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

Visualizing cross-channel campaign performance can blend data from a few different sources to compare performance metrics like spend, clicks, and conversions side-by-side in a single view, which helps in quick budget reallocation decisions. When dealing with massive volumes of data (millions of rows) or highly complex queries, Looker Studio dashboards can become slow, laggy, or even crash. Performance issues are a frequent complaint when working with large datasets, making it unsuitable for enterprise-level companies

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

• Breath of data - the number of ways to interrogate the data is endless, and the options to view metrics alongside each other make for comprehensive datasets.
• Data visualisation and customisation - the options for presenting data and separating out across pages allow for clean visuals and segmented information.
• Easy shareability/usability - a quick and simple tool to introduce colleagues to, and easy to grant access for them to be able to view the data, without having to understand the setup itself.

Incentivized

• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

Incentivized

• It needs better handling of complex logic. We often need workarounds to perform complex custom calculations, and it can be really unpleasant at times.
• Felt it got slow with a larger data set, and in one minor report, we had to set up time filters so that calculations during spikes could be traced more quickly.
• Compare to competition they need to improve with notification things.

Incentivized

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

Looker Studio is easy to use, and it offers a sufficient variety of predefined visualizations to choose from. It's easy for us, and anyone can set up basic reporting without extensive data visualization skills. The interface layout is easy to understand, and it doesn't take long to get used to.

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

Looker Studio is far easier to implement, stand up, and learn. The interface is simpler and user-friendly for various levels of data visualization/analysis knowledge and experience. The biggest benefit of Looker Studio, however, is its ease of connection to GA data and speed. Furthermore, since it is an online program/tool, it requires less CPU/battery/storage on the user's device.

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