On this episode of The Cloud Pod, the team talks about the new AWS region in Malaysia, the launch of AWS App Composer, the expansion of spanner database capabilities, the release of a vision AI by Microsoft; Florence Foundation Model, and the three migration techniques to the cloud space.

A big thanks to this week’s sponsor, Foghorn Consulting, which provides full-stack cloud solutions with a focus on strategy, planning and execution for enterprises seeking to take advantage of the transformative capabilities of AWS, Google Cloud and Azure.

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On this episode of The Cloud Pod, the team talks about the possible replacement of CEO Sundar Pichai after Alphabet stock went up by just 1.9%, the new support feature of Amazon EKS for Kubernetes, three partner specializations just released by Google, and how clients have responded to the AI Powered Bing and Microsoft Edge.

A big thanks to this week’s sponsor, Foghorn Consulting, which provides full-stack cloud solutions with a focus on strategy, planning and execution for enterprises seeking to take advantage of the transformative capabilities of AWS, Google Cloud and Azure.

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In this episode, Daniel explains a new strategy towards observability aimed at contextualizing large volumes of data to make it easier for users to identify the root cause of problems with their systems.

Daniel Kim is a Principal Developer Relations Engineer at New Relic and the founder of Bit Project, a 501(c)(3) nonprofit dedicated to making tech accessible to under-served communities. His job is basically to get developers excited about Observability, and he hopes to inspire students to maximize their potential in tech through inclusive, accessible developer education. He is passionate about diversity and inclusion in tech, good food, and dad jokes.

First, it is important to differentiate between monitoring and observability. Monitoring is basically when a code is instrumented to send data to a backend, to give answers to preconceived questions. With Observability, the goal is to monitor your system so as to later ask questions that were not in mind during the instrumentation of the system. Hence, if something new comes up you can find the root cause without modifying the code. There are so many levels of things to check when troubleshooting to find the cause of a problem, and this is where observability comes in.

There are different use cases for logs, metrics, and traces; Logs are files that record events, warnings, or errors however logs are ephemeral which means there is increased risk of losing a lot of data. A system needs to be in place to move logs to a central source. Another issue with logs is that it is poorly structured data. Logs are good to have as the last step of observability. Metrics and traces can however help to narrow down where to search in the logs to solve an issue.

Metrics are measurements that reflect the performance or health of your applications. They give an overview of how the systems are doing but tend to not be very specific in finding the root cause of a problem; other forms of data have to be adopted to get a clear picture. This is where Traces come in.

Traces are pieces of data that track a request as it goes through the system. Because of this, they can identify the root cause of an error or bottlenecks slowing down the system. However, they are very expensive and as such sampling is used when tracing but this reduces the accuracy of traces. Correlating information from logs, metrics, and traces gives a full clear picture for debugging to be carried out successfully. A lot of New Relic customers strive to get more pieces of data to get errors faster.

To balance the right data at the right time with the right cost, the first step when collecting large amounts of data is to find out how your organization is leveraging the data. A quick audit of the data to identify useful data is helpful. This can be done monthly or quarterly. Unstructured logs are difficult to aggregate

In the cloud native space, being able to be compatible with as many people as possible will determine the winners because there are many projects people use in production. Projects that are compatible with many other projects are the way forward.

APM is still very useful to understand application performance and in the future, data from all sources will be correlated to figure out the cause of a problem. Getting value very early from the system involves having a solid infrastructure and installing APM. The real power of full stack observability is getting data from different parts of your stack so you can diagnose what part of your system is going wrong. Leveraging AI to make sense of large amounts of data for engineers is going to be a huge plus.

A lot of vendors claim that their alert systems will automatically generate all alerts for you but this is not true because they would not know your

On this episode of The Cloud Pod, the team discusses the AWS systems manager default enablement option for all EC2 instances in an account, different ideas from leveraging innovators plus subscription using $500 Google credits, the Azure Open Source Day, the new theme for the Oracle OCI Console, and lastly, different ways to migrate to a cloud provider.

A big thanks to this week’s sponsor, Foghorn Consulting, which provides full-stack cloud solutions with a focus on strategy, planning and execution for enterprises seeking to take advantage of the transformative capabilities of AWS, Google Cloud and Azure.

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On this episode of The Cloud Pod, the team highlights the new Graviton3-based images for users of AWS, new ways provided by Google to pay for its cloud services, the new partnership between Azure and the Finops Foundation, as well as Oracle’s new cloud banking, and the automation of CCOE.

A big thanks to this week’s sponsor, Foghorn Consulting, which provides full-stack cloud solutions with a focus on strategy, planning and execution for enterprises seeking to take advantage of the transformative capabilities of AWS, Google Cloud and Azure.

On this episode of The Cloud Pod, the team talks about the announcement of Amazon VPC resource map, Google’s new AI product, the new Bing AI-powered search engine, and why multiple accounts are necessary for data centers to carry out work seamlessly in the cloud.

A big thanks to this week’s sponsor, Foghorn Consulting, which provides full-stack cloud solutions with a focus on strategy, planning and execution for enterprises seeking to take advantage of the transformative capabilities of AWS, Google Cloud and Azure. 

In this episode, Peter sits with Rahul Thakkar to discuss the revolutionary AWS SimSpace Weaver, highlighting its unique function and applications across several industries.

