In an environment where cloud-scaling applications is becoming more and more important, client-server architectures paradigms, as shown by memcached, are back with vengeance. In this talk, Galder will talk about Hot Rod, Infinispan's new client/server binary protocol, explaining the key differences compared to memcached's binary protocol, such as the possibility of receiving cluster topology changes. Audience of this talk will learn of the importance of Hot Rod in 'cloud-scale' application server clustering, where stateless application server instances could use Infinispan Hot Rod clients to retrieve state from an elastic farm of Infinispan Hot Rod servers, improving capabilities to run application server instances as a PaaS. The talk will finish with a brief demo of a cluster of Infinispan Hot Rod servers running on EC2 being accessed from a non-Java client. The audience is expected to have an intermediate understanding of client-server software architectures and cloud deployments.
Kafka error handling patterns and best practices | Hemant Desale and Aruna Ka...HostedbyConfluent
Transaction Banking from Goldman Sachs is a high volume, latency sensitive digital banking platform offering. We have chosen an event driven architecture to build highly decoupled and independent microservices in a cloud native manner and are designed to meet the objectives of Security, Availability Latency and Scalability. Kafka was a natural choice – to decouple producers and consumers and to scale easily for high volume processing. However, there are certain aspects that require careful consideration – handling errors and partial failures, managing downtime of consumers, secure communication between brokers and producers / consumers. In this session, we will present the patterns and best practices that helped us build robust event driven applications. We will also present our solution approach that has been reused across multiple application domains. We hope that by sharing our experience, we can establish a reference implementation that application developers can benefit from.
Secured (Kerberos-based) Spark Notebook for Data Science: Spark Summit East t...Spark Summit
This presentation will provide technical design and development insights in order to set up a Kerberosied (secured) JupyterHub notebook using Spark. Joy will show how Bloomberg set up the Kerberos-based Spark-notebook-integrating JupyterHub, Sparkmagic, and Levy. Sparkmagic provides the Spark kernel for Scala and Python. Livy is one of the most promising open source software to allow to submit Spark jobs over http-based REST interfaces. In this presentation, Joy will highlight the capabilities of Sparkmagic and Livy, along with the gap or development required in order to integrate the software seamlessly to work with your secured Spark cluster. The Kerberos integration techniques that he’ll discuss can be applied to other types of authenticators, such as OAuth. No prior knowledge of any of these technologies is requied in order to understand this presentation.
Building Cloud-Native App Series - Part 1 of 11
Microservices Architecture Series
Design Thinking, Lean Startup, Agile (Kanban, Scrum),
User Stories, Domain-Driven Design
Have you ever wondered what the relative differences are between two of the more popular open source, in-memory data stores and caches? In this session, we will describe those differences and, more importantly, provide live demonstrations of the key capabilities that could have a major impact on your architectural Java application designs.
Kafka error handling patterns and best practices | Hemant Desale and Aruna Ka...HostedbyConfluent
Transaction Banking from Goldman Sachs is a high volume, latency sensitive digital banking platform offering. We have chosen an event driven architecture to build highly decoupled and independent microservices in a cloud native manner and are designed to meet the objectives of Security, Availability Latency and Scalability. Kafka was a natural choice – to decouple producers and consumers and to scale easily for high volume processing. However, there are certain aspects that require careful consideration – handling errors and partial failures, managing downtime of consumers, secure communication between brokers and producers / consumers. In this session, we will present the patterns and best practices that helped us build robust event driven applications. We will also present our solution approach that has been reused across multiple application domains. We hope that by sharing our experience, we can establish a reference implementation that application developers can benefit from.
Secured (Kerberos-based) Spark Notebook for Data Science: Spark Summit East t...Spark Summit
This presentation will provide technical design and development insights in order to set up a Kerberosied (secured) JupyterHub notebook using Spark. Joy will show how Bloomberg set up the Kerberos-based Spark-notebook-integrating JupyterHub, Sparkmagic, and Levy. Sparkmagic provides the Spark kernel for Scala and Python. Livy is one of the most promising open source software to allow to submit Spark jobs over http-based REST interfaces. In this presentation, Joy will highlight the capabilities of Sparkmagic and Livy, along with the gap or development required in order to integrate the software seamlessly to work with your secured Spark cluster. The Kerberos integration techniques that he’ll discuss can be applied to other types of authenticators, such as OAuth. No prior knowledge of any of these technologies is requied in order to understand this presentation.
