The document summarizes a workshop agenda for new InfluxData practitioners. It outlines the schedule of presentations and topics to be covered throughout the day-long workshop, including installing and querying the TICK stack, chronograf dashboarding, writing queries, architecting InfluxEnterprise, optimizing the TICK stack, and downsampling data. The final presentation on downsampling data is given by Michael DeSa and covers the concepts of downsampling, why it is useful, and how to perform it in InfluxDB using continuous queries and Kapacitor.
[OpenInfra Days Korea 2018] (Track 1) TACO (SKT All Container OpenStack): Clo...OpenStack Korea Community
- 폰트 문제로 다운로드를 여기서 해 주세요: http://bit.ly/openinfradays-day1-skt-taco
- 발표자: 안재석, SK Telecom
- 설명: https://event.openinfradays.kr/2018/session1/track_1_4
Comparing Next-Generation Container Image Building ToolsAkihiro Suda
http://sched.co/EaYe
Until recently, running `docker build` against Dockerfile had been the only way to build container images.
However, lots of opensource software are being proposed as successors/alternatives to `docker build`:
- BuildKit (Moby Project / Docker)
- img (Jessica Frazelle / Microsoft)
- Buildah (Project Atomic / Red Hat)
- umoci & Orca (SUSE)
- Bazel (Google)
- OpenShift S2I (Red Hat)
Akihiro Suda compares these new tools' advantages and disadvantages.
His evaluation basis would include but not be limited to:
- Performance (Cache efficiency, Concurrency, Distributed Execution)
- Secret management, e.g. SSH and AWS keys
- Support for non-Dockerfile
- Non-root execution
- UI & UX
- Governance of the community
He also proposes a unified interface for using these tools with Kubernetes in a vendor-neutral way.
[OpenInfra Days Korea 2018] (Track 1) TACO (SKT All Container OpenStack): Clo...OpenStack Korea Community
- 폰트 문제로 다운로드를 여기서 해 주세요: http://bit.ly/openinfradays-day1-skt-taco
- 발표자: 안재석, SK Telecom
- 설명: https://event.openinfradays.kr/2018/session1/track_1_4
Comparing Next-Generation Container Image Building ToolsAkihiro Suda
http://sched.co/EaYe
Until recently, running `docker build` against Dockerfile had been the only way to build container images.
However, lots of opensource software are being proposed as successors/alternatives to `docker build`:
- BuildKit (Moby Project / Docker)
- img (Jessica Frazelle / Microsoft)
- Buildah (Project Atomic / Red Hat)
- umoci & Orca (SUSE)
- Bazel (Google)
- OpenShift S2I (Red Hat)
Akihiro Suda compares these new tools' advantages and disadvantages.
His evaluation basis would include but not be limited to:
- Performance (Cache efficiency, Concurrency, Distributed Execution)
- Secret management, e.g. SSH and AWS keys
- Support for non-Dockerfile
- Non-root execution
- UI & UX
- Governance of the community
He also proposes a unified interface for using these tools with Kubernetes in a vendor-neutral way.
Intro to Kapacitor for Alerting and Anomaly DetectionInfluxData
In this session you’ll get detailed overview of Kapacitor, InfluxDB’s native data processing engine. The session will cover how to install, configure and build custom TICKscripts enable alerting and anomaly detection.
OpenShift is Red Hat's Platform-as-a-Service (PaaS) that lets developers quickly develop, host, and scale Docker container-based applications. OpenShift enables a uniform and standardised approach to container management across all hosting options including AWS/EC2 and other private/public cloud and on/off-premise variants. At this session, you will learn how Red Hat's enterprise clients are using OpenShift to enable their digital transformation initiatives. Examples will cover how realising a hybrid cloud strategy can simplify and reduce the risk of migrating and transitioning application workloads to containers in the cloud.
Alex Smith, Solutions Architect, Amazon Web Services, ASEAN
Stephen Bylo, Senior Solution Architect, Red Hat Asia Pacific Pte Ltd
– Elastic stack과 Data pipeline의 개념
– 데이터의 종류와 형태 / Document 데이터 모델링 (mapping, data type)
– 분산 데이터 저장소 관점에서의 Elasticsearch (index, shard & replica, segment)
https://learningspoons.com/course/detail/elastic-stack/
How Netflix Tunes EC2 Instances for PerformanceBrendan Gregg
CMP325 talk for AWS re:Invent 2017, by Brendan Gregg. "
At Netflix we make the best use of AWS EC2 instance types and features to create a high performance cloud, achieving near bare metal speed for our workloads. This session will summarize the configuration, tuning, and activities for delivering the fastest possible EC2 instances, and will help other EC2 users improve performance, reduce latency outliers, and make better use of EC2 features. We'll show how we choose EC2 instance types, how we choose between EC2 Xen modes: HVM, PV, and PVHVM, and the importance of EC2 features such SR-IOV for bare-metal performance. SR-IOV is used by EC2 enhanced networking, and recently for the new i3 instance type for enhanced disk performance as well. We'll also cover kernel tuning and observability tools, from basic to advanced. Advanced performance analysis includes the use of Java and Node.js flame graphs, and the new EC2 Performance Monitoring Counter (PMC) feature released this year."
