The time series landscape evolves fast to meet the aggressive challenges in IoT. Influx 2.0 Beta was released in the first days of 2020 and although being already Top 1 time series database it introduces a revolutionary change again. InfluxDB 2 is now generally available and its key features are originate from Flux - a functional and open source 4th generation analytical programming language inspired by JavaScript. Supported in VS Code it takes a new approach towards data exploration of time series data and enables some unmatched capabilities like enrichment and filtering of time series data with external data from RDBMS.
Global azure virtual 2021 - Azure LighthouseIvo Andreev
Azure Lighthouse provides capabilities to perform cross-tenant management at scale.
We do this by providing you the ability to view and manage multiple customers from a single context.
Building a scalable business model in the cloud is a real challenge that is of uncomparable complexity compared to project-based solutions.
If you want to offer a solution in the cloud and onboard multiple customers, the next step would be to consider how would you deploy, maintain and monitor such environment. What is Azure Lighthouse and how to make your first steps following good practices is the response to that question and the main topic of our session.
Constrained Optimization with Genetic Algorithms and Project BonsaiIvo Andreev
Traditional machine learning requires volumes of labelled data that can be time consuming and expensive to produce,”
“Machine teaching leverages the human capability to decompose and explain concepts to train machine learning models
direction (teaching the correct answer is not by showing the data for it, but by using a person to show the answer).
Project Bonsai is a low code platform for intelligent solutions but with a different perspective on data it allows a completely new approach to tasks, especially when the physical world is involved. Under the hood it combines machine teaching, calibration and optimization to create intelligent control systems using simulations. The teaching curriculum is performed using a new language concept - “Inkling” and training a model is easy and interactive.
Cloud Design Patterns - Hong Kong CodeaholicsTaswar Bhatti
Talk on Cloud Design Patterns at Hong Kong Codeaholics Meetup Group. Talk includes External Config Pattern, Cache Aside, Federated Identity Pattern, Valet Key Pattern, Gatekeeper Pattern, Circuit Breaker Pattern, Retry Pattern and the Strangler Pattern. These patterns depicts common problems in designing cloud-hosted applications and design patterns that offer guidance.
A description of Azure Key Vault. Why do we need Azure Key Vault where does it fit in a solution. The details of storing keys, secrets and certificate inside of key vault. Using key vault for encryption and decryption of data
Visto il successo dello scorso anno, anche quest’anno il DotNetCampus ospita un Cert Path dedicato a chi vuole avvicinarsi al mondo delle certificazioni di prodotto e di tecnologia Microsoft. Microsoft, così come altre importanti aziende sul mercato, propone diversi percorsi di certificazione che ognuno può intraprendere, anche in autonomia, per guadagnare competenza e ottenere un riconoscimento. Una certificazione è generalmente un titolo che si ottiene dopo uno o più esami conseguiti con successo in un centro di formazione abilitato. Nel Cert Path vogliamo spiegarvi come affrontare alcuni esami di base per ottenere il titolo di MCP (Microsoft Certified Professional).
Global azure virtual 2021 - Azure LighthouseIvo Andreev
Azure Lighthouse provides capabilities to perform cross-tenant management at scale.
We do this by providing you the ability to view and manage multiple customers from a single context.
Building a scalable business model in the cloud is a real challenge that is of uncomparable complexity compared to project-based solutions.
If you want to offer a solution in the cloud and onboard multiple customers, the next step would be to consider how would you deploy, maintain and monitor such environment. What is Azure Lighthouse and how to make your first steps following good practices is the response to that question and the main topic of our session.
Constrained Optimization with Genetic Algorithms and Project BonsaiIvo Andreev
Traditional machine learning requires volumes of labelled data that can be time consuming and expensive to produce,”
“Machine teaching leverages the human capability to decompose and explain concepts to train machine learning models
direction (teaching the correct answer is not by showing the data for it, but by using a person to show the answer).
Project Bonsai is a low code platform for intelligent solutions but with a different perspective on data it allows a completely new approach to tasks, especially when the physical world is involved. Under the hood it combines machine teaching, calibration and optimization to create intelligent control systems using simulations. The teaching curriculum is performed using a new language concept - “Inkling” and training a model is easy and interactive.
Cloud Design Patterns - Hong Kong CodeaholicsTaswar Bhatti
Talk on Cloud Design Patterns at Hong Kong Codeaholics Meetup Group. Talk includes External Config Pattern, Cache Aside, Federated Identity Pattern, Valet Key Pattern, Gatekeeper Pattern, Circuit Breaker Pattern, Retry Pattern and the Strangler Pattern. These patterns depicts common problems in designing cloud-hosted applications and design patterns that offer guidance.
A description of Azure Key Vault. Why do we need Azure Key Vault where does it fit in a solution. The details of storing keys, secrets and certificate inside of key vault. Using key vault for encryption and decryption of data
Visto il successo dello scorso anno, anche quest’anno il DotNetCampus ospita un Cert Path dedicato a chi vuole avvicinarsi al mondo delle certificazioni di prodotto e di tecnologia Microsoft. Microsoft, così come altre importanti aziende sul mercato, propone diversi percorsi di certificazione che ognuno può intraprendere, anche in autonomia, per guadagnare competenza e ottenere un riconoscimento. Una certificazione è generalmente un titolo che si ottiene dopo uno o più esami conseguiti con successo in un centro di formazione abilitato. Nel Cert Path vogliamo spiegarvi come affrontare alcuni esami di base per ottenere il titolo di MCP (Microsoft Certified Professional).
Topics of this presentation:
- Fundamental concepts and principles.
- General architecture guidance.
- IoT applications component design.
- Cross-cutting issues.
This presentation by Andrii Antilikatorov (Consultant, Engineering, GlobalLogic) was delivered at GlobalLogic Kharkiv .NET TechTalk #1 on May 24, 2019.
Azure Operation Management Suite - security and complianceAsaf Nakash
Today’s IT Security and Operations teams are tasked with managing highly complex, hybrid-cloud, cross-platform systems which are increasingly vulnerable to a growing number of sophisticated cyber-attacks. With this, IT Operations teams have a requirement to identify any threats to their environment as soon as possible to mitigate damages, as well as continue to cost-effectively meet SLAs.
