4 ways Rampiva helps make Data Lakes a reality

Nuix recently posted a great blog describing different ways that data lakes are transforming how their clients are managing the risk and value of data. You can view it here: https://www.nuix.com/blog/five-real-data-lake-customer-stories


This is clearly the “future” that clients are pushing data processing products – and, rightly so. There’s a network effect from aggregating data, particularly across data type, that helps teams draw insights.


But, it can also be burdensome. And costly. Particularly for teams with less mature teams and processes.


So, we identified 4 ways that Rampiva can help Nuix clients turn “data lakes” into a reality.


- Repeatable, Standardized Jobs

Any good analyst knows that data quality is essential for finding value in large pools of data. So, most teams that are investing in data lakes have already developed critical Quality Control workflows. Unfortunately, those workflows can be costly, and teams still have to reprocess data to fix mistakes.


Rampiva Scheduler allows teams to execute specific jobs in exactly the same way, every single time, every single day.


- Optimized Performance and Capacity

The other challenge about building and feeding a data lake is the volume and complexity of data involved. Maximizing Nuix throughput in a 24-hour period helps to keep costs down. It also makes it easier for teams to do “double duty” – routine (discovery, forensics) casework during the day, and updating the data lake during downtime.


Combining Rampiva Workflow with Rampiva Scheduler can leverage the full 24-hour processing window, improve worker efficiency, and leverage complex workflows that are custom-fit for specific use cases and/or data types.


- Blended Queue

One big advantage of data lakes is that multiple teams can leverage the resource to achieve their objectives. However, each of those use cases has different data requirements, priorities, and resource profiles.


Rampiva Scheduler makes it easier for teams to manage a pipeline of jobs related to multiple request types, because the jobs themselves are standardized.


- Metrics

I know, jeez Bill, metrics again?

But, managing a 24-hour pipeline of activity across multiple use cases requires a real-time dashboard into your current activity, the ability to perform rigorous A|B testing, and distributing cost to departments in a routine manner. Meet Rampiva Dashboards.

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