Wednesday, November 17, 2021
WebAssembly is a high-performance, lightweight, polyglot, portable, and secure application runtime. It is ideally suited for many cloud native use cases, such as service mesh, embedded APIs for SaaS, and serverless functions for edge networks. However, as Docker and k8s’ success in cloud native infrastructure has shown, orchestration and management solutions are crucial for the adoption of application runtimes. In this presentation, Michael Yuan will use CNCF sandbox project WasmEdge as an example to discuss how to integrate and use widely used cloud native orchestration tools to manage WebAssembly workloads. Specific topics include the following. - WebAssembly use cases in cloud native infrastructure - Use runw to add WebAssembly management capabilities in CRI-O - Use crunw to support WebAssembly in k8s - Support WebAssembly as a service mesh sidecar in Dapr and others - Standardization efforts around WebAssembly orchestration and management
The number of microservices running in enterprises increases daily. As a result, service composition, governance, security, and observability are becoming a challenge to implement and incorporate. A “cell-based” architecture is an approach that can be applied to current or desired development and technologies to address these issues. This technology-neutral approach helps cloud-native dev teams become more efficient, act in a more self-organized manner, and speed overall release times. In this talk, Asanka will introduce the "cell-based" reference architecture, which is decentralized, API-centric, cloud-native and microservices friendly. He will explain the role of APIs in the cell-based approach, as well as examine how real applications are built as cells. Asanka will explore the metrics and approaches that can be used to measure the effectiveness of the architecture and explore how organizations can implement the cell approach.
One service call into your application can generate hundreds or more internal service calls across a mesh. How do you extract real value from the mesh by getting visibility into how your microservices are performing inside your applications? In this talk Hart Hoover, Field Engineer at Kong, will walk through an overview of CNCF’s Kuma, and the process of plugging in Prometheus metrics, CNCF Jaeger traces, and Grafana Loki log policies with a Kuma service mesh so you can gain real observability into your applications.
Kubernetes can solve many of the problems that apply to running dev environments in the cloud. This talk explores the benefits of cloud IDEs and discusses using K8s to host and manage remote IDEs for a dev team. Ben will present a demo of a Kubernetes cluster with workspaces for 25 developers that include code-server, JetBrains Projector, VS Code Remote, and pre-installed dev tools. Beyond the demo, Ben will also explore some of the complications associated with hosting dev environments at scale.
So your company has finally decided to move to the Cloud Native ecosystem. You’ve landed on containerization as your first step. You heard that all you needed to do was containerize your first app and then push it to Kubernetes/OpenShift/Nomad, and the cost savings just come. You’ve done this, and well, things have gone not as planned. Some of the tech didn’t do what you expected, and wait, what do you mean our OpEx has gone up? Simply said: the promise of containerization or migrating to the Cloud Native ecosystem can be a lie if you don’t do your homework. Sadly most companies don’t. In this talk, I’ll explain a few gotchas that a “few” enterprises, in the guise of AsgharLabs, hit moving towards the Cloud Native world, and hopefully, you’ll learn from their mistakes, so you’re trip down this path will be more comfortable and closer to the promise.Outline IntroductionsWhat is AsgharLabs and where they started, what they thought they needed to doWhere I came into the conversation to help AsgharLabs Questions you should ask after getting your app containerized Where are the architectural advantages and disadvantages? Are we doubling up on things? Isn’t automation good here? Why is this thing so complicated now? Questions you should ask about the cultural shift that will happen How the economics of the Cloud can differ from your DatacenterWhat do you mean our support is now Stack Overflow?What do you mean our goal is to move away from the CCB? Some tangible things you can start with to help become more successful Build that pipeline extension Collaborate with other teams Visibility and Monitoring Conclusion and where you can go from here
As software is becoming more pervasive, APIs are fast becoming the building blocks of business enterprises. And with that, the role of the API developer is gaining more responsibility for driving growth ranging from small to large companies. In this session, we will discuss how API developers can leverage tools to deliver quicker time to market value for both the business and the consumers.”
Over 2.5 trillion PDFs are created every year and contain a huge amount of valuable data. However, PDFs continue to be a challenge to reliably extracting data from.
In this session, we will learn:
- How you can use Adobe PDF Extract API to convert PDFs into JSON data use within your app.
- Introduce to the Adobe PDF Services SDKs (NodeJS, Python, Java)
- Extract tables easily to import into your own apps and databases
- Perform automated actions such as splitting PDFs, combining them, or flagging them based on certain criteria in the PDF.