At Ottawa .NET User Group I had a talk on Cloud Design Patterns, External Config Pattern, Cache Aside, Federated Identity Pattern, Valet Key Pattern, Gatekeeper Pattern and the Circuit Breaker Pattern. These patterns depicts common problems in designing cloud-hosted applications and design patterns that offer guidance.
Azure Security Center provides security posture management and threat protection for your hybrid cloud workloads. Cloud Security Posture Management includes Policies, initiatives, recommendations, secure scores, and security controls. Cloud Workload Protection protects threats against servers, cloud-native workloads, databases, and storage security alerts and incidents.
Time Series Anomaly Detection with Azure and .NETTMarco Parenzan
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
In this session, you’ll learn about security on AWS and why logging in the cloud is different than on-premises. We’ll explore AWS Cloudtrail, the logging service built into AWS. We’ll discuss Amazon Cloudwatch, a monitoring service for AWS cloud resources and the applications you run on AWS. We’ll also talk about Amazon Inspector, which is the recently announced application security assessment service from AWS. We’ll examine the AWS Config service and how you can use it to improve security and resource management on AWS. Finally, we will look at how the Splunk App for AWS ties all of these services together into deep insight and useful visualizations.
Azure Operational Insights is a Cloud based machine data collection, storage, analysis service fully managed and serviced by microsoft. Operational Insights uses data from servers in your on-premise or cloud infrastructure. You can collect machine data from the following sources:
Operations Manager agents
Computers connected directly to Operational Insights
Virtual machine diagnostic data in Azure Storage services
After data is collected, it is sent to the Operational Insights service.
My presentation from the 8th meeting of Finland Azure User Group where I went through basic and intermediate concepts of Azure Active Directory for software developers.
A deep dive session on AWS Security Hub service which is the single most important service in AWS to know the security and compliance posture across AWS accounts.
Many organizations are embracing the latest practices for DevOps agility and cloud innovations to manage their heterogeneous environments (Hybrid DC). Yet they are also concerned about their ability to make appropriate and responsible decisions about how to monitor those workloads. How to monitor application and infrastructure in a centralized location?
Azure Arc is a solution that simplifies management across different hybrid clouds or multi-clouds. Azure Arc extends Azure management and security beyond the walls of Azure to other cloud platforms or on-premises environments enabling you to make use of Azure services to manage infrastructure at these environments. In this session, you will be introduced to Azure Arc, why should you use it and how to make use of it in different scenarios.
Organizations need to apply security analytics to obtain seamless visibility and monitoring across both their on-premises and cloud environments. These challenges can be solved with comprehensive detection rules and behavioral analytics to ensure you detect potential threats.
Join FireEye and AWS to learn how Threat Analytics Platform (TAP) helped unify a major U.S. financial company’s on-premises and cloud-based Security Operations Centers (SOCs) by providing a single, cloud-based solution for monitoring their hybrid IT environment. FireEye’s TAP provides seamless visibility, detection and investigation across your on-premises and AWS Cloud environments ensuring actionable insight into threats targeting your company.
Join us to learn:
• How TAP ingests and analyzes AWS CloudTrail log files, providing visibility into both your AWS environment and the applications running on it
• TAP's best practices workflow to guide and inform your threat investigation
• How a major U.S. financial company unified their on-premises and cloud-based SOCs in to a single, cloud-based security operation
Who should attend: Directors and Managers of Security, IT Administrators, IT Architects, and IT Security Engineers
Siddhi: A Second Look at Complex Event Processing ImplementationsSrinath Perera
Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing.
Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and
Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science
use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated
with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing
style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We
present a performance study that exhibits that the resulting CEP Engine—Siddhi—has significantly improved performance.
Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying
suggestions for improvements, implementing those improvements
through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.
Revolutionary container based hybrid cloud solution for MLPlatform
Ness' data science platform, NextGenML, puts the entire machine learning process: modelling, execution and deployment in the hands of data science teams.
The entire paradigm approaches collaboration around AI/ML, being implemented with full respect for best practices and commitment to innovation.
Kubernetes (onPrem) + Docker, Azure Kubernetes Cluster (AKS), Nexus, Azure Container Registry(ACR), GlusterFS
Workflow
Argo->Kubeflow
DevOps
Helm, kSonnet, Kustomize,Azure DevOps
Code Management & CI/CD
Git, TeamCity, SonarQube, Jenkins
Security
MS Active Directory, Azure VPN, Dex (K8s) integrated with GitLab
Machine Learning
TensorFlow (model training, boarding, serving), Keras, Seldon
Storage (Azure)
Storage Gen1 & Gen2, Data Lake, File Storage
ETL (Azure)
Databricks, Spark on K8, Data Factory (ADF), HDInsight (Kafka and Spark), Service Bus (ASB)
Lambda functions & VMs, Cache for Redis
Monitoring and Logging
Graphana, Prometeus, GrayLog
Topics of this presentation:
- Fundamental concepts and principles.
- General architecture guidance.
- IoT applications component design.
- Cross-cutting issues.
This presentation by Andrii Antilikatorov (Consultant, Engineering, GlobalLogic) was delivered at GlobalLogic Kharkiv .NET TechTalk #1 on May 24, 2019.
Azure Operation Management Suite - security and complianceAsaf Nakash
Today’s IT Security and Operations teams are tasked with managing highly complex, hybrid-cloud, cross-platform systems which are increasingly vulnerable to a growing number of sophisticated cyber-attacks. With this, IT Operations teams have a requirement to identify any threats to their environment as soon as possible to mitigate damages, as well as continue to cost-effectively meet SLAs.
At Ottawa .NET User Group I had a talk on Cloud Design Patterns, External Config Pattern, Cache Aside, Federated Identity Pattern, Valet Key Pattern, Gatekeeper Pattern and the Circuit Breaker Pattern. These patterns depicts common problems in designing cloud-hosted applications and design patterns that offer guidance.