An introduction to docker; the concepts; how to use it and why. The presentation is mainly based on the following presentation by docker, but with added info about Docker Compose and Docker Swarm.
https://www.slideshare.net/Docker/docker-101-nov-2016
#container #docker #Trifork #TriforkSelected #GotoConf
Kubernetes is a core component of most modern app delivery strategies because it helps organizations deliver apps faster with more agility and avoid costly downtime. But adopting Kubernetes is far from easy for organizations that are used to delivering three-tier apps. How do you decide which apps to refactor and which to build from scratch? What kinds of processes and tools can help you avoid common challenges around security and complexity? Join this session led by Rey Lejano, Kubernetes Field Engineer at Racher, for answers to these common questions.
Speakers:
Rey Lejano
Que sont le cloud computing et Amazon Web Services (AWS)? Comment mon entreprise peut-elle tirer profit du cloud computing? Si vous cherchez des réponses à ces questions, suivez ce webinaire et apprenez comment baisser vos coûts, être plus agile et maintenir, voire améliorer, le niveau de sécurité de vos applications.
KSQL is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. KSQL is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
KSQL offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using KSQL for most part. This will be done in a live demo on a fictitious IoT sample.
Michael DeSa will go over some of the advanced topics in Kapacitor such as joins, templated tasks, and debugging your tasks. Prerequisite: Intro To Kapacitor.
Dyn delivers exceptional Internet Performance. Enabling high quality services requires data centers around the globe. In order to manage services, customers need timely insight collected from all over the world. Dyn uses DataStax Enterprise (DSE) to deploy complex clusters across multiple datacenters to enable sub 50 ms query responses for hundreds of billions of data points. From granular DNS traffic data, to aggregated counts for a variety of report dimensions, DSE at Dyn has been up since 2013 and has shined through upgrades, data center migrations, DDoS attacks and hardware failures. In this webinar, Principal Engineers Tim Chadwick and Rick Bross cover the requirements which led them to choose DSE as their go-to Big Data solution, the path which led to SPARK, and the lessons that we’ve learned in the process.
Intro to Kapacitor for Alerting and Anomaly DetectionInfluxData
In this session you’ll get detailed overview of Kapacitor, InfluxDB’s native data processing engine. The session will cover how to install, configure and build custom TICKscripts enable alerting and anomaly detection.
OpenShift is Red Hat's Platform-as-a-Service (PaaS) that lets developers quickly develop, host, and scale Docker container-based applications. OpenShift enables a uniform and standardised approach to container management across all hosting options including AWS/EC2 and other private/public cloud and on/off-premise variants. At this session, you will learn how Red Hat's enterprise clients are using OpenShift to enable their digital transformation initiatives. Examples will cover how realising a hybrid cloud strategy can simplify and reduce the risk of migrating and transitioning application workloads to containers in the cloud.
Alex Smith, Solutions Architect, Amazon Web Services, ASEAN
Stephen Bylo, Senior Solution Architect, Red Hat Asia Pacific Pte Ltd
– Elastic stack과 Data pipeline의 개념
– 데이터의 종류와 형태 / Document 데이터 모델링 (mapping, data type)
– 분산 데이터 저장소 관점에서의 Elasticsearch (index, shard & replica, segment)
https://learningspoons.com/course/detail/elastic-stack/
How Netflix Tunes EC2 Instances for PerformanceBrendan Gregg
CMP325 talk for AWS re:Invent 2017, by Brendan Gregg. "
At Netflix we make the best use of AWS EC2 instance types and features to create a high performance cloud, achieving near bare metal speed for our workloads. This session will summarize the configuration, tuning, and activities for delivering the fastest possible EC2 instances, and will help other EC2 users improve performance, reduce latency outliers, and make better use of EC2 features. We'll show how we choose EC2 instance types, how we choose between EC2 Xen modes: HVM, PV, and PVHVM, and the importance of EC2 features such SR-IOV for bare-metal performance. SR-IOV is used by EC2 enhanced networking, and recently for the new i3 instance type for enhanced disk performance as well. We'll also cover kernel tuning and observability tools, from basic to advanced. Advanced performance analysis includes the use of Java and Node.js flame graphs, and the new EC2 Performance Monitoring Counter (PMC) feature released this year."
