Databricks Data Intelligence Platform vs. Tealium Customer Data Hub

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 more suited, I'll say highly regulated industry around data like healthcare, insurance, finance, where Tealium can be very helpful and for clients that don't have much different channels where they interact with their clients. So companies that only sell on internet and are a small company, I can see Tealium being the best usage for these clients, for these 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

• Simple tag management that is tough to beat. So many solutions that are incredibly simple to setup and gather data for.
• Security around the data is key and Tealium excels in this area.
• The speed impact of the tag setup is key to optimizing and allowing our web properties to run more effectively, which is a better experience for the end user.

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

• Audiences—don't they technically exist in Tealium? They are just streamed—no count, no backfill, etc.
• Working backward to identify issues involves lots of clicking in the UI, going from audience to audience attribute, badge to event attribute, and so on.
• You have to wait for a Real-Time event to see the payload. There is no sample or other option.

Incentivized

I already know that my company has no plans to discontinue use of Tealium. We are heavily reliant on it due to a huge number of product teams and developers we would have to work with to place tags across many pages. Tealium is already there on the pages, and our application/product teams are familiar with how to integrate it. It is just the simplest way to ensure that new data requirements are implemented in a timely manner.

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

Tealium iQ Tag Management System does exactly what it is intended for, it manages vendortags. Changes can be made to websites and apps in minutes. The frontend is well structured, updates are easy to carry out, and Tealium support is available at all times.

Incentivized

It's been flawless as far as I am concerned.

Incentivized

Sometimes publishing can take longer than expected

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 support team often is so quick to respond and so helpful when it comes to working with the needs of my clients and being able to resolve potential shortcomings or technical issues or surrounding the tool. There have been times more recently that I’ve gotten more generic service rather than the tailored experience that I have come to expect.

Incentivized

Did what we needed to onboard us onto the platform

Incentivized

Implementation had some bumps in the road and it was new for all of us, but for the most part, it was easier than many other implementations we've done with other technologies.

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

Tealium Customer Data Hub can do it all in one. Whereas, I think by using multiple Tealium Customer Data Hubs, we can utilize what each tool is really good at. In an ideal world, we'd like to use Tealium Customer Data Hub for everything, but one thing that we struggle with is Audience Segmentation, and we are looking for a one touch solution, without a lot of the work since the data is already there.

Incentivized

Like anything it could be cheaper but we still feel we get value from it.

Incentivized

Never had an issue, scales as we use it.

Incentivized

Very easy to work with and relatively cost effective

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

• There has been a near immeasurable return on customer data and improvement of our quality for our physical products due to be in tune with the customer. This has changed our way of doing things for the better to gain a better flow and overall workplace experience.
• A negative is that Tealium AudienceStream becomes harder to manuever and use data analytics for when a database has been existing for a fairly large amount of time. It goes from an agile ship to a huge vessel that takes many components to be able to move.

Incentivized