The document provides an agenda for a seasoned developers track workshop. The agenda includes sessions on InfluxDB query language (IFQL), writing Telegraf plugins, using InfluxDB for open tracing, advanced Kapacitor techniques, setting up InfluxData for IoT, and database orchestration. There will also be breakfast, lunch, breaks and pizza/beer.
Kapacitor - Real Time Data Processing EnginePrashant Vats
Kapacitor is a native data processing engine.Kapacitor is a native data processing engine.It can process both stream and batch data from InfluxDB.It lets you plug in your own custom logic or user-defined functions to process alerts with dynamic thresholds. Key Kapacitor Capabilities
-Alerting
-ETL (Extraction, Transformation and Loading)
-Action Oriented
-Streaming Analytics
-Anomaly Detection
Kapacitor uses a DSL (Domain Specific Language) called TICKscript to define tasks.
In this InfluxDays NYC 2019 talk, InfluxData Founder & CTO Paul Dix will outline his vision around the platform and its new data scripting and query language Flux, and he will give the latest updates on InfluxDB time series database. This talk will walk through the vision and architecture with demonstrations of working prototypes of the projects.
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.
How to Build a Telegraf Plugin by Noah CrowleyInfluxData
Telegraf is a plugin-driven server agent for collecting & reporting metrics and there are many plugins already written to source data from a variety of services and systems. However, there may be instances where you need to write your own plugin to source data from your particular systems. In this InfluxDays NYC 2019 session, Noah Crowley will provide you with the steps on how to write your own Telegraf plugin. Writing your own Telegraf plugin will require an understanding of the Go programming language.
Kapacitor - Real Time Data Processing EnginePrashant Vats
Kapacitor is a native data processing engine.Kapacitor is a native data processing engine.It can process both stream and batch data from InfluxDB.It lets you plug in your own custom logic or user-defined functions to process alerts with dynamic thresholds. Key Kapacitor Capabilities
-Alerting
-ETL (Extraction, Transformation and Loading)
-Action Oriented
-Streaming Analytics
-Anomaly Detection
Kapacitor uses a DSL (Domain Specific Language) called TICKscript to define tasks.
In this InfluxDays NYC 2019 talk, InfluxData Founder & CTO Paul Dix will outline his vision around the platform and its new data scripting and query language Flux, and he will give the latest updates on InfluxDB time series database. This talk will walk through the vision and architecture with demonstrations of working prototypes of the projects.
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.
How to Build a Telegraf Plugin by Noah CrowleyInfluxData
Telegraf is a plugin-driven server agent for collecting & reporting metrics and there are many plugins already written to source data from a variety of services and systems. However, there may be instances where you need to write your own plugin to source data from your particular systems. In this InfluxDays NYC 2019 session, Noah Crowley will provide you with the steps on how to write your own Telegraf plugin. Writing your own Telegraf plugin will require an understanding of the Go programming language.
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
Presented at GDG Devfest Ukraine 2018.
Prometheus has become the defacto monitoring system for cloud native applications, with systems like Kubernetes and Etcd natively exposing Prometheus metrics. In this talk Tom will explore all the moving part for a working Prometheus-on-Kubernetes monitoring system, including kube-state-metrics, node-exporter, cAdvisor and Grafana. You will learn about the various methods for getting to a working setup: the manual approach, using CoreOS’s Prometheus Operator, or using Prometheus Ksonnet Mixin. Tom will also share some little tips and tricks for getting the most out of your Prometheus monitoring, including the common pitfalls and what you should be alerting on.
http://flink-forward.org/kb_sessions/scaling-stream-processing-with-apache-flink-to-very-large-state/
The majority of streaming programs is ‘stateful’: Windowed Aggregations, Sessions, Joins, Complex Event Processing, Tables – they all require to keep some form of state across individual events. With the migration of more and more complex batch jobs or data processing pipelines to streaming applications, some streaming programs need to keep terabytes of state. Apache Flink implements a checkpointing-based recovery mechanism that guarantees exactly-once semantics for state also in the presence of failures. The cost of checkpointing and recovery depends on the size of the program’s state. In this talk, we will discuss the current status of stateful processing in Apache Flink, as well as the ongoing efforts to make Flink’s fault tolerance mechanism scale to very large state sizes, supporting frequent checkpoints and faster recovery of large state, without requiring excessive numbers of machines.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
A quick introduction into promises and observables by Stefan Charsley.
