improving economics for users. In addition, Snowflake’s Snowgrid technology enables customers to operate at global scale with enhancements across cross-cloud collaboration, cross-cloud data governance, and cross-cloud business continuity.
Looker, which was acquired by Google two years ago, is web-based and offers plenty of analytics capabilities that businesses can use to explore, discover, visualize, and share analyses and insights. Enterprises can use it to drill down into data.
Looker takes advantage of a specific modeling language to define data relationships while bypassing SQL. Looker leverages its Google ownership by being tightly integrated with a great many Google data sets, including Google Analytics. It earns good marks for reporting granularity and scheduling, including real time analytics and real time reports. Users comment on its ability to build dashboards for users to be used in self-service analytics throughout the enterprise.
Snowflake has robust support for JSON-based functions as well as database maintenance automation. It provides columnar storage and massively parallel processing (MPP) for simultaneous analytics computations and fast querying even on huge datasets. Additionally, it keeps compute, storage, and cloud services separate as well as concurrent scaling. It possesses a wealth of analytics features including mobile data exploration, analytics dashboards, the publishing and embedding of analytics content, data source connectivity, and cloud-based BI.
Overall, Looker wins on broad analytics features. But for those needing stronger data warehousing and data management tools, Snowflake excels.
Snowflake vs. Looker: Support and Ease of Use
The Snowflake data warehouse is said to be user-friendly, with an intuitive SQL interface that makes it relatively simple to get set up and running. It automates data vacuuming, compression, diagnosis, and other features. There is no need to copy data during scale up operations with Snowflake.
On third-party data sharing and accessing it to conduct analysis, Snowflake makes the entire process straightforward. It supports structured and semi-structured. Users also report that its ability to handle many columns is strong. But some say the documentation is weak and that a lack of out-of-the box analytics holds it back. Gartner Peer Reviews give it a good score on ease of deployment and administration. Snowflake provides 24/7 live support.
Looker is said to have a steep learning curve due to the need to use the LookML proprietary programming language. But those familiar with that language say that once learned, it is easy to use. They say it is worth it in terms of being able to define data relationships and bypass SQL. They add that it streamlines the distribution of insights to staff across many business units.
Once the learning curve is mastered, users say how simple it is to use the platform for analytics. Visualizations, though, are relatively basic according to some users. That said, Looker’s drag and drop interface and pre-built templates and data models help analysts repeat similar analyses rapidly rather than starting again from scratch.
In this category, Snowflake wins.
Also see: What is Data Visualization
Snowflake vs. Looker: Security
Snowflake boasts always-on encryption, along with network isolation, secure access-based requests, and other robust security features. These features are tiered, with each higher tier costing more. That means you don’t end up paying for security features you don’t need or want.
As with many hyperscaler-based tools, security is both a big plus and a concern. Google says Looker is secured by its robust public cloud protections. The company takes responsibility for code quality but configuration of secure access between Looker and enterprise databases is up to the user. Users are also responsible for controlling access and permissions for users of Looker instances.
Snowflake wins on security.
Snowflake vs Looker: Integration
Snowflake is on the AWS Marketplace but is not so embedded within the AWS ecosystem and lacks the vendor partnership depth and breadth that Google can muster. Some users say that with certain analytics applications, it can be challenging to integrate Snowflake. But in other analytics use cases, Snowflake is wonderfully integrated. Tableau, Apache Spark, IBM Cognos, and Qlik are all fully integrated. Those using these tools will find analysis easy to accomplish. Gartner Peer Reviews rates Snowflake highly for integration and deployment.
Looker is also able to run Windows, Mac, and Linux. It offers APIs and other means of exporting its data models to external visualization platforms. And of course, it is closely integrated with many Google data sources. Gartner Peer Insights scores it well on integration.
Integration: Looker wins.
Snowflake vs. Looker: Pricing
Looker isn’t cheap. Some sources have its costing as much as $35,000 per year for enterprise deployments, with addition monthly fees due for features such as dashboard views, dashboard creators, and for developers. But those needing these enterprise-class features related to the distribution of users and reporting seem happy to pay. Some rough estimates put Looker at around $60 per month per user. But that will vary significantly from deployment to deployment.
Snowflake costs about $40 a month. But again, rate of usage will vary tremendously depending on the workload. Some users say large data sets cost more on Snowflake due to it offering separate pricing for compute and storage. On-demand pricing is a feature of Snowflake. It also provides concurrency scaling automatically, with all editions at no extra cost. Pricing, though, can be complex with four different editions from basic up – and prices rise as you move up the tiers. You can either pay for capacity upfront or choose the pay-as-you-go model for storage.
Differences between the pricing models make it difficult to do a clear apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. This category is a close comparison as it varies from use case to use case, but Snowflake appears to win by a hair on pricing.
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Snowflake vs. Looker: Conclusion
Snowflake and Looker are excellent tools. Each has its pros and cons. The choice between them comes down to usage patterns, data volumes, workloads, and data strategies. Gartner Peer Reviews scores Snowflake a little ahead of Looker in areas such as overall product capabilities. But the products are different enough that such a rating should not be the deciding factor.
Snowflake is best when data management, integration, and sharing are the biggest needs. Those wanting to centralize data across multiple data repositories and with large amounts of data will find it invaluable. Top-notch analytics can be added on via other platforms.
Looker, on the other hand, is best when the main need is for data analytics. Therefore, those that are already settled on an analytics tool are advised to opt for Snowflake. Whereas those lacking a modern analytics engine should favor Looker. There are certain enterprise user needs for reporting and analytics distribution where Looker excels. And for those heavily leaning on Google platforms, it offers advantages to skilled analysts.
Some say Snowflake is better when you are starting small and gradually scaling up. But these are generalities and each business needs to research how costs will work out for them. For some, Looker’s bundling of compute and storage will make it much cheaper. But the opposite might hold true for other workloads. In those cases, Snowflake’s ability to split compute and storage pricing may be best.
Also see: Top Business Intelligence Software