Databricks Data Intelligence Platform vs. LiveRamp

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

For marketers or advertising agencies who are trying to find better ways to activate first party data to improve advertising performance, LiveRamp is the industry leading data onboarding platform. Once data is onboarded, it can be segmented in granular ways to create unique audience segments using boolean logic. LiveRamp is also well suited for identity resolution. LiveRamp will be a key player in the post-cookie world for advertisers and agencies. LiveRamp is an enterprise product for brands and agencies who invest heavily in paid media - if you aren't investing heavily in paid media and aren't investing in ad tech, LiveRamp probably isn't a good fit.

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

• The interface makes sense and is usable.
• Automated cleansing.
• Can assist in revenue generation for website.
• Great customer support.

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

• Better UI
• API connectivity
• More complex ETL processes

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

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.

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

LiveRamp has become the industry leader in data onboarding, identity resolution, and data management. LiveRamp has grown faster than Neustar and the business model is more specialized than Neustar. LiveRamp has chosen to just focus on identity resolution, data onboarding, and data management, whereas Neustar has several business units and is less focused. For these reasons, we prefer using LiveRamp's technology over Neustar.

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

• Has helped us to be able to monetize website.
• Saves us a lot of time and thus money.

Incentivized