Krish Subramanian

Cloudflare Dev offering is the best in the entire Tech Industry. Why is no one talking about it. Alternative to Heroku, Netlify & Vercel is Cloudflare workers & Cloudflare Pages. They are also the best alternative to LLM Stack like Together AI, Replicate AI, Anyscale AI etc. You could get so much done for so less. My own personal portfolio website is hosted on Cloudflare Workers for the past 6 months. Ofcourse Cloudflare doesn't do marketing and thus thats why they have the cheapest offering in the industry with very big free limits. Just check this out - https://lnkd.in/dcYiKrjM Founders and builders should definitely check it out.

6

2 Comments

A really nice read from Sai Maddali on the benefits of Tiered Storage with Amazon MSK. https://lnkd.in/gGNJ5nyt #ApacheKafka #Kafka #AWS #MSK #tieredstorage #streaming #storage

55

2 Comments

Adam's post is good (and I like this categorization in general), but there are a couple of things that I don't agree with. First, I'd say time-to-recovery is a strength of CfD rather than CfM. Physalia (from our paper "Millions of Tiny Databases" https://lnkd.in/gepZPA2Y) is the CfD system that provides the metadata for EBS (https://lnkd.in/g7R3MgYk) a vastly bigger CfM system. We chose CfD for Physalia specifically to optimize recovery time, because it needs to recover before the larger system can recover. Second, I think the post is making an assumption that CfM systems are f+1 on the dataplane, use the minimal number of replicas, and don't use quorums. Aurora is an instructive counter example (https://lnkd.in/gNrNTPp7).

117

2 Comments

ScaleOps - Cloud-Native Optimization just turned up the heat in cloud optimization, grabbing a cool $58M in Series B like it was a no-brainer. Hats off to Yodar Shafrir and Guy Baron—the brains behind this real-time cloud orchestration powerhouse. Lightspeed led this round, with NFX, Glilot Capital Partners, and Picture Capital jumping on board. If you’re not paying attention to what these guys are building, you’re about to get left in the cloud dust. ScaleOps isn’t just fine-tuning the cloud; they’re ripping out the old wiring and rewiring the whole system with AI-powered efficiency. Let’s talk tech—predictive horizontal pod auto-scaling. It’s like handing your cloud the ability to read the future, scaling on command with zero wasted spend. The platform’s smart enough to handle intelligent pod placement like it’s playing 4D chess, lining up resources with precision so you’re not paying for an ounce more than you need. And here’s where it gets wild: ScaleOps cracked the code on automated pod rightsizing, dumping the whole tedious manual game of CPU and memory allocation. It’s not a tweak; it’s a full-blown transformation, letting your DevOps sleep while ScaleOps does the heavy lifting. Meanwhile, their node optimization chops are saving companies thousands in cloud costs with every new deployment. It’s no shock that ScaleOps has #Fortune100 like Wiz, SentinelOne, and Orca Security already in the mix. For anyone serious about squeezing every last drop of efficiency from their cloud budget, this is it. ScaleOps isn’t just in the optimization business—they’re taking the whole category up a notch. So what’s next for these cloud kings? With $58M in the tank, they’re scaling their dev team, building out new features, and expanding operations to make cloud management feel like child’s play. ScaleOps isn’t here to play nice with cloud spend; they’re here to redefine the game. Watch this space, because Yodar and Guy aren’t stopping until they’ve changed the whole damn landscape. #Cloud #CloudComputing #CPU #CloudCost #CloudOptimization #Optimization #AutoScaling #AI #CloudAI #Technology #Innovation #TechEcosystem #StartupEcosystem #VentureCapital #StartupCapital #startups

2

Incredibly proud of the latest Kinesis Client Library version (3.0) which, when compared to previous KCL versions, reduces compute costs by up to 33%. KCL 3.0 introduces an enhanced load balancing algorithm that continuously monitors resource utilization of the stream processing workers and automatically redistributes the load from over-utilized workers to other underutilized workers. This ensures even CPU utilization across workers and removes the need to over-provision the stream processing compute workers which reduces cost. Additionally, KCL 3.0 is built with the AWS SDK for Java 2.x for improved performance and security features, fully removing the dependency on the AWS SDK for Java 1.x. #Kinesis #DataStreaming #KCL