An introduction to docker; the concepts; how to use it and why. The presentation is mainly based on the following presentation by docker, but with added info about Docker Compose and Docker Swarm.
https://www.slideshare.net/Docker/docker-101-nov-2016
#container #docker #Trifork #TriforkSelected #GotoConf
Kubernetes is a core component of most modern app delivery strategies because it helps organizations deliver apps faster with more agility and avoid costly downtime. But adopting Kubernetes is far from easy for organizations that are used to delivering three-tier apps. How do you decide which apps to refactor and which to build from scratch? What kinds of processes and tools can help you avoid common challenges around security and complexity? Join this session led by Rey Lejano, Kubernetes Field Engineer at Racher, for answers to these common questions.
Speakers:
Rey Lejano
Que sont le cloud computing et Amazon Web Services (AWS)? Comment mon entreprise peut-elle tirer profit du cloud computing? Si vous cherchez des réponses à ces questions, suivez ce webinaire et apprenez comment baisser vos coûts, être plus agile et maintenir, voire améliorer, le niveau de sécurité de vos applications.
KSQL is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. KSQL is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
KSQL offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using KSQL for most part. This will be done in a live demo on a fictitious IoT sample.
Michael DeSa will go over some of the advanced topics in Kapacitor such as joins, templated tasks, and debugging your tasks. Prerequisite: Intro To Kapacitor.
Dyn delivers exceptional Internet Performance. Enabling high quality services requires data centers around the globe. In order to manage services, customers need timely insight collected from all over the world. Dyn uses DataStax Enterprise (DSE) to deploy complex clusters across multiple datacenters to enable sub 50 ms query responses for hundreds of billions of data points. From granular DNS traffic data, to aggregated counts for a variety of report dimensions, DSE at Dyn has been up since 2013 and has shined through upgrades, data center migrations, DDoS attacks and hardware failures. In this webinar, Principal Engineers Tim Chadwick and Rick Bross cover the requirements which led them to choose DSE as their go-to Big Data solution, the path which led to SPARK, and the lessons that we’ve learned in the process.
In this deck from the 2018 Swiss HPC Conference, Axel Koehler from NVIDIA presents: The Convergence of HPC and Deep Learning.
"The intersection of AI and HPC is extending the reach of science and accelerating the pace of scientific innovation like never before. The technology originally developed for HPC has enabled deep learning, and deep learning is enabling many usages in science. Deep learning is also helping deliver real-time results with models that used to take days or months to simulate. The presentation will give an overview about the latest hard- and software developments for HPC and Deep Learning from NVIDIA and will show some examples that Deep Learning can be combined with traditional large scale simulations."
Watch the video: https://wp.me/p3RLHQ-ijM
Learn more: http://nvidia.com
and
http://www.hpcadvisorycouncil.com/events/2018/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Real-Time Streaming: Move IMS Data to Your Cloud Data WarehousePrecisely
With over 22,000 transactions processed every second, your mainframe IMS is a critical source of data for the cloud data warehouses that feed analytics, customer experience or regulatory initiatives. However, extracting data from mainframe IMS can be time-consuming and costly, leading to the exclusion of IMS data from cloud data warehouses all together – and leaving valuable insights unseen.
Never ignore or manually extract mainframe IMS data again. In this on-demand webcast, you will learn how Connect CDC enables your team to develop integrations quickly and easily between mainframe IMS and cloud data warehouses in the most cost-effective way possible.
First in Class: Optimizing the Data Lake for Tighter IntegrationInside Analysis
The Briefing Room with Dr. Robin Bloor and Teradata RainStor
Live Webcast October 13, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=012bb2c290097165911872b1f241531d
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful data management solutions require a fusion of all relevant data, new and old, which has proven challenging for many companies. With a data lake that’s been optimized for fast queries, solid governance and lifecycle management, users can take data management to a whole new level.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses the relevance of data lakes in today’s information landscape. He’ll be briefed by Mark Cusack of Teradata, who will explain how his company’s archiving solution has developed into a storage point for raw data. He’ll show how the proven compression, scalability and governance of Teradata RainStor combined with Hadoop can enable an optimized data lake that serves as both reservoir for historical data and as a "system of record” for the enterprise.
Visit InsideAnalysis.com for more information.
