Bringing Knowledge+AI to Buyer Success
At Databricks, we need to carry knowledge and AI to all elements of our group. This yr, the Buyer Success group has partnered with our in-house knowledge scientists to create data-powered instruments that improve the best way we information our prospects on their journey to the Lakehouse. Certainly one of these initiatives is Sunstone, our inside advice platform.
Sunstone permits us to grasp how a buyer is leveraging the Databricks platform and customise suggestions amongst 4 completely different areas:
- Knowledge engineering
- Machine studying
- Knowledge evaluation
- Platform administration
On this weblog, we’ll discuss in regards to the historical past and motivation for Sunstone, how we construct our suggestions, and the way we have used it to make data-powered impacts for a lot of of our prospects. We are going to conclude with a quick part on our future plans for the mission. We hope this will probably be an inspiration for different data-driven buyer success groups trying to implement knowledge merchandise for their very own prospects.
Why we created Sunstone
Beforehand, serving to our prospects undertake finest practices required a number of cycles of data gathering and discovery classes. From these discovery classes, we might interact with people to study what position they’re in, what options they’re utilizing, and the way they’re implementing their use circumstances. We relied on these engagements to glean details about the place we may successfully assist prospects. It took on common 2-4 weeks to finish the cycle from scheduling a session to the shopper implementing the suggestions. We wanted to discover a strategy to velocity up the engagement and widen the breadth of suggestions and floor insights by means of telemetry.
We began with creating a rule that helped us establish the workloads working on older runtimes. It is vital for our prospects to remain present with a view to unlock the frequent enhancements we’re making to the Databricks runtime. Our knowledge scientists used our inside Lakehouse platform to instrument the function wanted to gasoline the advice. We then materialized this advice on inside dashboards, permitting our buyer dealing with groups to offer this advice with their very own prospects. We have since expanded our suite to incorporate many different suggestions, extending this functionality to our different core teams of customers together with, Knowledge Engineers and Knowledge Scientists, with suggestions on Delta, Structured Streaming, ML Stream, and extra. Sunstone has positively impacted our prospects as they’re implementing their use circumstances and we’ll discover extra on that within the subsequent part.
Huge advantages for our prospects at scale
By constructing a advice platform, we’re in a position to serve our prospects a constant technique of making certain they’re getting essentially the most worth out of the Databricks platform. Prior to now, it could have taken a number of discovery classes to study that the shopper did not have IP entry lists configured to limit entry to workspaces or weren’t utilizing cluster insurance policies to implement cluster creation constraints throughout their group. Now with Sunstone, we are able to generate this diagnostic earlier than we even meet our prospects for the primary time. This method has enabled the Buyer Success staff to onboard a buyer quicker, in addition to allow our prospects to develop their use circumstances quicker, resulting in an earlier and extra environment friendly go-live.
Let’s convert knowledge into actions
On this part, we’ll talk about our learnings on working with telemetry knowledge and influencing human conduct. Telemetry knowledge in its uncooked type is just not actionable. With a purpose to render the information to our goal, we take the telemetry knowledge and mix in finest practices to create a human readable rating. We wished the rating to be simply interpreted pretty much as good or unhealthy, so we designed the rating to be on a scale of 0 to 100 with 0 being least optimum and 100 being essentially the most optimum. A very good rating (larger than 80) is a constructive reinforcement that reveals the shopper is leveraging all of the beneficial options. A much less optimum rating (decrease than 50) prompts the shopper to research what’s inflicting it and opens the door to implementing modifications.
Our greatest practices are compiled from years of trial and error from buyer dealing with groups interacting with prospects. They’re knowledgeable, clever, and much from arbitrary. We take finest practices from the shopper groups and refine them additional with Product Supervisor and Product Specialist enter. After we are creating a brand new advice, it isn’t unusual that we see this rule improvement section debated over many iterations.
|Cluster 1 = Runtime 7.3
Cluster 2 = Runtime 9.1
Cluster 3 = Runtime 10.4
Cluster 4 = Runtime 11.2
Beneficial Motion: Preserve your clusters up to date.
|Your rating is 75/100. DBR 7.3 is coming as much as the tip of help whereas 11.3 permits Unity Catalog and quite a few different enhancements to the Databricks platform.
