Optimizing Network Performance for Amazon EC2 Instances (CMP308-R1) - AWS re:...Amazon Web Services
Many customers are using Amazon EC2 instances to run applications with high performance networking requirements. In this session, we provide an overview of Amazon EC2 network performance features— including enhanced networking, ENA, and placement groups—and discuss how we are innovating on behalf of our customers to improve networking performance in a scalable and cost-efficient manner. We share best practices and performance tips for getting the best networking performance out of your Amazon EC2 instances.
A brief history of Instagram's adoption cycle of the open source distributed database Apache Cassandra, in addition to details about it's use case and implementation. This was presented at the San Francisco Cassandra Meetup at the Disqus HQ in August 2013.
Introduction to the Mysteries of ClickHouse Replication, By Robert Hodges and...Altinity Ltd
Presented at the webinar, July 31, 2019
Built-in replication is a powerful ClickHouse feature that helps scale data warehouse performance as well as ensure high availability. This webinar will introduce how replication works internally, explain configuration of clusters with replicas, and show you how to set up and manage ZooKeeper, which is necessary for replication to function. We'll finish off by showing useful replication tricks, such as utilizing replication to migrate data between hosts. Join us to become an expert in this important subject!
Redis + Structured Streaming—A Perfect Combination to Scale-Out Your Continuo...Databricks
"Continuous applications" supported by Apache Spark's Structured Streaming API enable real-time decision making in the areas such as IoT, AI, fraud mitigation, personalized experience, etc. All continuous applications have one thing in common: they collect data from various sources (devices in IoT, for example), process them in real-time (example: ETL), and deliver them to machine learning serving layer for decision making. Continuous applications face many challenges as they grow to production. Often, due to the rapid increase in the number devices or end-users or other data sources, the size of their data set grows exponentially. This results in a backlog of data to be processed. The data will no longer be processed in near-real-time. Redis, the open-source, in-memory database offers many options to handle this situation in a cost-effective manner. First and foremost, you could insert Redis into an existing continuous application without disrupting its architecture, and with minimal code changes. Redis, being in-memory, allows over a million writes per second with sub-millisecond latency. The Redis Stream data structure enables you to collect both binary and text data in the time series format. The consumer groups of Redis Stream help you match the data processing rate of your continuous application with the rate of data arrival from various sources. In this session, I will perform a live demonstration of how to integrate a continuous application using Apache Spark's Structured Streaming API with open source Redis. I will also walk through the code, and run a live IoT continuous application.
Speaker: Roshan Kumar
Optimizing Network Performance for Amazon EC2 Instances (CMP308-R1) - AWS re:...Amazon Web Services
Many customers are using Amazon EC2 instances to run applications with high performance networking requirements. In this session, we provide an overview of Amazon EC2 network performance features— including enhanced networking, ENA, and placement groups—and discuss how we are innovating on behalf of our customers to improve networking performance in a scalable and cost-efficient manner. We share best practices and performance tips for getting the best networking performance out of your Amazon EC2 instances.
A brief history of Instagram's adoption cycle of the open source distributed database Apache Cassandra, in addition to details about it's use case and implementation. This was presented at the San Francisco Cassandra Meetup at the Disqus HQ in August 2013.
Introduction to the Mysteries of ClickHouse Replication, By Robert Hodges and...Altinity Ltd
Presented at the webinar, July 31, 2019
Built-in replication is a powerful ClickHouse feature that helps scale data warehouse performance as well as ensure high availability. This webinar will introduce how replication works internally, explain configuration of clusters with replicas, and show you how to set up and manage ZooKeeper, which is necessary for replication to function. We'll finish off by showing useful replication tricks, such as utilizing replication to migrate data between hosts. Join us to become an expert in this important subject!
Redis + Structured Streaming—A Perfect Combination to Scale-Out Your Continuo...Databricks
"Continuous applications" supported by Apache Spark's Structured Streaming API enable real-time decision making in the areas such as IoT, AI, fraud mitigation, personalized experience, etc. All continuous applications have one thing in common: they collect data from various sources (devices in IoT, for example), process them in real-time (example: ETL), and deliver them to machine learning serving layer for decision making. Continuous applications face many challenges as they grow to production. Often, due to the rapid increase in the number devices or end-users or other data sources, the size of their data set grows exponentially. This results in a backlog of data to be processed. The data will no longer be processed in near-real-time. Redis, the open-source, in-memory database offers many options to handle this situation in a cost-effective manner. First and foremost, you could insert Redis into an existing continuous application without disrupting its architecture, and with minimal code changes. Redis, being in-memory, allows over a million writes per second with sub-millisecond latency. The Redis Stream data structure enables you to collect both binary and text data in the time series format. The consumer groups of Redis Stream help you match the data processing rate of your continuous application with the rate of data arrival from various sources. In this session, I will perform a live demonstration of how to integrate a continuous application using Apache Spark's Structured Streaming API with open source Redis. I will also walk through the code, and run a live IoT continuous application.
Speaker: Roshan Kumar
Introduction to Ceph, an open-source, massively scalable distributed file system.
This document explains the architecture of Ceph and integration with OpenStack.
Load Balancing MySQL with HAProxy - SlidesSeveralnines
Agenda:
* What is HAProxy?
* SQL Load balancing for MySQL
* Failure detection using MySQL health checks
* High Availability with Keepalived and Virtual IP
* Use cases: MySQL Cluster, Galera Cluster and MySQL Replication
* Alternative methods: Database drivers with inbuilt cluster support, MySQL proxy, MaxScale, ProxySQL
Hadoop Meetup Jan 2019 - Router-Based Federation and Storage TieringErik Krogen
A presentation by CR Hota and Ekanth Sethuramalingam of Uber regarding their storage infrastructure, leveraging HDFS features like Router-Based Federation and Storage Tiering.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
Seastore: Next Generation Backing Store for CephScyllaDB
Ceph is an open source distributed file system addressing file, block, and object storage use cases. Next generation storage devices require a change in strategy, so the community has been developing crimson-osd, an eventual replacement for ceph-osd intended to minimize cpu overhead and improve throughput and latency. Seastore is a new backing store for crimson-osd targeted at emerging storage technologies including persistent memory and ZNS devices.
This presentation provides an overview of the Dell PowerEdge R730xd server performance results with Red Hat Ceph Storage. It covers the advantages of using Red Hat Ceph Storage on Dell servers with their proven hardware components that provide high scalability, enhanced ROI cost benefits, and support of unstructured data.
Daniel Stenberg explains HTTP/3 and QUIC at GOTO 10, January 22, 2019. This is the slideset, see https://daniel.haxx.se/blog/2019/01/23/http-3-talk-on-video/ for the video.
HTTP/3 is the designated name for the coming next version of the protocol that is currently under development within the QUIC working group in the IETF.
HTTP/3 is designed to improve in areas where HTTP/2 still has some shortcomings, primarily by changing the transport layer. HTTP/3 is the first major protocol to step away from TCP and instead it uses QUIC.
Why the new protocols are deemed necessary, how they work, how they change how things are sent over the network and what some of the coming deployment challenges will be.
As you will see in this film, there are a lot of questions from an interested and educated audience.
Daniel Stenberg is the founder and lead developer of the curl project. He has worked on HTTP implementations for over twenty years. He has been involved in the HTTPbis working group in IETF for ten years and he worked with HTTP in Firefox for years before he left Mozilla. He participates in the QUIC working group and is the author of the widely read documents ”HTTP2 explained” and ”HTTP/3 explained”.
This is a presentation of the popular NoSQL database Apache Cassandra which was created by our team in the context of the module "Business Intelligence and Big Data Analysis".
Linux Kernel vs DPDK: HTTP Performance ShowdownScyllaDB
In this session I will use a simple HTTP benchmark to compare the performance of the Linux kernel networking stack with userspace networking powered by DPDK (kernel-bypass).
It is said that kernel-bypass technologies avoid the kernel because it is "slow", but in reality, a lot of the performance advantages that they bring just come from enforcing certain constraints.
As it turns out, many of these constraints can be enforced without bypassing the kernel. If the system is tuned just right, one can achieve performance that approaches kernel-bypass speeds, while still benefiting from the kernel's battle-tested compatibility, and rich ecosystem of tools.
ClickHouse Data Warehouse 101: The First Billion Rows, by Alexander Zaitsev a...Altinity Ltd
Columnar stores like ClickHouse enable users to pull insights from big data in seconds, but only if you set things up correctly. This talk will walk through how to implement a data warehouse that contains 1.3 billion rows using the famous NY Yellow Cab ride data. We'll start with basic data implementation including clustering and table definitions, then show how to load efficiently. Next, we'll discuss important features like dictionaries and materialized views, and how they improve query efficiency. We'll end by demonstrating typical queries to illustrate the kind of inferences you can draw rapidly from a well-designed data warehouse. It should be enough to get you started--the next billion rows is up to you!
Optimizing Kubernetes Resource Requests/Limits for Cost-Efficiency and Latenc...Henning Jacobs
Kubernetes has the concept of resource requests and limits. Pods get scheduled on the nodes based on their requests and optionally limited in how much of the resource they can consume. Understanding and optimizing resource requests/limits is crucial both for reducing resource "slack" and ensuring application performance/low-latency. This talk shows our approach to monitoring and optimizing Kubernetes resources for 80+ clusters to achieve cost-efficiency and reducing impact for latency-critical applications. All shown tools are Open Source and can be applied to most Kubernetes deployments.
Introduction to Ceph, an open-source, massively scalable distributed file system.
This document explains the architecture of Ceph and integration with OpenStack.
Load Balancing MySQL with HAProxy - SlidesSeveralnines
Agenda:
* What is HAProxy?
* SQL Load balancing for MySQL
* Failure detection using MySQL health checks
* High Availability with Keepalived and Virtual IP
* Use cases: MySQL Cluster, Galera Cluster and MySQL Replication
* Alternative methods: Database drivers with inbuilt cluster support, MySQL proxy, MaxScale, ProxySQL
Hadoop Meetup Jan 2019 - Router-Based Federation and Storage TieringErik Krogen
A presentation by CR Hota and Ekanth Sethuramalingam of Uber regarding their storage infrastructure, leveraging HDFS features like Router-Based Federation and Storage Tiering.
This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.
Seastore: Next Generation Backing Store for CephScyllaDB
Ceph is an open source distributed file system addressing file, block, and object storage use cases. Next generation storage devices require a change in strategy, so the community has been developing crimson-osd, an eventual replacement for ceph-osd intended to minimize cpu overhead and improve throughput and latency. Seastore is a new backing store for crimson-osd targeted at emerging storage technologies including persistent memory and ZNS devices.
This presentation provides an overview of the Dell PowerEdge R730xd server performance results with Red Hat Ceph Storage. It covers the advantages of using Red Hat Ceph Storage on Dell servers with their proven hardware components that provide high scalability, enhanced ROI cost benefits, and support of unstructured data.
Daniel Stenberg explains HTTP/3 and QUIC at GOTO 10, January 22, 2019. This is the slideset, see https://daniel.haxx.se/blog/2019/01/23/http-3-talk-on-video/ for the video.
HTTP/3 is the designated name for the coming next version of the protocol that is currently under development within the QUIC working group in the IETF.
HTTP/3 is designed to improve in areas where HTTP/2 still has some shortcomings, primarily by changing the transport layer. HTTP/3 is the first major protocol to step away from TCP and instead it uses QUIC.
Why the new protocols are deemed necessary, how they work, how they change how things are sent over the network and what some of the coming deployment challenges will be.
As you will see in this film, there are a lot of questions from an interested and educated audience.
Daniel Stenberg is the founder and lead developer of the curl project. He has worked on HTTP implementations for over twenty years. He has been involved in the HTTPbis working group in IETF for ten years and he worked with HTTP in Firefox for years before he left Mozilla. He participates in the QUIC working group and is the author of the widely read documents ”HTTP2 explained” and ”HTTP/3 explained”.
This is a presentation of the popular NoSQL database Apache Cassandra which was created by our team in the context of the module "Business Intelligence and Big Data Analysis".
Linux Kernel vs DPDK: HTTP Performance ShowdownScyllaDB
In this session I will use a simple HTTP benchmark to compare the performance of the Linux kernel networking stack with userspace networking powered by DPDK (kernel-bypass).
It is said that kernel-bypass technologies avoid the kernel because it is "slow", but in reality, a lot of the performance advantages that they bring just come from enforcing certain constraints.
As it turns out, many of these constraints can be enforced without bypassing the kernel. If the system is tuned just right, one can achieve performance that approaches kernel-bypass speeds, while still benefiting from the kernel's battle-tested compatibility, and rich ecosystem of tools.
ClickHouse Data Warehouse 101: The First Billion Rows, by Alexander Zaitsev a...Altinity Ltd
Columnar stores like ClickHouse enable users to pull insights from big data in seconds, but only if you set things up correctly. This talk will walk through how to implement a data warehouse that contains 1.3 billion rows using the famous NY Yellow Cab ride data. We'll start with basic data implementation including clustering and table definitions, then show how to load efficiently. Next, we'll discuss important features like dictionaries and materialized views, and how they improve query efficiency. We'll end by demonstrating typical queries to illustrate the kind of inferences you can draw rapidly from a well-designed data warehouse. It should be enough to get you started--the next billion rows is up to you!
Optimizing Kubernetes Resource Requests/Limits for Cost-Efficiency and Latenc...Henning Jacobs
Kubernetes has the concept of resource requests and limits. Pods get scheduled on the nodes based on their requests and optionally limited in how much of the resource they can consume. Understanding and optimizing resource requests/limits is crucial both for reducing resource "slack" and ensuring application performance/low-latency. This talk shows our approach to monitoring and optimizing Kubernetes resources for 80+ clusters to achieve cost-efficiency and reducing impact for latency-critical applications. All shown tools are Open Source and can be applied to most Kubernetes deployments.
Cascading is a Data Processing API, Process Planner, and Process Scheduler used for defining and executing complex, scale-free, and fault tolerant data processing workflows on an Apache Hadoop cluster.
Build a cloud native app with OpenWhiskDaniel Krook
IBM OpenWhisk presentation and demo for developerWorks TV on December 14, 2016.
https://developer.ibm.com/tv/build-a-cloud-native-app-with-apache-openwhisk/
New cloud programming models enabled by serverless architectures are emerging, allowing developers to focus more sharply on creating their applications and less on managing their infrastructure. The OpenWhisk project started by IBM provides an open source platform to enable these cloud native, event driven applications.
At this live coding event, Daniel Krook provide an overview of serverless architectures, introduce the OpenWhisk programming model, and then deploy an OpenWhisk application on IBM Bluemix, while you watch, step-by-step.
Daniel Krook, Senior Software Engineer, IBM
"The benefit of real-time data can be measured by how frequently the data in question changes, nowhere is this more apparent than threat detection. Responding to an ever changing landscape of attacks and exploits requires a system that can not only handle the scale and dynamic nature of the data but also a dynamically changing set of detection rules. We developed Confluent SIGMA, an open source project built on Kafka Streams for the open SIGMA DSL, to handle real-time rule additions and modifications. In this talk we will cover:
* The architecture of our Kafka Streams layer that makes it possible to use external data feeds as rule input
* How we handle dynamic criteria for joins and filters
* Best practices for writing dynamic rule engines in Kafka Streams
* Upcoming improvements to Kafka Streams to support versioned rules
Although Confluent SIGMA focuses on cyber threat detection this same pattern can also be applied to any DSL (domain specific language) that would benefit from real-time stream processing. After attending you will have the framework to drive dynamic rules through Kafka Streams for any use case that might require it."
Druid is an open-source analytics data store specially designed to execute OLAP queries on event data. Its speed, scalability and efficiency have made it a popular choice to power user-facing analytic applications, including multiple BI tools and dashboards. However, Druid does not provide important features requested by many of these applications, such as a SQL interface or support for complex operations such as joins. This talk presents our work on extending Druid indexing and querying capabilities using Apache Hive. In particular, our solution allows to index complex query results in Druid using Hive, query Druid data sources from Hive using SQL, and execute complex Hive queries on top of Druid data sources. We describe how we built an extension that brings benefits to both systems alike, leveraging Apache Calcite to overcome the challenge of transparently generating Druid JSON queries from the input Hive SQL queries. We conclude with a demo highlighting the performant and powerful integration of these projects.
Hagen Toennies from Gaikai Inc. presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"In this talk we will present how we enable distributed, Unix style programming using Docker and Apache Kafka. We will show how we can take the famous Unix Pipe Pattern and apply it to a Distributed Computing System. We will demonstrate the development of two simple applications with the focus on "Do One Thing and Do It Well." Afterwards we demonstrate how we make these two programs work to together using Apache Kafka. By encapsulating our applications in containers we will also show how that enables us to go from the limited resources of a development machine to cluster of computers in a data center without changing our applications or containers."
