Bhawna Singh

V2MOM, SMART, LRP. PCT. Behind those acronyms are actionable concepts that helped Manu Gurudatha, Engineering Leader at PagerDuty skyrocket engineering productivity. And he shared his exact process in an in-depth article where you’ll learn: ◾ How to implement V2MOM (Vision, Values, Methods, Obstacles, Measures) and plan better. ◾ Why setting SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals can bring you clarity and success in eng projects. ◾ Tips for effective Long-Range Planning (LRP) that aligns daily operations with your long-term business goals. ◾ How to reduce Process Cycle Time (PCT) to accelerate project delivery. Click on the link below to go from slipping to shipping, with real life examples at PagerDuty 👇 🔗 https://lnkd.in/eHUAc6qJ

27

2 Comments

Share how Gemini for #GoogleWorkspace enhances collaboration, improves communication, and boosts productivity within organizations

1

New on First Round Capital Review, Will Larson digs into why common management practices have become anti patterns, and which ones engineering execs should lean into to sharpen their team strategy. Always fun to learn from old Stripe co-workers :)

78

1 Comment

Great read on our journey to securing your AI investments from Steph Hay and Umesh Shankar #1 - AI opens up a world of possibilities. Be proactive in securing your AI investments - data, models, applications, and infrastructure. #2 - Make Google part of your security team, and Security Command Center - Enterprise as your default platform to help secure your AI investments. #3 - New capabilities in Notebook Scanner (private preview) and Model Armor (previewing soon) to secure your AI investments. Shout out to Amit Patil, Bharath Chandrasekhar, Matt Driscoll, Bhaskar Annadata, Cfir Cohen, Rahul Venkatraj and many more for these new capabilities.

67

Fellow in-house taxonomists, ontologists, metadata managers, and semantic wizards: what is the name of the team or business unit that you work within? I have the good fortune of getting to rename the "Content Standards" program I lead at Kaiser Permanente IT. My team has determined that the name is too vague to convey the core activities of the program, and leads to confusion with the cross-functional teams we collaborate with. I'm looking for a clear name that will resonate with stakeholders within my org. So I've got a very classic taxonomist question: What to call the program? 🤔 For some context... What my program is responsible for: - Defining and governing standards for metadata, structured data, taxonomy, and ontology for classifying/structuring digital content - Implementing processes and platforms to good metadata hygiene - Assessment and remediation of content metadata What my program doesn't cover: - Writing or editorial style guides - Content design - Content strategy - Site maps or navigational structure - Data governance Any ideas are most welcome!

36

48 Comments

Whether you’re a developer or a business practitioner — #GenAI impacts everyone. Join Varun Grover and Distinguished Engineer Jayant Thomas to get advice on starting your #AI journey. Watch now: https://vrt.as/4a5vLzB

2

🌟PerfectScale is now on the Datadog Marketplace🌟 We're excited to announce that PerfectScale, a comprehensive autonomous Kubernetes optimization platform, is now accessible on the Datadog Marketplace. New Datadog Alerts Integration enables you to: ✅ Monitor resilience risks in your Kubernetes environment in a centralized way. ✅Stay informed about any resilience risks identified in your Kubernetes. ✅Prioritize tasks and eliminate the issues before they impact performance and user experience. Want more information on the feature? 📓Read more about the update: https://hubs.la/Q02BHxdj0 📚Check our Documentation Portal: https://hubs.la/Q02BHwpX0 🖇️Visit the Datadog Integration page: https://hubs.la/Q02BHxk10 #kubernetes #devops #k8s #sre #datadog

51

2 Comments

Congratulations to the #Observability Product and Engineering teams on the launch of Log Manager & Insights, now in beta! Log Manager and Insights enables you to proactively store, inspect, and monitor your log data on the Fastly Platform as an alternative to sending it to one of Fastly's supported third-party logging endpoints. We surface key insights you need for monitoring without requiring you to build complex data pipelines, so you can focus on delivering value and optimizing your customers’ experiences. 