Building Cloud-Native App Series - Part 1 of 11
Microservices Architecture Series
Design Thinking, Lean Startup, Agile (Kanban, Scrum),
User Stories, Domain-Driven Design
Have you ever wondered what the relative differences are between two of the more popular open source, in-memory data stores and caches? In this session, we will describe those differences and, more importantly, provide live demonstrations of the key capabilities that could have a major impact on your architectural Java application designs.
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Installation of Grafana on linux ; connectivity with Prometheus database , installation of Prometheus ; Installation of node_exporter ,Tomcat-exporter ; installation and configuration of alert manager .. Detailed step by step installation and working
Schema Registry 101 with Bill Bejeck | Kafka Summit London 2022HostedbyConfluent
If you were to ask any developer, ""what's a schema and where is it used?"" Most likely, you'd get an answer involving a relational database. The truth is the domain objects used in applications represent a contract, an implied schema, whether developers choose to acknowledge them or not. But even if you recognize the need for a formal schema, what's the best way to manage them?
This presentation will contain some theory and primarily practical application for schemas with Schema Registry. I'll briefly explain what a schema is and how it's very relevant to any application working with Kafka today. It will go into the practical, introducing Schema Registry, describing how it works and how developers can leverage it to provide schemas across an organization. The discussion will cover working with Schema Registry from the command line, how to leverage it with Kafka clients, and the supported serialization formats. Some established build tools that make life easier for the Kafka developer will also be covered.
Attendees will walk away with knowledge of Schema Registry and a solid understanding of how it works, how to integrate them into Kafka clients. They'll also learn enough about the supported serialization frameworks to start implementing schemas right away in their Kafka development efforts.
Building Cloud-Native App Series - Part 11 of 11
Microservices Architecture Series
Service Mesh - Observability
- Zipkin
- Prometheus
- Grafana
- Kiali
Processing Semantically-Ordered Streams in Financial ServicesFlink Forward
Flink Forward San Francisco 2022.
What if my data is already in order? Stream Processing has given us an elegant and powerful solution for running analytic queries and logic over high volumes of continuously arriving data. However, in both Apache Flink and Apache Beam, the notion of time-ordering is baked in at a very low level, making it difficult to express computations that are interested in a semantic-, rather than time-ordering of the data. In financial services, what often matters the most about the data moving between systems is not when the data was created, but in what order, to the extent that many institutions engineer a global sequencing over all data entering and produced by their systems to achieve complete determinism. How, then, can financial institutions and others best employ Stream Processing on streams of data that are already ordered? I will cover various techniques that can make this work, as well as seek input from the community on how Flink might be improved to better support these use-cases.
by
Patrick Lucas
Kafka Streams State Stores Being Persistentconfluent
Being Persistent: A Look Into Kafka Streams State Stores, Neil Buesing, Principal Solutions Architect, Rill Data
Meetup link: https://www.meetup.com/TwinCities-Apache-Kafka/events/284002062/
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...Flink Forward
Flink Forward San Francisco 2022.
Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way.
by
Jeff Chao
A stream processing platform is not an island unto itself; it must be connected to all of your existing data systems, applications, and sources. In this talk we will provide different options for integrating systems and applications with Apache Kafka, with a focus on the Kafka Connect framework and the ecosystem of Kafka connectors. We will discuss the intended use cases for Kafka Connect and share our experience and best practices for building large-scale data pipelines using Apache Kafka.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Big data real time architectures -
How do to big data processing in real time?
What architectures are out there to support this paradigm?
Which one should we choose?
What Advantages / Pitfalls they contain.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Streaming all over the world Real life use cases with Kafka Streamsconfluent
Streaming all over the world Real life use cases with Kafka Streams, Dr. Benedikt Linse, Senior Solutions Architect, Confluent
https://www.meetup.com/Apache-Kafka-Germany-Munich/events/281819704/
Comparing the TCO of HP NonStop with Oracle RACThomas Burg
HP NonStop is often (wrongly!) perceived as "expensive", specifically compared with the combination of "vanilla X86 hardware" and the Oracle RAC DB offering.
This presentation talks about an in-depth analysis HP did to compare the two offerings fair and square. You might be surprised at the results ...
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Installation of Grafana on linux ; connectivity with Prometheus database , installation of Prometheus ; Installation of node_exporter ,Tomcat-exporter ; installation and configuration of alert manager .. Detailed step by step installation and working
Schema Registry 101 with Bill Bejeck | Kafka Summit London 2022HostedbyConfluent
If you were to ask any developer, ""what's a schema and where is it used?"" Most likely, you'd get an answer involving a relational database. The truth is the domain objects used in applications represent a contract, an implied schema, whether developers choose to acknowledge them or not. But even if you recognize the need for a formal schema, what's the best way to manage them?
