Databricks Data Intelligence Platform vs. Reltio Connected Data Platform

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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The data model is really flexible, so it should function well across many industries. Reltio Connected Data Platform tends to add new features first to its industrial vertical customers, though, so it may be especially well suited if your use case is specifically supported.

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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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• integration with SFDC, D&B
• Reltio API allows us to build interfaces with ease.
• The beauty of Reltio is Consolidation at any time we can review data at field level from different sources (crosswalk) and configure match merge and survivorship rules to define operational value and approved data can be redistributed to the upstream or downstream systems.
• With Central data governance, enrichment with D&B allows creating trusted data and distribute to various systems.

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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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• UI does not support creating different profile views for an entity based upon customer types as example however we can achieve this by using sub entities ( sub classes). This feature is useful if we are creating profiles in reltio manually.
• Manual export every time we need to pick entities, fields etc does not allow to save templates. workaround is using postman creating predefined API with selected fields, filters and trigger export.
• UI does not support data masking which needed while we maintain PII information
• Potential Matches doesnt auto sort on confidence code so users need to manually select deselect relevant entities which is time consuming and painful process however this issue is when we have more than 4 entities as potential matches.
• It would be beneficial if UI displays data related errors (if any) between Reltio and SFDC.

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Good Feedback

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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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Love the UI

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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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We felt Reltio had the best user experience, with flexible graph database technology and a customizable UI. Also important was the ability to look at historical snapshots to see relationships as they existed as of a previous date.

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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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• unifying multiple sources of data = 10+
• Data quality on customer and vendors moved from 65% to 95%
• Reporting quality on operational, finance and sales improved from 70% to 95%

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Invited by TrustRadius (TR) on Vendor's Behalf

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. TR verified that a representative sample of customers was invited. More Info