Azure Security Center provides security posture management and threat protection for your hybrid cloud workloads. Cloud Security Posture Management includes Policies, initiatives, recommendations, secure scores, and security controls. Cloud Workload Protection protects threats against servers, cloud-native workloads, databases, and storage security alerts and incidents.
Time Series Anomaly Detection with Azure and .NETTMarco Parenzan
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
In this session, you’ll learn about security on AWS and why logging in the cloud is different than on-premises. We’ll explore AWS Cloudtrail, the logging service built into AWS. We’ll discuss Amazon Cloudwatch, a monitoring service for AWS cloud resources and the applications you run on AWS. We’ll also talk about Amazon Inspector, which is the recently announced application security assessment service from AWS. We’ll examine the AWS Config service and how you can use it to improve security and resource management on AWS. Finally, we will look at how the Splunk App for AWS ties all of these services together into deep insight and useful visualizations.
Azure Operational Insights is a Cloud based machine data collection, storage, analysis service fully managed and serviced by microsoft. Operational Insights uses data from servers in your on-premise or cloud infrastructure. You can collect machine data from the following sources:
Operations Manager agents
Computers connected directly to Operational Insights
Virtual machine diagnostic data in Azure Storage services
After data is collected, it is sent to the Operational Insights service.
My presentation from the 8th meeting of Finland Azure User Group where I went through basic and intermediate concepts of Azure Active Directory for software developers.
A deep dive session on AWS Security Hub service which is the single most important service in AWS to know the security and compliance posture across AWS accounts.
Many organizations are embracing the latest practices for DevOps agility and cloud innovations to manage their heterogeneous environments (Hybrid DC). Yet they are also concerned about their ability to make appropriate and responsible decisions about how to monitor those workloads. How to monitor application and infrastructure in a centralized location?
Azure Arc is a solution that simplifies management across different hybrid clouds or multi-clouds. Azure Arc extends Azure management and security beyond the walls of Azure to other cloud platforms or on-premises environments enabling you to make use of Azure services to manage infrastructure at these environments. In this session, you will be introduced to Azure Arc, why should you use it and how to make use of it in different scenarios.
Organizations need to apply security analytics to obtain seamless visibility and monitoring across both their on-premises and cloud environments. These challenges can be solved with comprehensive detection rules and behavioral analytics to ensure you detect potential threats.
Join FireEye and AWS to learn how Threat Analytics Platform (TAP) helped unify a major U.S. financial company’s on-premises and cloud-based Security Operations Centers (SOCs) by providing a single, cloud-based solution for monitoring their hybrid IT environment. FireEye’s TAP provides seamless visibility, detection and investigation across your on-premises and AWS Cloud environments ensuring actionable insight into threats targeting your company.
Join us to learn:
• How TAP ingests and analyzes AWS CloudTrail log files, providing visibility into both your AWS environment and the applications running on it
• TAP's best practices workflow to guide and inform your threat investigation
• How a major U.S. financial company unified their on-premises and cloud-based SOCs in to a single, cloud-based security operation
Who should attend: Directors and Managers of Security, IT Administrators, IT Architects, and IT Security Engineers
Siddhi: A Second Look at Complex Event Processing ImplementationsSrinath Perera
Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing.
Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and
Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science
use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated
with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing
style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We
present a performance study that exhibits that the resulting CEP Engine—Siddhi—has significantly improved performance.
Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying
suggestions for improvements, implementing those improvements
through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.
Revolutionary container based hybrid cloud solution for MLPlatform
Ness' data science platform, NextGenML, puts the entire machine learning process: modelling, execution and deployment in the hands of data science teams.
The entire paradigm approaches collaboration around AI/ML, being implemented with full respect for best practices and commitment to innovation.
Kubernetes (onPrem) + Docker, Azure Kubernetes Cluster (AKS), Nexus, Azure Container Registry(ACR), GlusterFS
Workflow
Argo->Kubeflow
DevOps
Helm, kSonnet, Kustomize,Azure DevOps
Code Management & CI/CD
Git, TeamCity, SonarQube, Jenkins
Security
MS Active Directory, Azure VPN, Dex (K8s) integrated with GitLab
Machine Learning
TensorFlow (model training, boarding, serving), Keras, Seldon
Storage (Azure)
Storage Gen1 & Gen2, Data Lake, File Storage
ETL (Azure)
Databricks, Spark on K8, Data Factory (ADF), HDInsight (Kafka and Spark), Service Bus (ASB)
Lambda functions & VMs, Cache for Redis
Monitoring and Logging
Graphana, Prometeus, GrayLog
Cisco Virtualized Multi-tenant Data Center solution (VMDC) is an architectural approach to IT which delivers a Cloud Ready Infrastructure. The architecture encompasses multiple systems and functions defining a standard framework for an IT organization. Standardization allows the organization to achieve operational efficiencies, reduce risk and achieve cost reductions while offering a consistent platform for business.
Managing and Deploying High Performance Computing Clusters using Windows HPC ...Saptak Sen
The new management features built into Windows HPC Server 2008 R2 are the foundation for deploying and managing HPC clusters of scale up to 1000 nodes. Join us for a deep dive in monitoring and diagnostic tools, a review of the updated heat-map and template-based deployment. We also cover the new PowerShell-based scripting capabilities: the basics of management shell, as well as the underlying design and key concepts, new Reporting Capabilities, and a discussion on network boot.
Safer Commutes & Streaming Data | George Padavick, Ohio Department of Transpo...HostedbyConfluent
The Ohio Department of Transportation has adopted Confluent as the event driven enabler of DriveOhio, a modern Intelligent Transportation System. DriveOhio digitally links sensors, cameras, speed monitoring equipment, and smart highway assets in real time, to dynamically adjust the surface road network to maximize the safety and efficiency for travelers. Over the past 24 months the team has increased the number and types of devices within the DriveOhio environment, while also working to see their vendors adopt Kafka to better participate in data sharing.