This presentation will contain some theory and primarily practical application for schemas with Schema Registry. I'll briefly explain what a schema is and how it's very relevant to any application working with Kafka today. It will go into the practical, introducing Schema Registry, describing how it works and how developers can leverage it to provide schemas across an organization. The discussion will cover working with Schema Registry from the command line, how to leverage it with Kafka clients, and the supported serialization formats. Some established build tools that make life easier for the Kafka developer will also be covered.
Attendees will walk away with knowledge of Schema Registry and a solid understanding of how it works, how to integrate them into Kafka clients. They'll also learn enough about the supported serialization frameworks to start implementing schemas right away in their Kafka development efforts.
Building Cloud-Native App Series - Part 11 of 11
Microservices Architecture Series
Service Mesh - Observability
- Zipkin
- Prometheus
- Grafana
- Kiali
Processing Semantically-Ordered Streams in Financial ServicesFlink Forward
Flink Forward San Francisco 2022.
What if my data is already in order? Stream Processing has given us an elegant and powerful solution for running analytic queries and logic over high volumes of continuously arriving data. However, in both Apache Flink and Apache Beam, the notion of time-ordering is baked in at a very low level, making it difficult to express computations that are interested in a semantic-, rather than time-ordering of the data. In financial services, what often matters the most about the data moving between systems is not when the data was created, but in what order, to the extent that many institutions engineer a global sequencing over all data entering and produced by their systems to achieve complete determinism. How, then, can financial institutions and others best employ Stream Processing on streams of data that are already ordered? I will cover various techniques that can make this work, as well as seek input from the community on how Flink might be improved to better support these use-cases.
by
Patrick Lucas
Kafka Streams State Stores Being Persistentconfluent
Being Persistent: A Look Into Kafka Streams State Stores, Neil Buesing, Principal Solutions Architect, Rill Data
Meetup link: https://www.meetup.com/TwinCities-Apache-Kafka/events/284002062/
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...Flink Forward
Flink Forward San Francisco 2022.
Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way.
by
Jeff Chao
A stream processing platform is not an island unto itself; it must be connected to all of your existing data systems, applications, and sources. In this talk we will provide different options for integrating systems and applications with Apache Kafka, with a focus on the Kafka Connect framework and the ecosystem of Kafka connectors. We will discuss the intended use cases for Kafka Connect and share our experience and best practices for building large-scale data pipelines using Apache Kafka.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Big data real time architectures -
How do to big data processing in real time?
What architectures are out there to support this paradigm?
Which one should we choose?
What Advantages / Pitfalls they contain.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Streaming all over the world Real life use cases with Kafka Streamsconfluent
Streaming all over the world Real life use cases with Kafka Streams, Dr. Benedikt Linse, Senior Solutions Architect, Confluent
https://www.meetup.com/Apache-Kafka-Germany-Munich/events/281819704/
Comparing the TCO of HP NonStop with Oracle RACThomas Burg
HP NonStop is often (wrongly!) perceived as "expensive", specifically compared with the combination of "vanilla X86 hardware" and the Oracle RAC DB offering.
This presentation talks about an in-depth analysis HP did to compare the two offerings fair and square. You might be surprised at the results ...
Advanced Data Retrieval and Analytics with Apache Spark and Openstack SwiftDaniel Krook
Lightning talk from the OpenStack NYC meetup on October 8, 2014.
http://bit.ly/ibm-os-meetup
By Gil Vernik
The integration between Apache Spark and Swift, and the use of Storlets for smart retrieval via filtering and privacy-support.
The content of this talk is a statement from the IBM Research division, not IBM product divisions, and is not a statement from IBM regarding its plans, directions or product intents. Any activities described by this talk are subject to change.
ELC-E 2010: The Right Approach to Minimal Boot Timesandrewmurraympc
This was presented at ELC-E 2010 in Cambridge and describes an approach to cold boot time reduction. It also demonstrates the approach through a case study with an MS7724 reference board.
Best Practices for Virtualizing Apache HadoopHortonworks
Join this webinar to discuss best practices for designing and building a solid, robust and flexible Hadoop platform on an enterprise virtual infrastructure. Attendees will learn the flexibility and operational advantages of Virtual Machines such as fast provisioning, cloning, high levels of standardization, hybrid storage, vMotioning, increased stabilization of the entire software stack, High Availability and Fault Tolerance. This is a can`t miss presentation for anyone wanting to understand design, configuration and deployment of Hadoop in virtual infrastructures.