Beneficial Motion: Improve Cluster 1 to Runtime 11.3 LTS.
Determine 4. The distinction between a foul advice and an excellent advice is being particular and prescriptive.
It is vital to offer suggestions solely when an motion could be taken because of it. A foul advice states the information and presents little path on what to do with the knowledge. A very good advice gives you an concept of the severity of the issue and intuitively communicates what you should do to extend the rating.
Moreover, suggestions ought to be bucketed and filtered primarily based on whether or not it’s related to them. An information engineer could not care about Audit Logging so we do not give them that advice. As an alternative, we give the information engineer suggestions on utilizing Vacuum when creating tables of their knowledge pipelines.
|The client ought to be utilizing the newest Runtime model.
|Customers can have entry to the newest options and safety updates.
|The client ought to activate Audit Logging on all of their workspaces.
|Prospects will be capable of audit person exercise on their workspace.
|The client ought to be making Secrets and techniques API calls to all of their workspaces within the final 28 days.
|Customers can cover delicate credentials like passwords in notebooks.
|The client ought to be working Vacuum instructions on their tables.
|Use Delta Vacuum to take away outdated recordsdata to not incur pointless cloud storage prices.
Determine 5. Beneficial actions are given to related roles and advantages are clearly articulated.
How a buyer lately benefited from Sunstone
In working with one among our prospects, our Buyer Success Engineers consulted Sunstone to establish a set of suggestions. They realized instantly that there have been a number of vital options that they may suggest. These have been:
- Secrets and techniques: The workspaces listed right here have jobs and different workflows that aren’t using the secrets and techniques API, which prevents the publicity of delicate credential data in Databricks notebooks and jobs. Utilizing the secrets and techniques API is a straightforward means to make sure you’re securely using credential and related data in your Databricks workflows.
- Audit Logging: This rating measures the implementation of the audit logging function throughout workspaces. Audit logging is a service prospects can activate which sends low-latency logs in JSON format for any workspace wherein it has been configured. Each quarter-hour, Databricks will pipe buyer workspace-level metrics to a desired cloud storage location. They comprise a wealthy schema of data together with particulars on accounts, secrets and techniques, dbfs, secrets and techniques, and extra.
Of their common syncs with the shopper, they addressed our suggestions. Over the subsequent few months, the shoppers’ use of each Secrets and techniques and Audit Logs hit the highest of the mark.
By implementing these options, the shoppers elevated their safety and diminished their compliance danger.
In conclusion, Sunstone has enabled our buyer groups to raised perceive and serve their prospects with a diagnostic that intelligently qualifies and supplies actionable suggestions. We’re constructing data-powered instruments to make it simpler for our prospects to achieve success on Databricks. On the horizon is a transfer away from our present self-serve mannequin to at least one that immediately makes suggestions to prospects.
Sunstone has been adopted broadly throughout the Buyer Success group. It’s leveraged each day to offer over 100,000 actionable suggestions up to now this yr. On common, we have now additionally seen a 60% discount in ramp up time for our prospects.
If you’re curious about constructing or leveraging instruments like Sunstone, we’re hiring! If you’re curious about how the Databricks platform can allow your use circumstances, please e-mail us at [email protected].
We wish to thank our knowledge heroes Francois Callewaert and Catherine Ta from the Knowledge Science staff for creating Sunstone with us and accommodating Buyer Success’ many function requests. Additionally, thanks to Ravi Dharnikota and Francois Callewaert for serving to overview this weblog publish. Lastly, thanks Manish Bharti for offering an awesome case research to stroll by means of!