Databricks Data Intelligence Platform vs. Looker Studio

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Incentivized

Looker Studio is well-suited for those wanting to analyze web/site data and performance quickly. It is simple enough to learn/use for quick report-building or drilling into data. Looker Studio is easier to use/understand than the GA4 console and thus has a better UI/UX. It is an efficient tool for fast, simple data needs—especially for team members with limited analytical capabilities and knowledge.

Incentivized

• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

Incentivized

• Self-service
• Easy to use, point and click
• Little to no training required
• Easy to share internally and externally
• Rich visualizations
• Canned reports
• Easy to copy/paste/dupe existing reports
• Ability to join data sets
• Easy integration with various data sources
• Flexible data integrations, including lowest common denominator (CSV, XLS, G-Sheets)
• Wide range of APIs
• Secure / authentication via Google SSO
• Easy to share / re-assign ownership of reports and data sources

Incentivized

• Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
• Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
• Visualization in MLFLOW experiment can be enhanced

Incentivized

• Few functionalities are very exclusive only for data studio.
• It's time taking to load data and at the same time only single Data source can be connected.
• When editing the reports you have to switch between Edit and View mode to see how does the change looks like.

It is the simplest and least expensive way for us to automate our reporting at this time. I like the ability to customize literally everything about each report, and the ability to send out reports automatically in emails. The only issue we have been having recently is a technical glitch in the automatic email report. Sadly, there is almost no support for this tool from Google, but is also free, so that is important to take into consideration

Incentivized

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

It is not ideal and requires time and dedication to understand how to work with it. Also, it has a lot of limitations around data it can accept. But in most cases, this tool is sufficient for everyday tasks of product and marketing departments. I wouldn't say that the interface is very user-friendly, but for people who regularly work with analytical tools, it must be ok.

Incentivized

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

I give it a lower support rating because it seems like our Dev team hasn't gotten the support they need to set up our database to connect. Seems like we hit a roadblock and the project got put on pause for dev. That sucks for me because it is harder to get the dev team to focus on it if they don't get the help they need to set it up.

Incentivized

The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

Incentivized

The free version of Looker Studio is still better than the leading enterprise-embedded BI tools, despite its weaknesses. The leading embedded BI platforms have terrible visualizations that can be spotted a mile away. They are also primarily locked to a grid, making it very hard to fully customize. The price point is also a major deterrent, since users end up paying for lots of features they might never use. Looker Studio has weaknesses on the blending and modeling side, but we've been able to get by via connection to GBQ and transformation done in dbt.

Incentivized

• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

Incentivized

• Free, so the only investment is time
• Because it doesn't have native support of non-Google sources, it can cost more money than Tableau
• The time spent formatting the templates or building connectors can have a negative impact on ROI
• As a agency, charging for the reporting service is profitable after the first month or two after building the dashboard.

Incentivized