Presented on 23rd July 2020 for Palmerston North Software Developers meetup group.
As presented at DevDuck #6 - JavaScript meetup for developers (www.devduck.pl)
----
Looking for a company to build your app? - Check us out at www.brainhub.eu
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
This document is about how to Write a CRUD App with Spring Boot Jpa or jdbc. a related example for this document is on github with the following address :
https://github.com/ghorbanihamid/SpringBoot_AOP_JPA_Example
Telegraf is a plugin-driven server agent for collecting & reporting metrics and there are many plugins already written to source data from a variety of services and systems. However, there may be instances where you need to write your own plugin to source data from your particular systems. In this session, Noah will provide you with the steps on how to write your own Telelgraf plugin. This will require an understanding of the Go programming language.
Presented at Web Unleashed on September 16-17, 2015 in Toronto, Canada
More info at www.fitc.ca/webu
Why TypeScript?
with Jeff Francis
OVERVIEW
TypeScript is a type-checked superset of JavaScript that benefits medium-sized to complex JavaScript projects. Why would you want to learn a new language, instead of another JavaScript framework? You have all this existing JavaScript code, so how can you adopt something new without throwing the old stuff out?
This session is about the benefits of using TypeScript on top of JavaScript in your projects, and demonstrate step by step ways of migrating an existing JavaScript project to TypeScript. We will dive into code generated by the compiler and look at resources and tools that make working in TypeScript a pleasurable experience.
OBJECTIVE
To understand when it’s a good idea to use TypeScript.
TARGET AUDIENCE
JavaScript developers.
ASSUMED AUDIENCE KNOWLEDGE
Intermediate JavaScript experience.
FIVE THINGS AUDIENCE MEMBERS WILL LEARN
The basics of TypeScript – types, classes, modules, and functions
How TypeScript’s design makes getting started simple and helps projects
What compiled TypeScript looks like and how to debug
What tools can help take advantage of TypeScript’s type information
How to migrate a JavaScript project to TypeScript
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
A detailed overview of Kapacitor, InfluxDB’s native data processing engine. How to install, configure and build custom TICKscripts enable alerting and anomaly detection
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
Presented at GDG Devfest Ukraine 2018.
Prometheus has become the defacto monitoring system for cloud native applications, with systems like Kubernetes and Etcd natively exposing Prometheus metrics. In this talk Tom will explore all the moving part for a working Prometheus-on-Kubernetes monitoring system, including kube-state-metrics, node-exporter, cAdvisor and Grafana. You will learn about the various methods for getting to a working setup: the manual approach, using CoreOS’s Prometheus Operator, or using Prometheus Ksonnet Mixin. Tom will also share some little tips and tricks for getting the most out of your Prometheus monitoring, including the common pitfalls and what you should be alerting on.
http://flink-forward.org/kb_sessions/scaling-stream-processing-with-apache-flink-to-very-large-state/
The majority of streaming programs is ‘stateful’: Windowed Aggregations, Sessions, Joins, Complex Event Processing, Tables – they all require to keep some form of state across individual events. With the migration of more and more complex batch jobs or data processing pipelines to streaming applications, some streaming programs need to keep terabytes of state. Apache Flink implements a checkpointing-based recovery mechanism that guarantees exactly-once semantics for state also in the presence of failures. The cost of checkpointing and recovery depends on the size of the program’s state. In this talk, we will discuss the current status of stateful processing in Apache Flink, as well as the ongoing efforts to make Flink’s fault tolerance mechanism scale to very large state sizes, supporting frequent checkpoints and faster recovery of large state, without requiring excessive numbers of machines.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
A quick introduction into promises and observables by Stefan Charsley.
Presented on 23rd July 2020 for Palmerston North Software Developers meetup group.