94

➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

15

1 Comment

In search of higher quality revenue, more AI startups will need to serve non-tech enterprises - Anshul Ramachandran of Codeium calls for AI startups to become 'enterprise infrastructure native'. Of the many product design choices this entails, being able to serve your product on-prem or in single-tenant clouds corresponds to a longer-term tailwind of repatriation of cloud workloads. Sarah Wang wrote about 'the cost of cloud' a few years ago, with savings from repatriating of roughly one-third when you reach a certain scale. As buyers learn that a large proportion of promised AI productivity is premised on access to company data, this will be another tailwind for AI workloads to be hybrid or on-prem (as surveys from Barclays and Morgan Stanley show). Tomasz Tunguz wrote about the hub and spoke Cloud Prem model in 2020. Strikingly, the definition back then neatly conforms to the emerging consensus of how agents will interact with databases to complete work. https://lnkd.in/eRJFynVC

49

10 Comments

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

This conversation is very important to you if you're managing #Kubernetes clusters at scale. It highlights the multiple issues with one of the basic resources in Kubernetes API - the Node API. TLDR; There's a growing agreement that Node API is ripe for a redesign because of its lifecycle management challenges (e.g draining workflow) that have been resolved as afterthoughts along the way. https://lnkd.in/dhJfUBGU

11

#MySQL 8 with the SYS Schema provides real-time insights into buffer pool activity, data I/O, and transaction logs. Monitoring these metrics helps identify bottlenecks, optimize configurations, and enhance database performance and availability. #MySQLPerformance @MinervaDB #dba https://zurl.co/X0wb

1

Join me next week for a discussion about table partitioning at the inaugural 🐘 Postgres Meetup for All 🐘 virtual meetup. We'll talk about partitioning basics, the why and how, and even a few things that recent versions of Postgres have improved. https://buff.ly/4eRUJUU The goal for "Postgres Meetup for All" is to provide not only a variety of speakers and topics, but time for discussion and interaction. I'm planning on a round of introductions from attendees, a short presentation (20-30 minutes), and then discussion time. If you're thinking about table partitioning, working through issues, or even have some tips of your own to share, join us for this discussion.

17

Materialized views in #PostgreSQL store pre-computed results of frequent, complex queries to boost performance. They are periodically refreshed for up-to-date data but require careful management of refresh frequency, disk space, and resources. #PostgreSQLDBA @MinervaDB https://zurl.co/7ixU

15

My whole career has been at companies whose biggest competitor is business as usual. Cloud Virtualization Deduplicated back up Direct attached storage Automated resource allocation Hyperconverged infrastructure These were crazy ideas initially. Now they are industry standards. Not because they are cool. But because the business outcomes they drove were powerful. Powerful enough to cut through the political and organizational debt that exists in every large company. Powerful enough to CREATE budget for. Powerful enough to change the way people thought about solving a problem they’ve dealt with for a long time. Shift Group is different in that we don’t solve a technical problem, but the problem we solve is a function of business as usual. Posting jobs online, going through resumes, spending time screening candidates that aren’t a fit, hiring people that are not committed to success and then realizing it too late. High attrition. Slow ramp time. Onboarding risk. Every single company in the world deals with these problems. Our brains are so broken through years of conditioning companies actually brag about the problem!!! “We get 1000 applicants for every job post” Sick bud - then what happens? Wasted time, money, and energy - that’s what. I’ve seen this movie for 2 decades and I know how it ends. We will change this. We will be the industry standard. #AllGasNoBrakes #DialedIn