Watch the video: http://wp.me/p3RLHQ-goG
Learn more: http://www.hpcadvisorycouncil.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Using hypervisor and container technology to increase datacenter security pos...Black Duck by Synopsys
As presented by Tim Mackey, Senior Technical Evangelist - Black Duck Software, at LinuxCon/ContainerCon 2016:
Cyber threats consistently rank as a high priority for data center operators and their reliability teams. As increasingly sophisticated attacks mount, the risk associated with a zero-day attack is significant. Traditional responses include perimeter monitoring and anti-malware agents. Unfortunately, those techniques introduce performance and management challenges when used at large VM densities, and may not work well with containerized applications.
Fortunately, the Xen Project community has collaborated to create a solution which reduces the potential of success associated with rootkit attack vectors. When combined with recent advancements in processor capabilities, and secure development models for container deployment, it’s possible to both protect against and be proactively alerted to potential zero-day attacks. In this session, we’ll cover models to limit the scope of compromise should an attack be mounted against your infrastructure. Two attack vectors will be illustrated, and we’ll see how it’s possible to be proactively alerted to potential zero-day actions without requiring significant reconfiguration of your datacenter environment.
Technology elements explored include those from Black Duck, Bitdefender, Citrix, Intel and Guardicore.
Using hypervisor and container technology to increase datacenter security pos...Tim Mackey
As presented at LinuxCon/ContainerCon 2016:
Cyber threats consistently rank as a high priority for data center operators and their reliability teams. As increasingly sophisticated attacks mount, the risk associated with a zero-day attack is significant. Traditional responses include perimeter monitoring and anti-malware agents. Unfortunately, those techniques introduce performance and management challenges when used at large VM densities, and may not work well with containerized applications.
Fortunately, the Xen Project community has collaborated to create a solution which reduces the potential of success associated with rootkit attack vectors. When combined with recent advancements in processor capabilities, and secure development models for container deployment, it’s possible to both protect against and be proactively alerted to potential zero-day attacks. In this session, we’ll cover models to limit the scope of compromise should an attack be mounted against your infrastructure. Two attack vectors will be illustrated, and we’ll see how it’s possible to be proactively alerted to potential zero-day actions without requiring significant reconfiguration of your datacenter environment.
Technology elements explored include those from Black Duck, Bitdefender, Citrix, Intel and Guardicore.
Implementing a canonical IoT backend in Azure with Azure Stream AnalyticsMarco Parenzan
What do you do with all data you receive in an Event Hub, from your IoT devices? You can do telemetry, you can do analytics.There is a new tool in Azure right for this task: Azure Stream Analytics.
How it works, for what is good for.
SYN 214: Linux Virtual Desktop Capabilities, Use Cases, Architecture, and Dep...Citrix
Rejoice! You can virtualize Linux workloads with XenApp and XenDesktop. That's great news for anyone in oil, natural gas, manufacturing, scientific modeling, and chipset design.
You can find out how to unlock the power of Linux virtual desktops in this Citrix Synergy session. Follow along with the recorded session on SynergyTV and use the slides here. http://live.citrixsynergy.com/2016/player/ondemandplayer.php?presentation_id=210af262-ea29-4666-b635-ab92382259cf
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
2. What is Redis?
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● Redis – (REmote DIctionary Server)
● Open source.
● The leading in-memory database platform
● Created in 2009 by Salvatore Sanfilippo (a.k.a @antirez)
● Source: https://github.com/antirez/redis
● Website: http://redis.io
What’s Redis?
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Open source. The leading in-memory database platform,
supporting any high performance operational, analytics or
hybrid use case.
The open source home and commercial provider of Redis
Enterprise technology, platform, products & services.
Redis – (REmote DIctionary Server)
3. Data Structures - Redis’ Building Blocks
Lists
[ A → B → C → D → E ]
Hashes
{ A: “foo”, B: “bar”, C: “baz” }
Bitmaps
0011010101100111001010
Strings
"I'm a Plain Text String!”