As presented at DevDuck #6 - JavaScript meetup for developers (www.devduck.pl)
----
Looking for a company to build your app? - Check us out at www.brainhub.eu
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
This document is about how to Write a CRUD App with Spring Boot Jpa or jdbc. a related example for this document is on github with the following address :
https://github.com/ghorbanihamid/SpringBoot_AOP_JPA_Example
Telegraf is a plugin-driven server agent for collecting & reporting metrics and there are many plugins already written to source data from a variety of services and systems. However, there may be instances where you need to write your own plugin to source data from your particular systems. In this session, Noah will provide you with the steps on how to write your own Telelgraf plugin. This will require an understanding of the Go programming language.
Presented at Web Unleashed on September 16-17, 2015 in Toronto, Canada
More info at www.fitc.ca/webu
Why TypeScript?
with Jeff Francis
OVERVIEW
TypeScript is a type-checked superset of JavaScript that benefits medium-sized to complex JavaScript projects. Why would you want to learn a new language, instead of another JavaScript framework? You have all this existing JavaScript code, so how can you adopt something new without throwing the old stuff out?
This session is about the benefits of using TypeScript on top of JavaScript in your projects, and demonstrate step by step ways of migrating an existing JavaScript project to TypeScript. We will dive into code generated by the compiler and look at resources and tools that make working in TypeScript a pleasurable experience.
OBJECTIVE
To understand when it’s a good idea to use TypeScript.
TARGET AUDIENCE
JavaScript developers.
ASSUMED AUDIENCE KNOWLEDGE
Intermediate JavaScript experience.
FIVE THINGS AUDIENCE MEMBERS WILL LEARN
The basics of TypeScript – types, classes, modules, and functions
How TypeScript’s design makes getting started simple and helps projects
What compiled TypeScript looks like and how to debug
What tools can help take advantage of TypeScript’s type information
How to migrate a JavaScript project to TypeScript
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
A detailed overview of Kapacitor, InfluxDB’s native data processing engine. How to install, configure and build custom TICKscripts enable alerting and anomaly detection
Virtual training Intro to InfluxDB & TelegrafInfluxData
How to setup InfluxDB & Telgraf to pull metrics into your InfluxDB. An introduction to querying data with InfluxQL. Learn more and download the open source version of Telegraf now: https://www.influxdata.com/time-series-platform/telegraf/
You use InfluxData to monitor the performance of your infrastructure and apps—so it is equally important to keep your InfluxEnterprise instance up and running. Tim Hall, InfluxData VP of Products, will outline why and how you can monitor InfluxEnterprise with InfluxDB.
Lessons Learned Running InfluxDB Cloud and Other Cloud Services at Scale by T...InfluxData
In this session, Tim will cover principles, learnings, and practical advice from operating multiple cloud services at scale, including of course our InfluxDB Cloud service. What do we monitor, what do we alert on, and how did we architect it all? What are our underlying architectural and operational principles?
Lessons Learned: Running InfluxDB Cloud and Other Cloud Services at Scale | T...InfluxData
In this session, Tim will cover principles, learnings, and practical advice from operating multiple cloud services at scale, including of course our InfluxDB Cloud service. What do we monitor, what do we alert on, and how did we architect it all? What are our underlying architectural and operational principles?
Accelerating Spark MLlib and DataFrame with Vector Processor “SX-Aurora TSUBASA”Databricks
NEC has recently released new vector system "SX-Aurora TSUBASA". This system is usually used for HPC, but is also designed for data analytics by building the vector processor as a PCIe-attached accelerator. In comparison with GPGPU, it suits for memory intensive workloads, often see at statistical machine learning and data frame processing. To accelerate data analytics on Spark, we have created acceleration framework "Frovedis" for SX-Aurora TSUBASA. It supports several machine learning algorithms on MLlib and Data Frame processing that are fully optimized for the vector processor. It is also optimized for distributed systems with multiple vector processors, and has API that is mostly the same with Spark MLlib and Data Frame. These features enables Spark developers to use multiple vector processors seamlessly from Spark and get a huge performance improvement. The performance evaluation shows that the "Frovedis" on the vector processor shows 10x to 50x speedup on several machine learning and data frame kernels compared with a Spark on Xeon Gold.