123

5 Comments

KubeCon 2024 highlights the powerful synergy between Kubernetes and machine learning, showcasing how these technologies are driving innovation in cloud-native environments. From simplifying ML workflows to enhancing scalability and efficiency, Kubernetes is becoming a cornerstone for deploying and managing AI applications. As organizations increasingly adopt machine learning, Kubernetes offers the tools needed to streamline complex processes and accelerate innovation, setting a new standard for AI-driven cloud solutions. Read more from Silicon Angle here => https://tulle.cc/5f6999 #Kubernetes #MachineLearning #AI #CloudNative #KubeCon #TechInnovation #Automation #Scalability #FutureOfTech

6

Cloud cost optimization needs to shift left, Listen to why/how with Flex Compute, Listen to Khursheed Hassan on #cloudnativefm: https://lnkd.in/e42HB_A2 where cost prevention is the leading strategy, and cloud cost optimization tools are only deployed in a downstream process to catch a tiny number of usage anomalies that have slipped through the tight net of preventive checks Today there are 3 ways to provision capacity on Cloud √ On-Demand Instances: While they provide flexibility, they are often associated with cost inefficiencies and unpredictable expenses since the billing is based on usage per hour or minute, without any long-term commitments. This can cause costs to spiral out of control if resources aren't managed properly. √ Spot Instances: Though these offer cost savings, they come with their own set of challenges. They are less predictable, require complex orchestration, and introduce additional complexity to cloud infrastructure. There can also be latency issues, and instances may be reclaimed by the cloud provider without prior notification, leading to disruptions. √ Capacity Reservations: This option involves long-term commitments, typically ranging from one to three years. While it guarantees resource availability, there is a risk of underutilization. If resources are not actively used, organizations may still incur high costs, making this solution potentially expensive when workloads are not running at capacity." CloudiDR is building a solution around flex compute that effectively address non-steady state workloads, which inherently rise and fall. They’ve launched a product suite of tools around flex compute as Cyber Recovery Compute,' which can also be used for Disaster Recovery purposes. and  'Scheduled Capacity Compute.' their thesis is that and don’t want to replicate what cloud providers are already offering. Instead, focus on identifying unaddressed gaps in the market and creating innovative solutions that deliver the best value for their customers. Their solution refer to as Cyber Recovery Compute (or Disaster Recovery Compute), allows customers to avoid paying full reservation prices and sidesteps the unreliability of on-demand options. Instead, they can access guaranteed capacity from Cloud IDR at 75% lower costs. Their solution refer to as Schedule Capacity Compute, is specifically designed for workloads such as CI/CD pipelines or events. For instance, if a concert broadcast is scheduled or there are retail events featuring promotional coupons, we anticipate a significant increase in compute demand. Customers can schedule their compute resources with Cloud IDR in advance, ranging from as little as five minutes to several weeks ahead of time. They will then transfer the compute resources into their account at a minimum of 30% lower cost compared to on-demand pricing. Listen to and watch the full episode: https://lnkd.in/e42HB_A2 #cloud #hyperscalers #shiftleft #cloudoptimization

19

Optimize #PostgreSQL JOIN performance with a comprehensive troubleshooting guide. Learn how suboptimal indexing affects JOIN efficiency and discover tips for writing optimized queries to enhance database performance. #PostgreSQLPerformance @MinervaDB #dba https://zurl.co/M9VL

2

In Datadog's recent State of Cloud Costs, it's revealed that more than 80% of container spend is wasted on idle resources, with containers contributing to 1/3rd of the EC2 compute spend. Over-provisioning resources is a cloud sin. Hence, at Industrility, we advocate for #serverless and scaling-to-zero-when-no-one-is-using-it architectures to combat this issue. Since the days of AWS Simple Monthly Calculator (https://lnkd.in/gQJuHUKG) back in 2006 till date, we have been dedicated to helping organizations save millions in AWS Cloud costs. Our team of cost-optimization experts focuses on reducing waste, eliminating idle resources, and establishing guardrails for customers to avoid recurrence and moving traditional cloud architectures to consumption-based architectures. Our deep cloud architecture is rooted in #serverless technologies. #cloudcosts #costoptimization #AWS #Industrility #serverless #cloudarchitecture

12

1 Comment