Databricks Data Intelligence Platform vs. Sentry

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.

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Great for standard web application performance monitoring, analytics and error reporting. Shows line level code errors, gives insight into performance issues (plugins, API issues, etc.). Automation and scheduled scanning in production gives client visibility into 'after deployment' value. Also lets a relatively small number of developers keep tabs on a handful of different site/applications without needing a bunch of tools. The UI is pretty complicated and can be overwhelming for new users. Documentation could be better for the learning curve,

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• 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

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• Great web interface. Lots of data available in a really clean format, with filtering options and more.
• Per-user exception tracking. User is complaining about something being broken? Look up their account ID in Sentry and you can see if they've run into any exceptions (with device information included, of course).
• Source map uploading. Took a little while to figure this out but now we have our deploy script upload sourcemaps to Sentry on each deployment, meaning we get to see stack traces that aren't obfuscated!
• Very generous free tier – 10,000 events per month. We're nowhere near that yet.

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• 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.

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• Alert Configuration. Would be really helpful to have multiple logical groupings within the "If" section of a single configurable alert.
• Alert Copying. Being able to copy an alert from one project to another would be super beneficial.
• Alert Tags. Better UI around how we select which tags are getting sent with each alert instead of a tiny text box.

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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

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Its incredibly versatile, but that leads to complexity for the uninitiated, which can be intimidating. Nevertheless its a well polished product, in our case leading to only using it for a focus on frontend is still more cost effective than buying a one-to-rule-them-all tool...

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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.

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It is cheaper and offers better support for front-end applications for enterprise large environments with more then 30 scrum teams and hundreds of micro frontend applications. The configuration options, both with the agent and from the user interface, are superior to other tools, and the documentation is also very easy to use.

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• 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.

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• So it has helped us for quick error resolutions.
• From dashboard we are able to assign errors to particular member and see when its resolved.
• Without customer informing us about errors we know what happened in real time helps in customer service also.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info