SingleStore & Kafka: Better Together to Power Modern Real-Time Data Architect...HostedbyConfluent
To remain competitive, organizations need to democratize access to fast analytics, not only to gain real-time insights on their business but also to power smart apps that need to react in the moment. In this session, you will learn how Kafka and SingleStore enable modern, yet simple data architecture to analyze both fast paced incoming data as well as large historical datasets. In particular, you will understand why SingleStore is well suited process data streams coming from Kafka.
Scylla Summit 2019 Keynote - Dor Laor - Beyond CassandraScyllaDB
ScyllaDB CEO Dor Laor lays out the ten million dollar engineering problem for distributed systems, and how only Scylla is architected to address the issue at the heart of Big Data ROI. He then introduces ScyllaDB's Glauber Costa and Packet's James Malachowski to reveal a new level of performance for a persistent NoSQL datastore. Dor concludes his talk with a bold proposition about how Scylla is uniquely positioned to help companies easily create and scale the software they need to achieve their vision.
Smuggling Multi-Cloud Support into Cloud-native Applications using Elastic Co...Nane Kratzke
Elastic container platforms (like Kubernetes, Docker Swarm, Apache Mesos) fit very well with existing cloud-native application architecture approaches. So it is more than astonishing, that these already existing and open source available elastic platforms are not considered more consequently for multi-cloud approaches. Elastic container platforms provide inherent multi-cloud support that can be easily accessed. We present a solution proposal of a control process which is able to scale (and migrate as a side effect) elastic container platforms across different public and private cloud-service providers. This control loop can be used in an execution phase of self-adaptive auto-scaling MAPE loops (monitoring, analysis, planning, execution). Additionally, we present several lessons learned from our prototype implementation which might be of general interest for researchers and practitioners. For instance, to describe only the intended state of an elastic platform and let a single control process take care to reach this intended state is far less complex than to define plenty of specific and necessary multi-cloud aware workflows to deploy, migrate, terminate, scale up and scale down elastic platforms or applications.
SingleStore & Kafka: Better Together to Power Modern Real-Time Data Architect...HostedbyConfluent
To remain competitive, organizations need to democratize access to fast analytics, not only to gain real-time insights on their business but also to power smart apps that need to react in the moment. In this session, you will learn how Kafka and SingleStore enable modern, yet simple data architecture to analyze both fast paced incoming data as well as large historical datasets. In particular, you will understand why SingleStore is well suited process data streams coming from Kafka.
This is a slide deck that was used for our 11/19/15 Nike Tech Talk to give a detailed overview of the SnappyData technology vision. The slides were presented by Jags Ramnarayan, Co-Founder & CTO of SnappyData
The Sierra Supercomputer: Science and Technology on a Missioninside-BigData.com
In this deck from the Stanford HPC Conference, Adam Bertsch from LLNL presents: The Sierra Supercomputer: Science and Technology on a Mission.
"LLNL just celebrated its 65th anniversary. Since 1952, the laboratory has been at the forefront of high performance computing. Initially, HPC was used to accelerate the design and testing of the nation's nuclear stockpile. Since the last U.S. nuclear test in 1992, HPC has been used to validate the safety, security, and reliability of stockpile without nuclear testing.
Our next flagship HPC system at LLNL will be called Sierra. A collaboration between multiple government and industry partners, Sierra and its sister system Summit at ORNL, will pave the way towards Exascale computing architectures and predictive capability."
Watch the video: https://wp.me/p3RLHQ-i4K
Learn more: https://computation.llnl.gov/computers/sierra
and
http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Optimizing, Profiling, and Deploying TensorFlow AI Models with GPUs - San Fra...Chris Fregly
http://pipeline.ai
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models - and the TensorFlow Runtime - in GPU-based production environment. This talk is 100% demo based on open source tools and completely reproducible through Docker on your own GPU cluster.
Bio
Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High-Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
http://pipeline.ai
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
InfluxData is excited to announce InfluxDB Clustered, the self-managed version of InfluxDB 3.0 with unparalleled flexibility, speed, performance, and scale. The evolution of InfluxDB Enterprise, InfluxDB Clustered is delivered as a collection of Kubernetes-based containers and services, which enables you to run and operate InfluxDB 3.0 where you need it, whether that's on-premises or in a private cloud environment. With this new enterprise offering, we’re excited to provide our customers with real-time queries, low-cost object storage, unlimited cardinality, and SQL language support – all with improved data access, support, and security! The newest version of InfluxDB was built on Apache Arrow, and through the open source ecosystem and integrations, extends the value of your time-stamped data.