63

We are excited to announce the launch of Ursa engine, our Kafka API-compatible data streaming engine that is built on top of Lakehouse. Ursa is not something entirely new; it is built on our many years of experience building and operating #ApachePulsar and #ApacheKafka. To learn more about the #Ursa engine, I recommend our #PulsarSummit keynote presentation and announcement blog post. Here, I’d like to delve deeper into some of its features: 1. Making Kafka API a first-class citizen: In #ApachePulsar, we developed a unified messaging model combining queuing and streaming semantics into one protocol, aimed at enhancing data sharing between business applications. Although powerful and increasingly adopted by enterprises and unicorns, the requirement for application rewrites is impractical in today’s economy. Instead of building data gravity around a protocol, we open up access to the robust stream storage layer by extending support to the Kafka protocol, transitioning from a single protocol to a multi-protocol platform, facilitating greater flexibility and integration. 2. Achieve Stream-Table duality via Lakehouse Storage: Data streams commonly end up in data lakehouses, where integrating and maintaining them can be complex and resource-intensive. Ursa simplifies this by storing data streams as lakehouse tables in object storage. This integration not only streamlines operations but also allows seamless access to operational data for analytical processes. Thanks to its multi-modal storage engine which fosters data sharing across teams and departments. 3. Object storage as the primary storage solution: To better align with cloud economics, particularly for large workloads, Ursa makes ZooKeeper and BookKeeper optional and utilizes a cost-optimized write-ahead log based solely on object storage with leaderless and stateless brokers. This architecture leverages object storage for both data replication and storage, minimizing inter-AZ data transfers and reducing costs for latency-relaxed, high-volume workloads. By integrating these features at the multi-tenant level rather than at isolated cluster levels, Ursa eliminates costly data duplication and movement across clusters, allowing teams to map directly to tenants and be able to share data across teams and departments more efficiently. We believe that the future of data streaming platforms lies in being open and multi-faceted, fundamentally different from traditional data platforms. They are designed not just to store and process data, but to enhance organizational capabilities by fostering data sharing across the organization and integration with other systems. More and more data streaming platforms will look more like Ursa, with multi-protocol, multi-tenant, and multi-modal capabilities. Check out our announcement blog post about Ursa: https://lnkd.in/ghEKTYE6 #ApacheKafka #Lakehouse #DataStreaming

100

1 Comment

Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy

11

Scaling up — made effortless with AI. Our implementation of AI slashed engineering workloads by 60%, freeing up our engineers to focus on what truly matters. Learn how AI is empowering our team to achieve more:

24

As companies go multi-cloud in their deployments, we need stand alone MLOps/LLMOps solutions that can deployed across vs being dependent on any one platform. As developers use multiple code generators, we will need security solutions that go across all of them to support developers. As businesses use multiple LLMs/AI models/APIs across the enterprise, we will need stand alone solutions to help identify, implement, and secure data and use cases. These are great opportunities to build next gen independent large software companies.

48

6 Comments

What’s Next: RunWhen's Kyle Forster talks open source and microservices SDxCentral CEO Matt Palmer speaks with RunWhen's Kyle Forster about a better way for companies to support gigantic code bases.

2

AI helps defenders as well as attackers. Here is a nice concrete example of this: generative AI systems can't replace human responders, but they can make them faster and more accurate. We don't need to wait for "AGI" to augment the capabilties of incident responders.

35

Multi cloud GenAI needs, with all the building blocks for modern applications, from MongoDB...

10

The past two years at StarTree, working with the Apache Pinot community, have been fantastic. I’ve been able to get to know some incredibly talented people, and it’s validated my perspective that the advent of event-driven architecture is real and pressing, and even more interesting than I realized. Looking back, I’d never spent that much time focusing on analytics and the huge set of tools and concerns that attend it. It’s a critical ecosystem with a history deeper than digital computing itself, and I anticipate a lot of fruitful work with the Pinot community in the months and years to come. That being said, it’s ironic that my time working with this community has crystallized an important idea I’d been developing since before I joined it. The disruption introduced into our world by the perfect storm of the cloud, microservices, and ubiquitous Kafka has created an entirely new approach to building business software. It’s new, uncharted territory to be explored—and I want to help map out as much of it as possible, together with the hundreds and thousands developers who are doing the work of building applications in this new world. That’s why I’ve decided to transition from StarTree, and return to Confluent as VP of Developer Relations. The most valuable thing we can do as developers is write differentiated code that provides meaningful value to our customers or users. That code sits atop a huge stack of what is, with respect to their businesses, undifferentiated software—various kinds of frameworks and infrastructure components—that solves hard problems that application developers shouldn’t have to spend time on. They shouldn’t build that software, but they need to know how to navigate the terrain and make choices in the space they can trust. The problem is that the event-driven revolution has thrown a somewhat stable stack into relative disarray, so some of us are lured into creating little bits of data infrastructure in that unknown area between our code and Kafka. Those of us who are able to avoid that temptation still aren’t totally clear on what all that terrain looks like, and what infrastructure should even live there. History suggests this could take another five to ten years to stabilize. It will require the input of many communities, vendors, and a world full of developers. In my opinion, Confluent is the company that is best positioned to significantly move the needle on this problem, and that’s why I’m excited to (re)join them. One company can’t do it all, but their vision of the Data Streaming Platform is exactly what I’m talking about here. The opportunity to lead a world-class developer relations team at the company which has pioneered the journey so far, and is building the most comprehensive vision for its next stage, is a tremendous one. I’m delighted to be back, and eager to do my part to help chart our course to the future. It is, as I am wont to say, a wonderful time to be alive.

913

291 Comments