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
Let’s discover with a step-by-step approach the entire ecosystem of features driven by Azure Data eXplorer. Let’s have many examples using Kusto dialect, in order to acquire data, process and build up complete web interfaces using only one service: ADX. Using IoT Asset monitoring as Functional Context, we’ll make a full example, using Azure Data Studio, SQL Server, ADLS managed by ADX infrastructure.
Scaling Security on 100s of Millions of Mobile Devices Using Apache Kafka® an...confluent
Watch this talk here: https://www.confluent.io/online-talks/scaling-security-on-100s-of-millions-of-mobile-devices-using-kafka-and-scylla-on-demand
Join mobile cybersecurity leader Lookout as they talk through their data ingestion journey.
Lookout enables enterprises to protect their data by evaluating threats and risks at post-perimeter endpoint devices and providing access to corporate data after conditional security scans. Their continuous assessment of device health creates a massive amount of telemetry data, forcing new approaches to data ingestion. Learn how Lookout changed its approach in order to grow from 1.5 million devices to 100 million devices and beyond, by implementing Confluent Platform and switching to Scylla.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
Machbase Neo is an innovative iot data processing solution that integrates various features into an #all_in_one timeseries database.
In the past, development organizations had to invest a lot of time and resources to build a single service or solution. Moreover, they had to navigate complex and challenging processes for data collection and processing. But now, with the introduction of Machbase Neo, these problems have been solved. You can now set up everything using just one Machbase Neo server, allowing developers to focus on their core tasks. This product can save developers over 90% of their time by eliminating unnecessary tasks.
Distributed Database Design Decisions to Support High Performance Event Strea...StreamNative
Event streaming architectures launched a reexamination of applications and systems architectures across the board. We live in a world where answers are needed now in a constant real-time flow. Yet beyond the event streaming system itself, what are the corequisites to ensure our large scale distributed database systems can keep pace with this always-on, always-current real time flow of data? What are the requirements and expectations for this next tech cycle?
Second edition of this popular interactive workshop, this time we focussed on the new “Windows Azure Accelerator for Umbraco” CodePlex project.
Topics
Web & Worker Role
Virtual Machine sizes & performance
Storage Types: Blobs, Tables, Azure SQL, queues
No local persistant storage
Network Load Balancing (round robin)
Scale out to multiple instances
Multiples websites in one Azure account
Azure Content Delivery Network
Swap between development & production environments
Typical monthly costs to host Umbraco site
Q&A
Advanced Open IoT Platform for Prevention and Early Detection of Forest FiresIvo Andreev
The session was about open architecture using IoT Edge, Azure Cognitive Services, Mosquitto MQTT, Influx DB and GraphQL web services to develop advanced architecture for early detection of forest fires that integrates sensor networks and mobile (drone) technologies for data collection and processing. Unmanned air vehicles (UAVs) will allow coverage of larger areas to raise the percentage of forest fires detections, monitor areas with high fire weather index and such already affected by forest fires. All information is forwarded and stored in cloud computing platform where near real-time processing and alerting is performed.
Cybersecurity and Generative AI - for Good and Bad vol.2Ivo Andreev
The presentation is an extended in-depth version review of cybersecurity challenges with generative AI, enriched with multiple demos, analysis, responsible AI topics and mitigation steps, also covering a broader scope beyond OpenAI service.
Popularity, demand and ease of access to modern generative AI technologies reveal new challenges in the cybersecurity landscape that vary from protecting confidentiality and integrity of data to misuse and abuse of technology by malicious actors. In this session we elaborate about monitoring and auditing, managing ethical implications and resolving common problems like prompt injections, jailbreaks, utilization in cyberattacks or generating insecure code.
Architecting AI Solutions in Azure for BusinessIvo Andreev
The topic is about Azure solution architectures that involve IoT and AI to solve common business domain problems. With near real time recommender system and an object detection with image recognition we review the architecture, build from the ground-up and illustrate how the typical realistic challenges could be addressed.
Cybersecurity Challenges with Generative AI - for Good and BadIvo Andreev
The presentation is an extended in-depth version review of cybersecurity challenges with generative AI, enriched with multiple demos, analysis, responsible AI topics and mitigation steps, also covering a broader scope beyond OpenAI service.
Popularity, demand and ease of access to modern generative AI technologies reveal new challenges in the cybersecurity landscape that vary from protecting confidentiality and integrity of data to misuse and abuse of technology by malicious actors. In this session we elaborate about monitoring and auditing, managing ethical implications and resolving common problems like prompt injections, jailbreaks, utilization in cyberattacks or generating insecure code.
JS-Experts - Cybersecurity for Generative AIIvo Andreev
Popularity, demand and ease of access to modern generative AI technologies reveal new challenges in the cybersecurity landscape that vary from protecting confidentiality and integrity of data to misuse and abuse of technology by malicious actors. In this session we elaborate about monitoring and auditing, managing ethical implications and resolving common problems like prompt injections, jailbreaks, utilization in cyberattacks or generating insecure code.
This is a totally different perspective of LLMs
How do OpenAI GPT Models Work - Misconceptions and Tips for DevelopersIvo Andreev
Have you ever wondered why GPT models work? Do you ask questions like:
◉ How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? ◉ Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? ◉ How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Key Terms; ChatGPT Enterprise; Top Questions; Enterprise Data; Azure Search; Functions; Embeddings; Context Encoding; General Intelligence; Emerging Abilities; Chain of Thought; Plugins; Multimodal with DALL-E; Project Florence
OpenAI GPT in Depth - Questions and MisconceptionsIvo Andreev
OpenAI GPT in depth – misconceptions and questions you would like answered
Have you ever wondered why GPT models work? Do you ask questions like:
How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Cutting Edge Computer Vision for EveryoneIvo Andreev
Microsoft offers a wide range of tools and advanced solutions to support you in managing computer vision related tasks.
From purely coding approaches with ML.NET, through zero-code ComputerVision.ai to advanced and flexible AI service in Azure ML, there is a solution for every need and each type of person.
From running on premises, through managed infrastructure to completely cloud services the speed of getting to the desired results and the return of investment are guaranteed.