Join this webinar to learn more about InfluxDB Clustered, and how to manage your large mission-critical workloads in the highly available database service offering!
In this webinar, Balaji Palani and Gunnar Aasen will dive into:
Key features of the new InfluxDB Clustered solution
Use cases for using the newest version of the purpose-built time series database
Live demo
During this 1-hour technical webinar, you’ll also get a chance to ask your questions live.
Best Practices for Leveraging the Apache Arrow EcosystemInfluxData
Apache Arrow is an open source project intended to provide a standardized columnar memory format for flat and hierarchical data. It enables more efficient analytics workloads for modern CPU and GPU hardware, which makes working with large data sets easier and cheaper.
InfluxData and Dremio are both members of the Apache Software Foundation (ASF). Dremio is a data lakehouse management service known for its scalability and capacity for direct querying across diverse data sources. InfluxDB is the purpose-built time series database, and InfluxDB 3.0 has a new columnar storage engine and uses the Arrow format for representing data and moving data to and from Parquet. Discover how InfluxDB and Dremio have advanced their solutions by relying on the Apache Arrow framework.
Join this live panel as Alex Merced and Anais Dotis-Georgiou dive into:
Advantages to utilizing the Apache Arrow ecosystem
Tips and tricks for implementing the columnar data structure
How developers can best utilize the ASF to innovate and contribute to new industry standards
How Bevi Uses InfluxDB and Grafana to Improve Predictive Maintenance and Redu...InfluxData
Bevi are the creators of smart water dispensers which empower people to choose their desired beverage — flat or sparkling, their desired flavor and temperature. Since 2014, Bevi users have saved more than 350 million bottles and cans. Their "smart" water coolers have prevented the extraction of 1.4 trillion oz of oil from Earth and have saved 21.7 billion grams of CO2 from the atmosphere.
Discover how Bevi uses a time series database to enable better predictive maintenance and alerting of their entire ecosystem — including the hardware and software. They are using InfluxDB to collect sensor data in real-time remotely from their internet-connected machines about their status and activity — i.e., flavor and CO2 levels, water temp, filter status, etc. They a7re using these metrics to improve their customer experience and continuously improve their sustainability practices. Gain tips and tricks on how to best utilize InfluxDB's schema-less design.
Join this webinar as Spencer Gagnon dives into:
Bevi's approach to reducing organizations' carbon footprint — they are saving 50K+ bottles and cans annually
Their entire system architecture — including InfluxDB Cloud, Grafana, Kafka, and DigitalOcean
The importance of using time-stamped data to extend the life of their machines
Power Your Predictive Analytics with InfluxDBInfluxData
If you're using InfluxDB to store and manage your time series data, you're already off to a great start. But why stop there? In our upcoming webinar, we'll show you how to take your data analysis to the next level by building predictive analytics using a variety of tools and techniques.
We will demonstrate how to use Quix to create custom dashboards and visualizations that allow you to monitor your data in real-time. We'll also introduce you to Hugging Face, a powerful tool for building models that can predict future trends and identify anomalies. With these tools at your disposal, you'll be able to extract valuable insights from your data and make more informed decisions about the future. Don't miss out on this opportunity to improve your data analysis skills and take your business to the next level!
What you will learn:
Use InfluxDB to store and manage time series data
Utilize Quix and Hugging Face to build models, visualize trends, and identify anomalies
Extract valuable insights from your data
Improve your data analysis skills to make informed decision
How Teréga Replaces Legacy Data Historians with InfluxDB, AWS and IO-Base InfluxData
Are you considering replacing your legacy data historian and moving your OT data to the cloud? Join this technical webinar to learn how to adopt InfluxDB and IO Base - a digital platform used to improve operational efficiencies!
Teréga Solutions are the creators of digital solutions used to improve energy efficiencies and to address decarbonization challenges. Their network includes 5,000+ km of gas pipelines within France; they aim to help France attain carbon neutrality by 2050. With these impressive goals in mind, Teréga has created IO-Base — the digital platform to improve industrial performance, and increase profitability. Creating digital twins for their clients allows them to collect data from all production sites and view it in real time, from anywhere and at any time.
Discover how Teréga uses InfluxDB, Docker, and AWS to monitor its gas and hydrogen pipeline infrastructure. They chose to replace their legacy data historian with InfluxDB — the purpose built time series database. They are collecting more than 100K different metrics at various frequencies — some are collected every 5 seconds to only every 1-2 minutes. THey have reduced overall IT spend by 50% and collect 2x the amount of data at 20x frequency! By using various industrial protocols (Modbus, OPC-UA, etc.), Teréga improved output, reduced the TCO, and is now able to create added-value services: forecast, monitoring, predictive maintenance.
