Databricks Data Intelligence Platform vs. mParticle

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

If you can't trust the data you get into your customer journey service or user behavior analysis tool, mParticle can make a difference quickly. We used it to great success getting from untrustworthy data to reliable data in no time. If you already have a great data pipeline or use a user behavior analysis tool that includes checks for ensuring the trustworthiness of data, you might not need mParticle. If you have a lot of external or internal services you need to connect to your customer data, then mParticle is definitely worth looking at.

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

• Collect customer data from multiple sources.
• Integrated with wide breadth of vendors and verticals.
• Helps track unregistered user data into registered user data.

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

• Support - often times it feels like they're trying to create a sales opportunity rather than actually provide support - whether it's technical or non-technical.
• Some connection setups are not as consistent with respect to the procedure of setting up the connection, e.g. setting user identities in Braze.
• Audiences...definitely not as robust as Amplitude

Incentivized

It's a great tool for beginners but not scalable to advance use cases. It's not it's a fault in our case, because any platform is as good as the data collected

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.

Superb technical support, always going the extra mile to understand your problem and work towards solutions. Specifically, the team are happy to jump on Zoom to troubleshoot the problem.

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

mParticle has a bigger catalog of services you can interface with and provides a more complete check for data integrity. It also allows you to use any analytics solution.

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

• Negative: Quite a lot overlaps with the work being done on the Data Warehouse as a CDP.
• Positive: Able to connect to 3rd party tools with small effort (Appsflyer).

Incentivized