Join this session to get insights about the options, deployment, pricing, pros and cons compared and select the most appropriate tech for your business case.
Collecting and Analysing Spaceborn DataIvo Andreev
Communicating with space and analysing satellite data
Azure reached beyond the clouds and bring space-born satellite data to your subscription for analysis and discovering insights.
Satellite as a service, Azure Orbital and a whole new ecosystem signal the ambition to push the limits and explore new opportunities.
In this session we are talking about geospatial AI-based analysis and a comprehensive flow that will allow you touch a vector of increasing importance for extending the cloud and helping businesses make tactical decisions.
Collecting and Analysing Satellite Data with Azure OrbitalIvo Andreev
Azure reached beyond the clouds and bring space-born satellite data to your subscription for analysis and discovering insights.
Satellite as a service, Azure Orbital and a whole new ecosystem signal the ambition to push the limits and explore new opportunities.
In this session we are talking about geospatial AI-based analysis and a comprehensive flow that will allow you touch a vector of increasing importance for extending the cloud and helping businesses make tactical decisions.
Azure Orbital - a fully managed cloud-based ground station as a service that enables you to communicate with your spacecrafts or satellites and generate products for customers.
AZ orbital handles machine-machine communication for the user based on the schedule and TLE location of satellites.
Azure software modules decrypt satellite data and prepare for usage.
Since Nov 2021 AZ cognitive for language is having a fresh tool – the Language Studio which is now in Preview. The studio offers multiple prebuilt and preconfigured models which allow you to quickly implement, test and deploy tasks like understanding conversational language, extracting information, classifying text or answering questions. But it goes further and offers multiple features to create, train and deploy custom models that model your data and serves your needs best. Language Studio does that by utilizing workflows that let developers build models without the need of ML knowledge and deploy the results as handy APIs.
Cosmos DB is among the top databases, with its strengths being in a flexible, extremely scalable hosted model, high SLA, low latency, globally distributed, automatic indexing, 2-dimensional redundancy and granular access level. But how does it suit IoT scenarios and for what scenarios is it appropriate?
Forecasting time series powerful and simpleIvo Andreev
Time series are a sequence of data points positioned in order of time. Time series forecasting has two main purposes - to understand the mechanisms that lead to rise or fall, and to predict future values. Very often it analyses trends, cyclical events, seasonality and has unique importance in Economics and Business. The quality of predictions can be evaluated only in future due to temporal dependencies on previous data points and there are many model types for approximation. In this session we are going to talk about challenges, ways of improvement and technology stack like ML.NET, ARIMA, Python, Azure ML, Regression and FB Prophet
Azure security guidelines for developers Ivo Andreev
Azure security baselines and benchmarks, Security Maturity Model, Industrial Internet Consortium IIC , Certification, Web Application Firewall, API Management Service
Autonomous Machines with Project BonsaiIvo Andreev
Autonomous machines rely on fusion of many technologies to sense, plan, optimize and act as if an intelligent superhuman is in control.
Project Bonsai is a machine teaching service that combines machine learning (ML), calibration and optimization to create intelligent control systems using simulations. The teaching curriculum is performed using a proprietary “Inkling” language close to JavaScript and training a model is easy and interactive. Join this session for a Bonsai jump start and a demo and try it yourself – it is free.
Azure architecture design patterns - proven solutions to common challengesIvo Andreev
Building a reliable, scalable, secure applications could happen either following verified design patterns or the hard way - following the trial and error approach. Azure architecture patterns are a tested and accepted solutions of common challenges thus reducing the technical risk to the project by not having to employ a new and untested design. However, most of the patterns are relevant to any distributed system, whether hosted on Azure or on other cloud platforms.
Industrial IoT from the Ground up with Azure and Open Source
IIoT leverages the power of machines and realtime analytics to pick up on industrial inefficiencies and problems sooner, and save time and money in addition to supporting BI efforts. In a myriad of reference architectures it is up to experience and trial-error to find out what really works in a real life scenario.
We will review the challenges and solutions in building an IIoT platform from the ground up on the edge between Azure and open source in order to have the best from both worlds. Technical focus will be on IoT Edge, TS Insights, Stream Analytics, IoT Hub, App Insights, Event Grid, Service Bus, ARM templates, Influx DB, Grafana and more - all neatly glued together by Azure Functions.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Flying a Drone with JavaScript and Computer VisionIvo Andreev
Almost anything that used to run on desktop, now runs in the browser and as of Atwood's law: anything that could be written in JavaScript, will eventually be written in JavaScript.
If you have dared imagining to control your toys with code, communicate with the cloud and use advanced computer intelligence, your dreams have now become close at hand.
This session is to challenge your fantasy and make you think what you could do with JavaScript. This session is about programming drones with JavaScript and AI capabilities.
For business users, always using AI is about easy access to the tools without writing any code. This session is not about learning how to do AI but how to make AI usable and add value.
AI powered visuals such as Key Influencer in Power BI desktop to analyse the data without deep knoledge of the machine learning concepts.
Machine Learning is approaching a peak of inflated expectations, although we see AI daily and in all contexts. Media pressure is high, governments are overly optimistic, plenty of ventures are putting money in unviable ideas or some brilliant engineers fail to reach business users.
But Microsoft bring all of this under the same roof and unleash the power of AI by integrating Power BI ecosystem with Azure ML and Cognitive services. The result is as simple and effective as great technology at end-user's hand.
Industrial IoT with Azure and Open SourceIvo Andreev
IIoT leverages the power of machines and realtime analytics to pick up on industrial inefficiencies and problems sooner, and save time and money in addition to supporting BI efforts. In a myriad of reference architectures it is up to experience and trial-error to find out what really works in a real life scenario..
We will review the challenges and solutions in building an IIoT platform from the ground up on the edge between Azure and open source in order to have the best from both worlds. Technical focus will be on IoT Edge, TS Insights, Stream Analytics, IoT Hub, App Insights, Event Grid, Service Bus, ARM templates, Influx DB, Grafana and more - all neatly glued together by Azure Functions.