Join this webinar as Thomas Delquié dives into:
Teréga's approach to modernizing fossil fuel pipelines IT systems while improving yields and safety
Their centralized methodology to collecting sensor, hardware, and network metrics
The importance of time series data and why they chose InfluxDB
Build an Edge-to-Cloud Solution with the MING StackInfluxData
FlowForge enables organizations to reliably deliver Node-RED applications in a continuous, collaborative, and secure manner. Node-RED is the popular, low-code programming solution that makes it easy to connect different services using a visual programming environment. InfluxData is the creator of InfluxDB, the purpose-built time series database run by developers at scale and in any environment in the cloud, on-premises, or at the edge.
Jump-start monitoring your industrial IoT devices and discover how to build an edge-to-cloud solution with the MING stack. The MING stack includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana. This solution can be used to improve fleet management, enable predictive maintenance of industrial machines and power generation equipment (i.e. turbines and generators) and increase safety practices (i.e. buildings, construction sites). Join this webinar to learn best practices from industrial IoT SME's.
In this webinar, Robert Marcer and Jay Clifford dive into:
Best practices for monitoring sensor data collected by everyone — from the edge to the factory
Tips and tricks for using Node-RED and InfluxDB together
Demo — see Node-RED and InfluxDB live
Meet the Founders: An Open Discussion About Rewriting Using RustInfluxData
Rust is a systems programming language designed for high performance, type safety, and concurrency. According to Stack Overflow’s annual survey in 2022, Rust is the most loved language with 87% of developers saying they want to continue using it. The same survey also reported that nearly 20% of developers aren’t currently using Rust, but want to start developing using it.
Ockam’s suite of programming libraries, command line tools, and managed cloud services enable developers to orchestrate end-to-end encryption. InfluxDB is the purpose-built time series database developed to handle time series data for IoT, monitoring, and real-time analytics. Ockam was originally developed using C, and InfluxDB was originally written using Go; both solutions have been completely rewritten in Rust. Discover why two founders decided to rewrite their developer tools using Rust, and gain insight into the strategy beforehand and the entire process.
Join this live panel as Mrinal Wadhwa and Paul Dix dive into:
Their approach to rewriting a project in Rust
How to build and train engineering teams
Tips and tricks learned along the way - pitfalls to look out for!
Join this webinar as there will be a live discussion with Q&A
InfluxData is excited to announce the general availability of InfluxDB Cloud Dedicated! It is a fully managed time series database service running on cloud infrastructure resources that are dedicated to a single tenant. With this new offering, we’re excited to provide our customers with additional security options, and more custom configuration options to best suit customers’ workload requirements. Join this webinar to learn more about InfluxDB Cloud, and the new dedicated database service offering!
In this webinar, Balaji Palani and Gary Fowler will dive into:
Key features of the new InfluxDB Cloud Dedicated solution
Use cases for using the newest version of the purpose-built time series database
Live demo
During this 1-hour technical webinar, you’ll also get a chance to ask your questions live.
Gain Better Observability with OpenTelemetry and InfluxDB InfluxData
Many developers and DevOps engineers have become aware of using their observability data to gain greater insights into their infrastructure systems. InfluxDB is the purpose-built time series database used to collect metrics and gain observability into apps, servers, containers, and networks. Developers use InfluxDB to improve the quality and efficiency of their CI/CD pipelines. Start using InfluxDB to aggregate infrastructure and application performance monitoring metrics to enable better anomaly detection, root-cause analysis, and alerting.
This session will demonstrate how to record metrics, logs, and traces with one library — OpenTelemetry — and store them in one open source time series database — InfluxDB. Zoe will demonstrate how easy it is to set up the OpenTelemetry Operator for Kubernetes and to store and analyze your data in InfluxDB.
How a Heat Treating Plant Ensures Tight Process Control and Exceptional Quali...InfluxData
American Metal Processing Company ("AMP") is the US' largest commercial rotary heat treat facility with customers in the automotive, construction, military, and agriculture industries. They use their atmosphere-protected rotary retort furnaces to provide their clients with three primary hardening services: neutral hardening (quench and temper), carburizing, and carbonitriding.
This furnace style ensures consistent, uniform heat treatment process vs. traditional batch-or-belt-style furnaces; excels at processing high volumes of smaller parts with tight tolerances; and improves the strength and toughness of plain carbon steels. Discover why AMP’s use of Telegraf, InfluxDB, Node-RED, and Grafana allows them to gain 24/7 insights into their plant operations and metallurgical results. Learn how they use time-stamped data to gain accurate metrics about their consumables usage, furnace profiles, and machine status.