IBM Monitoring and Diagnostic Tools - GCMV 2.8Chris Bailey
Overview of IBM Monitoring and Diagnostics Tools - Garbage Collection and Memory Visualizer 2.8, which provides offline memory and Garbage Collection monitoring for Java and Node.js applications
Kubernetes is great for deploying stateless containers, but what about the big data ecosystem? Episode 3 of our Kubernetes series covers how DC/OS enables you to connect your Kubernetes-based applications to co-located big data services.
Slides cover:
1. Why persistence is challenging in distributed architectures
How DC/OS helps you take advantage of the services available in the big data ecosystem
2. How to connect Kubernetes to your data services through networking
3. How Apache Flink and Apache Spark work with Kubernetes to enable real-time data processing on DC/OS
Finding OOMS in Legacy Systems with the Syslog Telegraf PluginInfluxData
Dylan Ferreira from FuseMail will share how they use the Syslog Telegraf plugin to help them troubleshoot their systems faster and with more success. Dylan will go over how to set up Rsyslog and Telegraf to filter logs then configure Kapacitor to help you look for interesting things in your raw logs to trigger alerts to your team. He will then bring this all together in a dashboard for your teams to use.
Ingesting streaming data for analysis in apache ignite (stream sets theme)Tom Diederich
Apache Ignite provides a distributed platform for a wide variety of workloads, but often the issue is simply in getting data into the database in the first place. The wide variety of data sources and formats presents a challenge to any data engineer; in addition, 'data drift', the constant and inevitable mutation of the incoming data's structure and semantics, can break even the most well-engineered integration.
This session, aimed at data architects, data engineers and developers, will explore how we can use the open source StreamSets Data Collector to build robust data pipelines. Attendees will learn how to collect data from cloud platforms such as Amazon and Salesforce, devices, relational databases and other sources, continuously stream it to Ignite, and then use features such as Ignite's continuous queries to perform streaming analysis.
We'll start by covering the basics of reading files from disk, move on to relational databases, then look at more challenging sources such as APIs and message queues. You will learn how to:
* Build data pipelines to ingest a wide variety of data into Apache Ignite
* Anticipate and manage data drift to ensure that data keeps flowing
* Perform simple and complex ad-hoc queries in Ignite via SQL
* Write applications using Ignite to run continuous queries, combining data from multiple sources
Use Logstash and Elasticsearch to make your Logs of your cloud native app meaningful. Unit test your Logstash configuration with the Logstash Filter Verifier.
nuclio is iguazio's open source serverless project. nuclio is 100x faster, brings significant new functionality and works with data and event sources to accelerate performance and development.
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.
Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
Agriculture and Animal Care
Ken Research has an expertise in Agriculture and Animal Care sector and offer vast collection of information related to all major aspects such as Agriculture equipment, Crop Protection, Seed, Agriculture Chemical, Fertilizers, Protected Cultivators, Palm Oil, Hybrid Seed, Animal Feed additives and many more.
Our continuous study and findings in agriculture sector provide better insights to companies dealing with related product and services, government and agriculture associations, researchers and students to well understand the present and expected scenario.
Our Animal care category provides solutions on Animal Healthcare and related products and services, including, animal feed additives, vaccination
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
Gen Z and the marketplaces - let's translate their needsLaura Szabó
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
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.
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdf
Advanced kapacitor
1. Agenda: Seasoned Developers Track
WORKSHOPAGENDA
8:00 AM – 9:00 AM Breakfast
9:00 AM – 10:00 AM InfluxDB Functional Query Language (IFQL) Paul Dix
10:00 AM – 10:50 AM Writing a Telegraf Plugin Noah Crowley
10:50 AM – 11:20 AM Break
11:20 AM – 12:10 PM Using InfluxDB for Open Tracing Chris Goller
12:10 PM – 1:10 PM Lunch
1:10 PM – 2:00 PM Advanced Kapacitor Michael DeSa
2:00 PM – 2:10 PM Break
2:10 PM – 3:10 PM Setting Up InfluxData for IoT David Simmons
3:10 PM – 4:00 PM A True Story About Database Orchestration Gianluca Arbezzano
4:00 PM Pizza and Beer
2. Michael DeSa
Software Engineer
@mjdesa
Advanced Kapacitor
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.