Understanding Nidhi Software Pricing: A Quick Guide 🌟
Choosing the right software is vital for Nidhi companies to streamline operations. Our latest presentation covers Nidhi software pricing, key factors, costs, and negotiation tips.
📊 What You’ll Learn:
Key factors influencing Nidhi software price
Understanding the true cost beyond the initial price
Tips for negotiating the best deal
Affordable and customizable pricing options with Vector Nidhi Software
🔗 Learn more at: www.vectornidhisoftware.com/software-for-nidhi-company/
#NidhiSoftwarePrice #NidhiSoftware #VectorNidhi
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
✅Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
✅First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
✅Publish Automated Posts and Pages using AI Genie directly on Your website
✅50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
✅Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
✅30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, 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.
GraphSummit Paris - The art of the possible with Graph Technology
Flux QL - Nexgen Management of Time Series Inspired by JS
1. Nov 20-21, 2020Sofia
var title = “FluxQL - NextGen Management of Time
Series Inspired by JS”;
var info = {
name: “Ivelin Andreev”,
otherOptional: “Functional data scripting language designed for
querying, analyzing and acting on data. ”
};
2. Nov 20-21, 2020
Thanks to our Sponsors:
General Sponsor:
Trusted Sponsor:
Innovation Sponsor:
Silver Sponsor:
Gold Sponsors:
Platinum Sponsors:
Bronze Sponsors:
Technology Partners:
3. Nov 20-21, 2020
About me
• Microsoft Azure MVP
• Software Architect @
o 18+ years professional experience
• CTO @
• External Expert Horizon 2020, Eurostars-Eureka
• External Expert InnoFund Denmark, RIF Cyprus
• Business Interests
o Web Development, SOA, Integration
o IoT, Machine Learning, Computer Intelligence
o Security & Performance Optimization
• Contact
ivelin.andreev@icb.bg
www.linkedin.com/in/ivelin
www.slideshare.net/ivoandreev
4. Nov 20-21, 2020
agenda();
AGENDA
Intro to TimeSeries
What are Flux & InfluxDB v2
Deployment and Configuration
What’s New & Cool
InfluxDB with SQL and Azure
Demo
from(bucket: v.bucket)
|> range(start: v.timeRangeStart)
|> filter(fn: (r) => r._measurement == "diskio")
|> filter(fn: (r) => r._field == "read_bytes" or r._field == "write_bytes")
|> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)
|> derivative(unit: 1s,nonNegative: false)
|> yield(name: "derivative")
5. Nov 20-21, 2020
• TimeSeries DB Requirements (Credits: Baron Schwartz)
o https://www.xaprb.com/blog/2014/06/08/time-series-database-requirements/
• InfluxDB Clustering & Flux Specification
o https://www.influxdata.com/blog/influxdb-clustering/
o https://github.com/influxdata/influxdb/issues
o https://github.com/influxdata/flux
o https://github.com/influxdata/flux/blob/master/docs/SPEC.md
• What’s New & InfluxQL vs Flux
o https://docs.influxdata.com/influxdb/v2.0/api/
o https://docs.influxdata.com/influxdb/v1.8/flux/flux-vs-influxql/
• Templates Gallery
o https://www.influxdata.com/products/influxdb-templates/gallery/
• InfluxDB Client Libraries
o https://docs.influxdata.com/influxdb/v2.0/tools/client-libraries/
o https://github.com/influxdata/influxdb-client-csharp (InfluxDB 1.8+, InfluxDB 2.0)
o https://github.com/influxdata/influxdb-csharp (InfluxDB 1.7 and earlier)
• InfluxDB YouTube Videos (150 videos for the last 1y)
o https://www.youtube.com/channel/UCnrgOD6G0y0_rcubQuICpTQ/videos /
Takeaways & References
7. Nov 20-21, 2020
DB optimized for storing and monitoring time-stamped data –
events tracked, monitored and aggregated over time.
• The fastest growing DB category (24M)
• What makes TS DB different?
Compression (1:1000 - 1:5000 compared to RDBMS)
Continuous queries, downsampling
Writes
95%-99% of all operations
Streaming live data from multiple devices
Typically sequential appends
Updates to modify values are rare
Deletes are bulk on large ranges (days, months, years)
Aggregation
Performance issues are typically I/O bound
Caching does not work well for BigData
Queries
Typically sequential
What is a Time Series DB?
8. Nov 20-21, 2020
• Popularity distribution (Sept 2020)
• RDBMS still hold popularity (74%)
• Document (9.3%), Key-value (5.2%), Search (4.5%)
• Emerging Workloads
• More devices, more monitoring, more data points
• Scenarios
• Real-time Performance Monitoring, Analytics, Alerts
• Internet of Things (sensors, events)
• Trade transactions, Engineering
• Scientific computing (earthquake, rainfall, weather)
• Predictive analytics and ML to help predict future outcomes
• TimeSeries Data Lake
• Efficient ingestion (x1M points/sec)
• Spark or Python processing through SDKs
Scenarios to Consider Time Series
9. Nov 20-21, 2020
• Purpose-built TS DB (not repurposed - MongoDB)
• Part of Influx Data Platform Telegraf, InfluxDB, Chronograf and Kapacitor (TICK)
• Comprehensive platform (collection, storage, visualization, alerting)
• Variable compression
depending required level of precision
• Variable time precision
sec, ms, µs or ns precision
• InfluxQL (-v1.8), Flux (v1.8-v2.0)
• Multiple data types, No tag limits
What is InfluxDB?
11. Nov 20-21, 2020
What’s New (since 08.01.2020)
• Production Ready -> GA from 2020-11-10 <-
• I,C,K from TICK stack in single binary (Telegraf lives own life)
• Full power FluxQL (also Influx 1.8+)
• Templates Gallery (30+ pre-made monitoring solutions – Google cloud, Kafka, Docker, CoViD-19)
• Influx Transpile CLI(converts InfluxQL to FluxQL)
https://docs.influxdata.com/influxdb/v1.8/flux/flux-vs-influxql/#influxql-and-flux-parity
• Flux (beta) Grafana Datasource Plugin
• Shareable dashboards, alerts and queries
• API allows everything to be programmatically controlled
What’s Bad
https://github.com/influxdata/influxdb/issues/18088
https://www.reddit.com/r/influxdb/comments/dgo5w3/is_it_expected_for_flux_queries_to_be/
What’s new in InfluxDB 2.0 GA
12. Nov 20-21, 2020
• 4th gen programming language (since July 2018)
• Why a new Language?