Join this webinar as Grant Pinkos dives into:
American Metal Processing's approach to heat treating in a digitized environment through connected systems
Their approach to collecting and measuring sensor data to enable predictive maintenance and improve product quality
Why they need a time series database for managing and analyzing vast amounts of time-stamped data
How Delft University's Engineering Students Make Their EV Formula-Style Race ...InfluxData
Delft University is the oldest and largest technical university in the Netherlands with 25,000+ students. Since 1999, they have had a team of students (undergraduate and graduate) designing, building, and racing cars, as part of the Formula Student worldwide competition. The competition has grown to include teams from 1K+ universities in 20+ countries. Students are responsible for all aspects of car manufacturing (research, construction, testing, developing, marketing, management, and fundraising). Delft University's team includes 90 students across disciplines.
Discover how Delft University's team uses Marple and InfluxDB to collect telemetry and sensor metrics while they develop, test, and race their electrics cars. They collect sensor data about their EV's control systems using a time series platform. During races, they are collecting IoT data about their batteries, accelerometer, gyroscope, tires, etc. The engineers are able to share important car stats during races which help the drivers tweak their driving decisions — all with the goal of winning. After races, the entire team are able to analyze data in Marple to understand what to do better next time. By using Marple + InfluxDB, their team are able to collect, share and analyze high frequency car data used to make their car faster at competitions.
Join this webinar as Robbin Baauw and Nero Vanbiervliet dive into:
Marple's approach to empowering engineers to organize, analyze, and visualize their data
Delft University's collaborative methodology to building and racing their Formula-style race car
How InfluxDB is crucial to their collaborative engineering and racing process
Introducing InfluxDB’s New Time Series Database Storage EngineInfluxData
InfluxData is excited to announce the general availability of InfluxDB Cloud's new storage engine! It is a cloud-native, real-time, columnar database optimized for time series data. InfluxDB's rebuilt core was coded in Rust and sits on top of Apache Arrow and DataFusion. InfluxData's team picked Apache Parquet as the persistent format. In this webinar, Paul Dix and Balaji Palani will demonstrate key product features including the removal of cardinality limits!
They will dive into:
The next phase of the InfluxDB platform
How using Apache Arrow's ecosystem has improved InfluxDB's performance and scalability
Key features of InfluxDB Cloud's new core — including SQL native support
Start Automating InfluxDB Deployments at the Edge with balena InfluxData
balena.io helps companies develop, deploy, update, and manage IoT devices. By using Linux containers and other cloud technologies, balena enables teams to quickly and easily build fleets of connected devices. Developers are able to use containers with the language of choice and pull IoT sensor data from 70+ different single board computers into balenaCloud. Discover how to use balena.io to automate your InfluxDB deployments at the edge!
During this one-hour session, experts from balena and InfluxData will demonstrate how to build and deploy your own air quality IoT solution. You will learn:
The fundamentals of IoT sensor deployment and management using balena.
How to use a time series platform to collect and visualize metrics from edge devices.
Tips and tricks to using balenaCloud to automate InfluxDB deployments and Telegraf configurations.
How to use InfluxDB's Edge Data Replication feature to collect sensor data and push it to InfluxDB Cloud for analysis.
No coding experience required, just a curiosity to start your own IoT adventure.
Understanding InfluxDB’s New Storage EngineInfluxData
Learn more about InfluxDB’s new storage engine! The team developed a cloud-native, real-time, columnar database optimized for time series data. We built it all in Rust and it sits on top of Apache Arrow and DataFusion. We chose Apache Parquet as the persistent format, which is an open source columnar data file format. This new storage engine provides InfluxDB Cloud users with new functionality, including the removal of cardinality limits, so developers can bring in massive amounts of time series data at scale.
In this webinar, Anais Dotis-Georgiou will dive into:
Requirements for rebuilding InfluxDB’s core
Key product features and timeline
How Apache Arrow’s ecosystem is used to meet those requirements
Stick around for a demo and live Q&A
Streamline and Scale Out Data Pipelines with Kubernetes, Telegraf, and InfluxDBInfluxData
RudderStack — the creators of the leading open source Customer Data Platform (CDP) — needed a scalable way to collect and store metrics related to customer events and processing times (down to the nanosecond). They provide their clients with data pipelines that simplify data collection from applications, websites, and SaaS platforms. RudderStack's solution enables clients to stream customer data in real time — they quickly deploy flexible data pipelines that send the data to the customer's entire stack without engineering headaches. Customers are able to stream data from any tool using their 16+ SDK's, and they are able to transform the data in-transit using JavaScript or Python. How does RudderStack use a time series platform to provide their customers with real-time analytics?