Bit field
{23334}{112345569}{766538}
Key
Streams
{id1=time1.seq1(A:“xyz”, B:“cdf”),
d2=time2.seq2(D:“abc”, )}
Hyperloglog
00110101 11001110
Sorted Sets
{ A: 0.1, B: 0.3, C: 100 }
Sets
{ A , B , C , D , E }
Geospatial Indexes
{ A: (51.5, 0.12), B: (32.1, 34.7) }
5. Redis Streams: Connect Many Producers and Consumers
Producer 2
Producer m
Producer 1
Producer 3
Consumer 1
Consumer n
Consumer 2
Consumer 3
Streams enable asynchronous data exchange between producers and
consumers and historical range queries
12. redis.io
• Streams documentation
university.redislabs.com
• RU202 Redis Streams
• Free online course
• Earn a Certificate of Completion
YouTube: Streams overview with Salvatore
• youtube.com/watch?v=qXEyuUxQXZM
Would You Like to Know More?
Editor's Notes
* Introduce yourself
Created in 2009 by Salvatore Sanfillipo.
Everything in this talk applies equally to open source Redis and Redis Enterprise
When we say "in memory", all reads will come from memory
Data can be persisted to disk
Show of hands, who has any experience with Redis?
Who has experience using Redis as cache?
Who has experience using Redis as a data structure store / non cache applications?
Everything is about keys
Streams is a newer data type, added in Redis 5.0 about a year ago
Are a Redis data structure, built into the product
Store and share event data
Can also provide communication between processes
A stream is an append-only list of entries called messages.
The order of messages is immutable.
Messages can be read from the stream by ID or by ranges.
Consuming from the stream doesn't remove the message from it, it's still there for other consumers
Streams decouple multiple producers from multiple consumers
Useful when there is fast data production and slower consumption – as Redis can ingest very quickly
Also useful for implementing architectural patterns such as fan out – one input to multiple consumers
A good way to implement communication between microservices too
Producers add messages to a stream using XADD command
There is no explicit stream creation step, first XADD to the key creates it and starts a stream
Explain timestamp format
Explain that you can supply own ID but next message ID must always be greater than the last you added
Message bodies are a series of name/value pairs
There is no schema, message bodies can vary between messages in the same stream
Python example using a redis client library
Libraries available for pretty much all programming languages
Most libraries map each Redis command to a function call in the target language e.g. redis.xadd here
In a real case, we'd poll the temperature sensor periodically or whatever application logic needs going
As message IDs are numeric, streams naturally form a sort of time series
Range queries can be made with the XRANGE command, by supplying a start and end timestamp
Messages returned in oldest to newest order
For newest to oldest order, use XREVRANGE command
XREAD allows us to read from multiple streams at once
Designed to allow consumer to work through a stream by fetching messages whose ID is greater than the last highest ID the consumer read
XREAD can block waiting for new messages to appear in the stream
This is how we can implement a blocking pub/sub type consumer
For expensive processing tasks or greater throughput we may want a set of consumers to be able to work together to process the stream, splitting workload between them
Consumer groups allow consumers to do this by reading using the XREADGROUP command, similar syntax to XREAD
Each consumer in the group would see only a subset of the messages, but the group as a whole processes the entire stream between them
The Redis server keeps track of which consumer in a group has seen each message
Once a consumer in a group has processed a message, it uses XACK to tell Redis it was processed
But what if a consumer takes a message and fails before processing it?
As the consumer group shares the consumption of the stream amongst its members, no other consumer in the group would see this message
Redis provides commands that allow for the detection of failed consumers in a group
If a consumer fails after reading a message but before acknowledging it was successfully processed, actions can be taken to reassign the message elsewhere in the group
Note that independently other consumers can use XREAD or XRANGE as before to consume the same stream
So far it seems that streams can grow forever
Obviously, the Redis server has a finite amount of memory to store the data in
Streams can be trimmed to a specified length
Either when adding
Or by another process using XTRIM command
Length can be an exact number, or an approximation (Radix trie implementation)
Redis will compress repeating a sequence of message bodies that are the same
Because streams are just a Redis key you can also set expiry – useful for time partitioning?
Documentation is online, organized by command and topic
Redis University is free online training.
Currently 5 different courses, all apply to open source Redis
Can earn certificates of completion
Redis Streams course covers streams exclusively, uses a small amount of Python coding
Salvatore's half hour video on streams gives a longer introduction with examples in Ruby