Videos Archive - Page 28 of 282

Who is Catchpoint and What Do We Do?

Appearance: Catchpoint Presents at AppDev Field Day 1

Video Links:

Vimeo: Who is Catchpoint and What Do We Do?
YouTube: Who is Catchpoint and What Do We Do?

Listen as Mehdi Daoudi talks through their humble beginnings and the origin of the need for Internet Performance Monitoring (IPM). Mehdi elaborates on the pervasive nature of digital experiences and how they are shaped by the world’s biggest brands. The presentation then posits the criticality of The Internet as your new application infrastructure before continuing to explain both the limitations of traditional Application Performance Monitoring (APM) and how IPM is purpose built to give deeper visibility into The Internet Stack – where agent-based APM has no reach. These critical points are catalyzed by a discrete explanation of The Internet Stack versus the application stack before asking a critical question: “What are you prepared to lose?” when Internet outages occur.

Don’t Limit What You Log But Optimize What You Analyze with Mezmo

Event: AppDev Field Day 1

Appearance: Mezmo Presents at AppDev Field Day 1

Company: Mezmo

Video Links:

Personnel: Bill Meyer

In this demo by Mezmo, you will learn steps DevOps, SREs, and Platform Engineers can take to understand their telemetry data (logs, metrics, traces, events) by data profiling, optimize it for cost and volume for observability platforms, and respond to changing application context using alerts on data-in-motion or routing data through alternate paths in case of any incident. In this session, you’ll learn how a telemetry pipeline can help lower observability costs, improve security and compliance, and provide business insights to maximize the outcomes of your data strategy.

The session will demonstrate:

Data profiling to identify meaningful application logs vs. OS, repetitive, and redundant logs, separating signal from noise.
How stateful pipelines help with the real-time detection of data aberrations.
Strategies to reduce log volumes, and capture and process any sensitive data/PII in logs before it reaches observability platforms like DataDog, New Relic, Prometheus, Grafana, and Splunk.

Control Your Telemetry Data with Confidence with Mezmo

Event: AppDev Field Day 1

Appearance: Mezmo Presents at AppDev Field Day 1

Company: Mezmo

Video Links:

Personnel: Bill Meyer, Tucker Callaway

Controlling telemetry data (logs, metrics, events, traces) has been a fundamental challenge as the data volumes increase and budgets decrease. Data’s continuous growth and dynamic nature cause DevOps and security teams to lose confidence in the data captured. On the other hand application developers are under pressure to minimize log generation to limit observability costs but are held responsible when the absence of logs increases MTTx for debugging and troubleshooting. This session discusses how Mezmo telemetry pipelines help organizations have confidence in their data by enabling them to understand telemetry data, optimize it for observability and security, and deliver responsiveness by detecting aberrations while data is in motion. This approach ensures cost control, business insights, and faster incident response.

The presentation by Mezmo at AppDev Field Day, led by Tucker Callaway, CEO, and supported by Bill Meyer, a principal sales engineer, focused on the challenges and solutions related to managing telemetry data (logs, metrics, events, traces) in organizations. Tucker introduced Mezmo’s role in solving observability problems since 2015, highlighting their experience with large-scale data handling and their partnership with major organizations like IBM Cloud.

The core discussion revolved around the increasing volume of telemetry data and the associated costs, which do not correspond to the value derived from the data. Mezmo addresses this issue through their telemetry pipelines, which allow organizations to take control of their telemetry data by enabling functionalities such as understanding, optimizing, and responding to data in real-time. This approach facilitates cost control, enhances business insights, and accelerates incident response times.

Tucker elaborated on the concept of telemetry data, emphasizing its unique characteristics compared to business data. He introduced the telemetry pipeline as a new control point for enterprises, enabling them to manage data effectively by routing, normalizing, enriching, and transforming it according to their needs. This system prevents over-reliance on external vendors and promotes internal management of data, ensuring compliance and reducing costs.

The presentation also touched on the challenges application developers face in balancing the generation of logs with observability costs and the pressures of debugging and troubleshooting. Mezmo’s solution helps bridge the communication gap between developers and platform engineers/SREs, facilitating better data management and operational efficiency.

Bill Meyer contributed by discussing the technical aspects of Mezmo’s platform, including data profiling, in-stream data aggregation, and dynamic adjustments to data handling in response to changes in the system or data structure. He also highlighted the importance of owning and understanding telemetry data to leverage it fully for operational and business insights.

What’s New with Google Cloud Run?

Event: AppDev Field Day 1

Appearance: Google Cloud Presents at AppDev Field Day 1

Company: Google Cloud

Video Links:

Vimeo: What’s New with Google Cloud Run?
YouTube: What’s New with Google Cloud Run?

Personnel: Sridhar Venkatakrishnan, Yunong Xiao

Cloud Run: The Serverless Platform for Developer Velocity and Enterprise-Grade Workloads. In this session, discover how Cloud Run, Google Cloud’s serverless platform, empowers developers to rapidly build and scale websites, APIs, and data processing workloads. Explore Cloud Run’s key features and learn how its latest advancements make it an enterprise-ready solution for your applications.

The presentation by Yunong Xiao and Sridhar Venkatakrishnan at AppDev Field Day focused on Google Cloud’s serverless platform, Cloud Run. They highlighted Cloud Run’s ability to empower developers to rapidly build and scale applications such as websites, APIs, and data processing workloads without managing infrastructure. Key features include its integration with Google’s AI products, leading developer experience, strong compliance and security posture, and the scalability derived from Google’s infrastructure.

Yunong Xiao emphasized Cloud Run’s efficiency in getting applications from development to production, its seamless integration with Google’s AI tools like Gemini Code Assist, and its fully managed environment which abstracts underlying complexities, allowing developers to focus on application logic rather than infrastructure.

Sridhar Venkatakrishnan discussed Cloud Run’s enterprise capabilities, explaining how it meets the needs of large organizations through features like direct VPC connectivity, allowing enterprises to securely connect their Cloud Run services to their private network. He also mentioned new features like automatic security updates for containers and multi-region deployment capabilities to enhance availability and security.

Visit https://cloud.google.com/run for more information.

Running GenAI Apps on Google Cloud

Event: AppDev Field Day 1

Appearance: Google Cloud Presents at AppDev Field Day 1

Company: Google Cloud

Video Links:

Vimeo: Running GenAI Apps on Google Cloud
YouTube: Running GenAI Apps on Google Cloud

Personnel: Lisa Shen, Steren Giannini

Deploy Generative AI Apps with Cloud Run in Google Cloud with confidence and speed: In this session, you will learn about Gen AI use cases in Cloud Run and how to combine LangChain on Cloud Run with Cloud SQL’s pgvector and Vertex Endpoints to create generative AI applications.

In the presentation, Steren Giannini and Lisa Shen from Google Cloud discuss deploying Generative AI (GenAI) applications using Cloud Run on Google Cloud. They highlight how GenAI is transforming interactions with technology, both in AI-assisted development and in applications that incorporate language and other AI models.

The presenters introduced GenAI apps, emphasizing their transformative role in technology interaction. They specifically mentioned LangChain, a popular framework for building language-based applications, which integrates seamlessly with Cloud Run for easy and fast deployment. They also discussed the architecture of GenAI apps, focusing on a model called Retrieval-Augmented Generation (RAG). This model enriches language model responses by querying a database with context data, improving accuracy.

Lisa Shen demonstrated the deployment process and operation of a GenAI app using Cloud Run, showcasing the ease of integrating with other Google Cloud services like Cloud SQL and Vertex AI. The demo involved setting up a web app to answer queries about Cloud Run features using data indexed from release notes stored in a vector database. They emphasized Cloud Run’s scalability, cost-effectiveness (scaling down to zero when not in use), and ease of use, which simplifies the deployment and management of containerized applications without the need to manage infrastructure.

Colab for the demo: https://codelabs.developers.google.com/codelabs/build-and-deploy-a-langchain-app-on-cloud-run

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