Over the past year, more and more Java applications have benefited from gaining access to elastic, cloud-ready, data grids thanks to Infinispan, and from now on, Ruby apps running within TorqueBox get the same benefit as well thanks to the ability of TorqueBox to talk to Infinispan Hot Rod servers. In this talk, Galder will demo the integrations between TorqueBox, which is a Ruby application plattform, and Infinispan, which is a Java based data grid plattform, highlighting the benefits for TorqueBox administrators and Ruby developers, which include, amongst others, access to highly-scalable, low-latency data store that avoids single point of failure.
In this session, Galder Zamarreño, a senior software engineer at Red Hat, will:
- Provide a brief introduction to RESTful principles
Discuss how cloud-scale APIs are done best with REST
- Introduce Infinispan REST server, focusing on its cloud capabilities and simple REST API
- Detail how REST can apply to many APIs, focusing on some of the deeper principles and practices behind it and how easy it is to implement and use
Keeping Infinispan In Shape: Highly-Precise, Scalable Data EvictionGalder Zamarreño
Java Collections Framework represents one of the key building blocks of any Java application. Although the standard JDK devoted a lot of attention to developing a coherent and easy to use collections framework one important aspect remains overlooked – collection element eviction. Collection memory footprint can not grow indefinitely because we would eventually run out of memory; we either have to remove elements from a collection or somehow periodically evict certain elements according to a chosen eviction algorithm. Since day one eviction has been a key feature of Infinispan, and in the latest 4.1 release thanks to event update batching, Infinispan has reduced the eviction overhead to such an extent that it hardly affects application performance. On top of that, Infinispan implements LIRS, a more precise eviction algorithm compared to the traditional LRU, making it the first open source project to implement this revolutionary algorithm in the data grid space. In this session, Galder and Vladimir will present to the details behind these changes, performance measurements and third-party use case testimonies.
Infinispan Servers: Beyond peer-to-peer data gridsGalder Zamarreño
In this session, Infinispan developer Galder Zamarreño will:
- Provide a brief introduction to peer-to-peer and client/server architectures.
- Describe the benefits of using Infinispan in a client/server mode, particularly in cloud-style environments.
- Introduce the audience to Infinispan’s selection of server modules that provide varied access methods: REST and WebSocket for HTTP access, Memcached protocol access and Hot Rod, Infinispan’s very own highly efficient binary protocol which supports smart-clients.
- Demonstrate an Infinispan client/server example showing how geographically separated Infinispan data grids could be linked together via Hot Rod client/server modules in order to provide different disaster recovery strategies.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
2. Infinispan’s Hot Rod Protocol
Galder Zamarreño
Senior Software Engineer, Red Hat
21st June 2010
Tuesday, June 22, 2010
3. Who is Galder?
• Core R&D engineer on Infinispan and JBoss Cache
• Contributor and committer on JBoss AS, Hibernate,
JGroups, JBoss Portal,...etc
Galder Zamarreño | galder@jboss.org 3
Tuesday, June 22, 2010
4. Agenda
• Infinispan peer-to-peer vs Infinispan client-server mode
• What is Hot Rod
• The motivations behind Hot Rod
• Hot Rod implementations and sample code
• Infinispan server comparison
• The path ahead for Hot Rod
• Demo
Galder Zamarreño | galder@jboss.org 4
Tuesday, June 22, 2010
5. Infinispan Peer-To-Peer
• Infinispan is an in-memory
distributed data grid
• Traditionally, deployed in
peer-to-peer (p2p) mode
Galder Zamarreño | galder@jboss.org 5
Tuesday, June 22, 2010
6. Infinispan Client-Server
• Sometimes client-server
makes more sense
• E.g., access from non-JVM
environment
• No Infinispan running on
client
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Tuesday, June 22, 2010
7. Infinispan Client-Server
• P2P data grids do not get
along with elastic application
tiers
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Tuesday, June 22, 2010
8. Infinispan Client-Server
• Elastic application tiers work
better with client-server
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Tuesday, June 22, 2010
9. Infinispan Client-Server
• Multiple applications with data
storage needs
• Starting a data grid per app
is wasteful
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Tuesday, June 22, 2010
10. Infinispan Client-Server
• Data service tier
• Keep a pool of data grid
nodes as shared storage tier
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Tuesday, June 22, 2010
11. Infinispan Client-Server
• More examples:
• Independent tier
management
• E.g., upgrading AS without
bringing down DB
• Contrasting JVM tuning
needs - CPU vs Memory
• Security
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Tuesday, June 22, 2010
15. What is Hot Rod?
• Hot Rod is Infinispan’s binary client-server protocol
• Protocol designed for smart clients, which have the ability to:
• Load balance and failover dynamically
• Smartly route requests
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Tuesday, June 22, 2010
16. Client Server with Hot Rod
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Tuesday, June 22, 2010
17. Client Server with Hot Rod
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Tuesday, June 22, 2010
18. The Hot Rod Protocol
• Transmitted keys and values treated as byte[]
• To ensure platform neutral behaviour
• Each operation prepended with cache name
• Basic operations:
• put, get, remove, containsKey, putIfAbsent, replace, clear
• stats, ping
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Tuesday, June 22, 2010
19. Data Consistency
• Concurrently accessed structures can suffer data
consistency issue
• Normally solved with JTA
• No JTA in Hot Rod (yet)
• Versioned API as solution
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Tuesday, June 22, 2010
22. Data Consistency in P2P
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Tuesday, June 22, 2010
23. Hot Rod Versioned API
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Tuesday, June 22, 2010
24. Hot Rod Versioned API
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Tuesday, June 22, 2010
25. Hot Rod Client Intelligence
• Different client intelligence levels supported:
• Basic clients
• Topology-aware clients
• Hash-distribution-aware clients
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Tuesday, June 22, 2010
26. Infinispan Hash Functions
• Infinispan uses language independent hash functions
• Used for smart routing
• Enables smart client implementations in any language
• So far, MurmurHash 2.0 implemented
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Tuesday, June 22, 2010
29. Hot Rod Implementations
• Server implementation included in 4.1.0.Beta2
• Uses high performance Netty socket framework
• Start via script: startServer.sh -r hotrod
• Java client reference implementation also available
• Supports all client intelligence levels
• Volunteers for writing clients in other languages welcomed :)
• If interested, join us at the Cloud Hackfest!
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Tuesday, June 22, 2010
30. Hot Rod Client Basic API
//API entry point, by default it connects to localhost:11311
CacheContainer cacheContainer = new RemoteCacheManager();
//obtain a handle to the remote default cache
Cache<String, String> cache = cacheContainer.getCache();
//now add something to the cache and make sure it is there
cache.put("car", "ferrari");
assert cache.get("car").equals("ferrari");
//remove the data
cache.remove("car");
assert !cache.containsKey("car") : "Value must have been removed!";
Galder Zamarreño | galder@jboss.org 30
Tuesday, June 22, 2010
31. Hot Rod Client Versioned API
//API entry point, by default it connects to localhost:11311
CacheContainer cacheContainer = new RemoteCacheManager();
//obtain a handle to the remote default cache
RemoteCache<String, String> remoteCache = cacheContainer.getCache();
//put something in the cache
remoteCache.put("car", "ferrari");
//retrieve the value and the version
RemoteCache.VersionedValue value = remoteCache.getVersioned("car");
//replace it with a new value passing the version read
assert remoteCache.replace("car", "mclaren", value.getVersion());
Galder Zamarreño | galder@jboss.org 31
Tuesday, June 22, 2010
32. Infinispan Servers Comparison
Client Smart Load Balancing /
Protocol Clustered
Availability Routing Failover
Right now, Yes, dynamic via
Hot Rod Binary Yes Yes
only Java Hot Rod client
Only with
Infinispan
Text Tons Yes No predefined list of
Memcached
servers
Infinispan Any Http Load
Text Tons Yes No
REST Balancer
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Tuesday, June 22, 2010
33. The path ahead for Hot Rod
• Within Hot Rod:
• Clients in other languages
• Querying
• Event handling...
• Submit protocol to a standards body (maybe)
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Tuesday, June 22, 2010
34. Hot Rod as base for new
functionality
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Tuesday, June 22, 2010
36. Summary
• Infinispan client-server architectures are needed
• Hot Rod is Infinispan’s binary client-server protocol
• Designed for load balancing, failover and smart routing
• Server and java client available now
• We need your help to build more clients!
• Hot Rod as foundation for interesting new functionality
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Tuesday, June 22, 2010
37. Questions?
• Project: www.infinispan.org
• Blog: blog.infinispan.org
• Twitter:
• @infinispan, @galderz
• #infinispan #judcon
• Join us at the Cloud Hackfest!!!
• JBoss Asylum Podcast recording - panel discussion
• Tonight, 8.30pm community room
Galder Zamarreño | galder@jboss.org 37
Tuesday, June 22, 2010
38. Learn more about Infinispan!
•Storing Data on Cloud Infrastructure in a Scalable,
Durable Manner - Wed 23rd
•Using Infinispan for High Availability, Load Balancing, &
Extreme Performance - Thu, 24th
•How to Stop Worrying & Start Caching in Java - Thu 24th
•Why RESTful Design for Cloud is Best - Fri 25th
Tuesday, June 22, 2010