Rahul Thakkar is the Director and General Manager of Simulation Technologies at Amazon Web Services. Before AWS, he held multiple executive roles at Boeing, Brivo, PIXIA, and DreamWorks Animation. He is an inventor, and global technology executive with a background in cloud computing, distributed and high-performance computing, media and entertainment, film, television, defense and intelligence, aerospace, and access control.

His film credits include Shrek, Antz, and Legend of Bagger Vance. In 2002, he was part of the team that won an Academy Award for Shrek as the Best Animated Feature. Again in 2016, at the 88th Annual Academy Awards, Thakkar received a Technical Achievement Award.

AWS SimSpace Weaver enables customers to run extremely large-scale spatial simulations without having to manage any of the underlying infrastructure. It also removes the complexity of state management of entities as they move about the simulation. Previously, carrying out such simulations would be done sequentially, in a cumbersome manner over years but now it can be done in parallel in weeks. Different organizations have tried out this functionality for several scenarios and the results have been amazing. This value was largely made possible due to the approach of working with customer feedback.

Rahul’s interest in the cloud came much later in his career which started initially in the R&D department of the Motion Picture industry where he created many of the complex graphics in movies. He later moved into a small start-up that was developing technologies for satellite imagery and mapping, and from here he moved to aerospace. Generally, he observed the problem that it is very expensive for companies to maintain their infrastructure when dealing with simulations. It also would drain resources and distract from the main focus of the company. Eventually, knew he had to use AWS, and now he works with them.

All the other primitive tools within AWS are being consumed to build the service. There is also the ability to write to S3 so that customers can write the simulations out. This helps customers to remember how the simulation played out.

Relating this new service to the metaverse, Rahul believes that when it comes to the metaverse, each organization has its vision of what it should be. However, AWS built the tools to empower these organizations to build their metaverses. Despite the possibility of having competition from Azure or GCP, the focus of AWS would remain on the customer and their needs, innovation on their behalf.

Identifying new problems that the service would be very applicable for is a great challenge that AWS relies on customers for, to help AWS envision where they want to go with the service. There are definitely many companies running simulations but it is hard to predict how many would migrate to the AWS SimSpace Weaver because it is still a new product. Nonetheless, a lot of industries are interested in this new service. These include smart cities, organizations ranging from local to federal or international, logistics and supply chains, large-scale event planning, or any situation where there is a need to simulate a large problem with digital replicas of the real world.

In this episode, Christine talks about her company, Honeycomb which runs on AWS, with the goal of promoting observability for clients interested in the performance of their code or those trying to identify problem areas that need to be corrected.

Christine Yen is the Co-Founder and CEO of Honeycomb. Before founding Honeycomb, she built analytics products at Parse/Facebook and loved writing software to separate signals from noise. Christine delights in being a developer in a room full of ops folks. Outside of work, Christine is kept busy by her two dogs and wants your sci-fi & fantasy book recommendations.

Honeycomb is an observability platform that helps customers understand why their code is behaving differently from what they expected. The inspiration behind this software came after Christine’s previous company was acquired by Facebook and they realized how software made it very easy to identify problems in large code data within a short time. This encouraged them to build the tool and make it available to all engineers.

If the first wave of DevOps was Ops-people learning how to automate their working code, the second wave would be helping developers learn to operate their code. Honeycomb is designed intentionally to ensure that all types of engineers can make sense of the tool.

Honeycomb has always come up with ways for customers to use AWS products and get the data reflected in Honeycomb to be manipulated. Over the last few months, they have ensured that it is possible for clients to plug into CloudWatch Log and CloudWatch metrics, and redirect data directly from AWS products into Honeycomb instead. Clients can also use Honeycomb to extract data based on what their applications are doing. This applies to performance optimization, experimentation, or any situation where a company wants to try a code to see how it performs on production. The focus remains on the application layer. Before Honeycomb, no one was using observability in this context.

The pricing of Honeycomb is based on the volume of data, which makes it predictable and understandable. Unlike when the pricing scale is based on the fidelity of the data, which can be quite expensive.

Challenges within the observability space: The question is how to help new engineers learn from the seasoned engineers on the team through paper trails left by the seasoned engineers. This is a problem that can only be solved by enabling teams to orient new engineers on their systems without having to create another question as part of the code.

Building an AI Approach in Honeycomb may not be suitable because of the context involved, since training effective machine learning models relies on a vast amount of easily classifiable data and this does not apply in the world of software; every engineering team’s systems are different from every other engineering team’s systems. Honeycomb is interested in using Al to build these models in order to help users know what questions to ask.

With Honeycomb, usage patterns are much more dependent on the curiosity and proficiency of the engineering team; while some engineers who are used to getting answers directly may just leave the software, those who have a culture of asking questions will benefit more from it.

On this episode of The Cloud Pod, the team discusses the upcoming 2023 in-person Google Cloud conference, the accessibility of AWS CloudTrail Lake for non-AWS activity events, the new updates from Azure Chaos studio, and the comparison between Oracle Cloud service and other Cloud providers. They also highlight the application and importance of VPCs in CCOE.

A big thanks to this week’s sponsor, Foghorn Consulting, which provides full-stack cloud solutions with a focus on strategy, planning and execution for enterprises seeking to take advantage of the transformative capabilities of AWS, Google Cloud and Azure. 

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