Join this webinar as Ryan McCrary dives into:
RudderStack's approach to streamlining data pipelines with their 180+ out-of-the-box integrations
Their data architecture including Kapacitor for alerting and Grafana for customized dashboards
Why using InfluxDB was crucial for them for fast data collection and providing single-sources of truths for their customers
Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...InfluxData
Customers using ThingWorx and the Manufacturing Solutions often need to store property data longer than the Solutions default to. These customers are recommended to use InfluxDB, and this presentation will cover the key considerations for moving to InfluxDB vs the standard ThingWorx value streams. Join this session as Ward highlights ThingWorx’s solution and its easy implementation process.
Scott Anderson [InfluxData] | New & Upcoming Flux Features | InfluxDays 2022InfluxData
Two new features are coming to Flux that add flexibility
and functionality to your data workflow—polymorphic
labels and dynamic types. This session walks through
these new features and shows how they work.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
2. Agenda: New Practitioners Track
WORKSHOPAGENDA
8:00 AM – 9:00 AM Breakfast
9:00 AM – 10:00 AM Installing the TICK Stack and Your First Query Katy Farmer
10:00 AM – 10:50 AM Chronograf and Dashboarding Margo Schaedel
10:50 AM – 11:20 AM Break
11:20 AM – 12:10 PM Writing Queries (InfluxQL and TICK) Katy Farmer
12:10 PM – 1:10 PM Lunch
1:10 PM – 2:00 PM Architecting InfluxEnterprise for Success Ed Bernier
2:00 PM – 2:10 PM Break
2:10 PM – 3:10 PM Optimizing the TICK Stack Dave Patton
3:10 PM – 4:00 PM Downsampling Data Michael DeSa
4:00 PM Pizza and Beer
3. Michael DeSa
Software Engineer
@mjdesa
Downsampling Data
Michael DeSa is a Software Engineer at InfluxData who focuses
on increasing the performance capabilities of InfluxDB. He has
led the InfluxDB training course across the US, providing
students with an in depth understanding of how InfluxDB works
as well as sharing best practices. He has a degree in Math, with
a focus on Abstract Algebra, from the University of California, at
Berkeley and was an Instructor of the Web Development
Immersive series at General Assembly in San Francisco.
12. Smart
Downsampling
• Data relevance decreases with age
– new data - high resolution
– old data - low resolution
• As new data becomes old, decrease the
resolution
16. CQ Syntax
CREATE CONTINUOUS QUERY <cq_name>
ON <database_name>
BEGIN
SELECT <function>(<stuff>)
INTO <destination_measurement>
FROM <source_measurement>
GROUP BY time(<interval>), [tags]
END
17. CQ Example
CREATE CONTINUOUS QUERY mycq
ON telegraf
BEGIN
SELECT mean(usage_user)
INTO telegraf.rollup_5m.cpu
FROM telegraf.autogen.cpu
GROUP BY time(5m), *
END
18. CQ Example
CREATE CONTINUOUS QUERY mycq
ON telegraf
BEGIN
SELECT mean(usage_user) AS usage_user
INTO telegraf.rollup_5m.cpu
FROM telegraf.autogen.cpu
GROUP BY time(5m), *
END
19. CQ Example
CREATE CONTINUOUS QUERY mycq
ON telegraf
BEGIN
SELECT mean(*)
INTO telegraf.rollup_5m.:MEASUREMENT:
FROM telegraf.autogen./.*/
GROUP BY time(5m), *
END
20. Issues with the
General CQ
CREATE CONTINUOUS QUERY mycq
ON telegraf
BEGIN
SELECT mean(*)
INTO telegraf.rollup_5m.:MEASUREMENT:
FROM telegraf.autogen./.*/
GROUP BY time(5m), *
END
21. Issues with CQs
• Places Extra Load on your InfluxDB instance
• Limited to things expressible in InfluxQL
– No rolling windows
– No cross measurement joins
• All CQ processing and execution runs on a
single node in the cluster
• Can’t have consistent field names without
having many CQs
– field_a -> mean_field_a
28. Issues with
Kapacitor
• Stream tasks currently aren’t feasible in high
throughput InfluxDB instances
– All write load gets mirrored onto a single
Kapacitor instance
• TICKscript doesn’t have support for
wildcard operations
– Only way to downsample all data is to have
(#measurement x #fields) tasks