• Features
• Highly Readable – piping, instead of nesting, clear data origin
• Useable – aids productivity, shorter code to express the same
• Readable – derived from something common (JS)
• Testable – Flux functions can be tested in isolation from the outside world(unlike SQL)
• Shareable – community defines functions, creates libraries
• Decouples query engine from storage tier
Why the Trouble of a New Language?
d1=from(bucket: "industry4sme")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "telemetry")
|> filter(fn: (r) => r["SensorID"] == "M186")
|> filter(fn: (r) => r["Type"] == "S1load")
13. Nov 20-21, 2020
• Joins – from any bucket, measurement and on any columns
• Math across measurements - run calculations using data from separate measurements
• Sort on tags – Order by time only was supported in InfluxQL
• Group by any column – InfluxQL allows grouping on tags and time only
• Multiple datasources – Flux query data from datasources like CSV, SQL and BigTable packages
• Custom functions – define custom auxiliary functions
• Datepart queries –only data within a specified hour range (i.e. work hours), for a large period
• Pivot - pivot data tables by specifying rowKey, columnKey
• Histograms - generate a cumulative histogram in buckets [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
• Covariance - covariance() function calculates the covariance between two columns
Flux vs InfluxQL
15. Nov 20-21, 2020
Enterprise version allows clustering
Rule: A cluster shall have 3 meta nodes
with an even number of data nodes.
Meta nodes
3 = magic number, consensus quorum
Expose HTTP API
Add/remove servers; Move shards around the cluster
Each meta communicates with all other
Low resource requirements
Data nodes
Hold actual TS data – tag keys/values, field keys/values
Handle writes and queries; Replicate data
Meta-Data Communicate
InfluxDB Enterprise Architecture
16. Nov 20-21, 2020
InfluxDB Enterprise (v.1.8) available on Azure Marketplace
Note: v.1.x is dramatically different from v.2.0
Highlights
• Easy and straightforward installation
• Billing in Azure subscription
• Open source ARM templates (GitHub)
• Multi-node environment with load balancing
• Pricing
• Scalesets (2 Data (2vCPU, 7GiB), 3 Meta (1vCPU, 3.5 GiB)) ~ $ 250
• Networking, IP, Load Balancer, Premium SSD ~ $ 50
• $0.64/core/hr (recommended 2 data nodes, 2 vCPU) ~ $ 1868
InfluxDB Enterprise Cluster on Azure (v1.8)
17. Nov 20-21, 2020
$ wget https://dl.influxdata.com/influxdb/releases/influxdb_2.0.0-beta.16_linux_amd64.tar.gz
$ tar xvzf influxdb_2.0.0-beta.16_linux_amd64.tar.gz
• MacOS, Linux, Docker…No Windows? (see Docker)
• Create a VM (i.e. Standard B2s (2 vcpus, 4 GiB memory, Standard SSD))
• Connect with SSH client of choice (i.e. Putty)
• Download and Unzip
• Setup as Service
$ sudo cp influxdb_2.0.0-beta.16_linux_amd64/{influx,influxd}
/usr/local/bin/
$ sudo useradd -rs /bin/false influxdb
$ sudo mkdir /home/influxdb
$ sudo chown influxdb /home/influxdb
$ sudo vi /lib/systemd/system/influxdb2.service
(Add the InfluxDB service file content)
$ sudo systemctl enable influxdb2
$ sudo systemctl start influxdb2
$ sudo systemctl status influxdb2
InfluxDB service file
[Unit]
Description=InfluxDB 2.0 service file.
After=network-online.target
[Service]
User=influxdb
Group=influxdb
ExecStart=/usr/local/bin/influxd
Restart=on-failure
[Install]
WantedBy=multi-user.target
InfluxDB OSS 2.0 Installation (Azure VM)
18. Nov 20-21, 2020
• Configure
Add Firewall rule (allow incoming TCP port 9999)
Option 1(GUI)
Open http://[IPAddress]:9999
Option 2(CLI)
influx setup
Configure InfluxDB OSS 2.0
19. Nov 20-21, 2020
Register Free at cloud2.influxdata.com
Highlights
• Located in Amsterdam (NL) and Virginia (US)
• Free plan retention 30 days
• Transparent predictable pricing (Usage Based)
• Not priced on underlying resource
• Not priced on query/seconds
Cloud Pricing Price Sample Usage Subtotal
Data In $0.002/MB 1000 MB $ 2.00
Queries $0.01/100 qry 100,000 queries $ 10.00
Storage $0.002/GB-hr 2 GB $ 2.88
Data Out $0.09/GB 1 GB $ 0.09
Total $ 14.97
InfluxDB Cloud on Azure (beta)
21. Nov 20-21, 2020
• Data Input
• Telegraf – 200 plugin datasources (https://docs.influxdata.com/telegraf/v1.14/plugins/plugin-list/)
• Custom Data Source – build a plugin using Telegraf open source
• FluentD – 700 plugins
• Azure IoT Hub - Telegraf plugin (IoT Hub, Event Hub, AMQP, MQTT, HTTPS)
• Azure Storage Queues – Telegraf plugin
• SQL Server and Windows Server – monitoring templates
• Flux SQL package – enrich telemetry from RDBMS (i.e. MSSQL)
(Amazon Athena, Google BigQuery, MS SQL, MySQL, PostgreSQL, Snowflake, SQLite)
InfluxDB v2 Integration
• Data Output
• Azure Application Insights
• Azure Monitor – can store date in InfluxDB indefinitely
• Node-Red
22. Nov 20-21, 2020
• Flux editor on Monaco editor (VS Code)
• IntelliSense, Validation, Diff editor, Syntax highlighting
• Tasks (Continuous Queries)
• Scheduled Flux query runs periodically and stores results
• 300+ built-in functions
• Quantiles / Percentiles