The business demanded rapid innovation. Software development and IT figured out how to provide it. But now we have a whole host of new problems. In the resulting world of cloud-native apps, microservices, and API-driven applications, what we came to rely on for keeping it all running and secure is no longer enough.
In this new fog, we are basically “flying blind”. Modern applications are extremely hard to secure and protect as they are complex and continuously changing. Our visibility of what we have, how it is behaving, and how it is being used (and abused) has diminished tremendously. So how do we begin to see through the fog once again?
In this session you’ll learn:
Why are we flying blind
4 key areas to focus on to stop flying blind
A way to get started quickly (for free!)
For more information on Traceable AI, visit us at: www.traceable.ai
How does one choose to architect a system that has a Microservice / REST API endpoints? There are many solutions out there. Some are better than others. Should state be held in a server side component, or externally? Generally we are told this is not a good practice for a Cloud Native system, when the 12-factor guidelines seem to be all about stateless containers, but is it? It’s unclear and this confusion may lead to poor technology stack choices that are impossible or extremely hard to change later on as your system evolves in terms of demand and performance. While stateless systems are easier to work with, the reality is that we live in a stateful world, so we have to handle the state of data accordingly to ensure data integrity beyond securing it. We will examine and demonstrate the fundamentals of a Cloud Native system with Stateful Microservices that’s built with Open Liberty in Kubernetes: * Microservices/REST API – Options to use when running your apps in the JVM * Concurrency – how to take advantage of multi-core CPUs and clustered distributed systems * Stateful vs Stateless - while stateless apps are easier to implement, the bulk of the apps in production are stateful which involve a higher level of complexity and risk, especially when data would need to travel across multiple machine and network boundaries * Deployment – how about containerization and orchestration using Kubernetes?
Have you ever wanted to make your apps “smarter”? This session will cover what every ML/AI developer should know about Open Neural Network Exchange (ONNX) . Why it’s important and how it can reduce friction in incorporating machine learning models to your apps. We will show how to train models using the framework of your choice, save or convert models into ONNX, and deploy to cloud and edge using a high-performance runtime.
Thursday, November 18, 2021
In this talk I will present a technique for deploying machine learning models to provide real-time predictions using Apache Pulsar Functions. In order to provide a prediction in real-time, the model usually receives a single data point from the caller, and is expected to provide an accurate prediction within a few milliseconds.
Throughout this talk, I will demonstrate the steps required to deploy a fully-trained ML that predicts the delivery time for a food delivery service based upon real-time traffic information, the customer's location, and the restaurant that will be fulfilling the order.
Security is becoming a more prevalent issue every day, especially for young companies and developers looking to manage and update their applications.Ultimately, almost every application uses a database. However, traditional SQL systems are dated and lack out-of-the-box solutions for building clear audit trails, validating record integrity, and analyzing historical versions of the data.This talk will overview Blockpoint's Immutable, SQL compliant database management system, MDB, and how using an immutable database can benefit your data-driven applications.
We went from a single monolith to a set of microservices that are small, lightweight, and easy to implement. Microservices enable reusability, make it easier to change and scale apps on demand but they also introduce new problems. How do microservices interact with each other toward a common goal? How do you figure out what went wrong when a business process composed of several microservices fails? Should there be a central orchestrator controlling all interactions between services or should each service work independently, in a loosely coupled way, and only interact through shared events? In this talk, we’ll explore the Choreography vs Orchestration question and see demos of some of the tools that can help.
Over the past decade, graph databases have become an indispensable asset in dealing with networked and non-relational data. However, as the amount of data ingested into graph databases has exploded, performance has become a key criterion when determining which one to use. In addition, it is critical for organizations to understand the type of use cases in which graph databases can add business value relative to more traditional SQL or other types of NoSQL databases. During the workshop, we will dig into the fundamentals of Labeled Property Graphs and highlight use cases, ranging from fraud detection, money laundering, and complex manufacturing to real-time analytics for Customer 360. We will show where graph databases are not simply an option but perhaps the only choice available. Lastly, we will explore how to use Python to interact with the graph databases to extract features to be used in ML models. This hands-on workshop will cover: - Graph Fundamentals - Graph Use-Cases - Introduction to TigerGraph Cloud - Integrating Python with TigerGraph Cloud - Feature Generation for Supervised Machine Learning
Nobody complains that the database is too fast. But when things slow down they do complain. The two most popular ways of speeding up queries in a relational database are indexes and histograms, This talks covers when to use one over the other, how to properly construct an index, where histograms fail, and much more.