• Windowing and data aggregation
• SQL data enrichment, join with SQL data
• TS forecasting (Holt-Winters): not random, trend, seasonality
• Geotracking (beta) – geospatial filters require Lat/Lon fields
• Python Client + Influx Pandas DF + Python 3.6 + Analytics
• Anomaly detection (MAD, BIRCH)
Flux Built-in Analytics
23. Nov 20-21, 2020
• Client SDKs integrate with the InfluxDB v2 API
• Go, C#, Java, PHP, Ruby, Scala, JavaScript, Python
• Nuget package (.NET CLI)
• Client SDK Features
• Query data with Flux
• Write data
• Delete data
• Management APIs (Setup, Authorization, Buckets, Organizations, Users, Sources, Tasks)
• Monitor Data
• Check = Query + Configuration (Schedule, Delay, Message, Type)
• Types: Deadman check, Threshold check
• Notification Endpoints
• MS Teams, Slack, Telegram, Webhooks
Acting on Data
24. Nov 20-21, 2020
The sql.from() function retrieves data from a SQL data source
Parameters
• driverName – driver to connect to source
• dataSourceName – connection string; driver-specific format
• query – query string to run against source
Secrets
• Import influxdata/influxd/secrets package
• BoltDB
• Embedded simple key-value store in Go
• Base64-encoded
• Vault
• Stores and controls tokens, passwords, certificates
• Requires a dedicated Vault server
• Default for InfluxDB cloud
import "sql"
import "influxdata/influxdb/secrets"
username = secrets.get(key: "SQLSERVER_USER")
password = secrets.get(key: "SQLSERVER_PASS")
sql.from(
driverName: "sqlserver",
dataSourceName:
"sqlserver://${username}:${password}@dbServer:port?
database=examplebdb",
query: "SELECT * FROM Example.Table"
)
Flux sql.from() function
26. Nov 20-21, 2020
Export from InfluxDB 1.x
• Export to Influx line protocol
influx_inspect export -datadir “[influxRoot]data" -waldir "[influxRoot]meta" -out “[outFolder]"
-database [databaseName] -retention autogen -start "2020-10-01T00:00:00Z“
Note: Edit file and remove DDL statements (CREATE DATABASE [databaseName] WITH NAME autogen)
Import in InfluxDB 2
• Option 1 (GUI)
• Open InfluxDB home (http://[IPAddress]:9999/)
• Open Data Tab
• Find TS Data bucket
• 1.1 Line Protocol
• Drag file for import
• 1.2 Client Library
• Open IDE
• Reuse sample code
• Option 2 (CLI)
• Connect remotely (i.e. Putty)
• Upload files to VM over SFTP (i.e. WinSCP)
• Bucket must exist prior to import
influx write -b [bucket] -o [orgName] -p ns --
format=lp -f [filePath/filename] -t [token]
Migration to Flux
27. Nov 20-21, 2020
Open Explore Tab
1. Select Time Interval !!! Important !!!
2. Select Bucket filter
3. Select Measurement filter
4. Select Tag filter
5. Select Aggregation function and Window
➀
➁
➂ ➃
➄
Other Options
• Select Chart type
• Set Refresh interval
• View FluxQL script
• Export CSV
• View Raw data
• Save As dashboard cell
Data Exploration
29. Nov 20-21, 2020
Join
• Join 2 data streams in a single table - InfluxDB query results with SQLServer query results
• Both tables must have all columns specified in this list.
• Columns renamed if having the same name i.e. Type_metric
Mapped table
• Build a new table, mapping existing columns to new ones.
• Aggregate the rows, grouping by interval and using Max as aggregation function
join(tables: {metric: sensordata, info: machineData}, on: ["SensorID"])
|> map(fn: (r) => ({
SensorID: r.SensorID,
Name: r.Name,
_value: r._value,
_time: r._time
})
)
|> aggregateWindow(every: 10m, fn: max)
Flux join()
30. Nov 20-21, 2020
cov()
• Computes the covariance between two streams by first joining the streams
Covariance
• Measures the extent of change in one variable compared to other (-∞;+∞)
• High covariance - strong relationship, low covariance - weak relationship
Pearsonr()
• Computes the Pearson R correlation coefficient between two streams by first joining the streams
Correlation
• How strongly two variables are related, scaled covariance [-1;+1] , 0.0-0.4 – low, 0.4-0.7 – moderate, 0.7-1.0 -
high
d1=from(bucket: "industry4sme")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "telemetry")
|> filter(fn: (r) => r["SensorID"] == "M186")
|> filter(fn: (r) => r["Type"] == "S1load")
d2=from(bucket: "industry4sme")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "telemetry")
|> filter(fn: (r) => r["SensorID"] == "M186")
|> filter(fn: (r) => r["Type"] == "S2load")
pearsonr(x: d2, y: d1, on:["_time", "SensorID"])
cov(x: d2, y: d1, on:["_time", "SensorID"])
Flux cov(), pearsonr()
31. Nov 20-21, 2020
Telegraf - open source data collector agent
• Download
• Generate Token
• Export Token
• Start Telegraf
Alerts
• Define query
• Configure check (Notification email – SendGrid API with API Key)
wget https://dl.influxdata.com/telegraf/releases/telegraf_1.15.3-1_amd64.deb
sudo dpkg -i telegraf_1.15.3-1_amd64.deb
export INFLUX_TOKEN=<INFLUX_TOKEN>
telegraf --config http://13.69.61.108:9999/api/v2/telegrafs/066bdefe903de000
Alerts
32. Nov 20-21, 2020
Thanks to our Sponsors:
General Sponsor:
Trusted Sponsor:
Innovation Sponsor:
Silver Sponsor:
Gold Sponsors:
Platinum Sponsors:
Bronze Sponsors:
Technology Partners: