Marina Svicevic, Milos Pavkovic, Mladen Maric, Vijeta Hingorani [Socialgist] | Transforming Email Reports into Self-Serving Dashboards & Detecting Anomalies In-Stream | InfluxDays NA 2021
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The document discusses two projects using InfluxDB: 1) Transforming email reports by consuming messages from Kafka, aggregating and storing in InfluxDB, and generating dashboards. They solved a high volume mismatch issue by adding unique tags and building a point cache. 2) Anomaly detection by collecting data from various sources, storing counts in InfluxDB, and finding anomalies like sudden drops, volume decay, and deadman events. Anomalies and alerts are stored in InfluxDB and handled via Slack, email, and SMS. Visualizations are created in Grafana by obtaining data from InfluxDB.
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On 17th May as part of InfluxDays EMEA 2021 Virtual Experience, the Technical Services team will be offering a free live InfluxDB training to the first 300 registered attendees. This will be hosted over Zoom with two main trainers and there will be assistants to help participants with the course work. The training will be recorded and made available on the InfluxDays website and the InfluxData YouTube channel.
InfluxDB + Kepware: Start Monitoring Industrial Data Quickly
Kepware (a PTC Technology) is the market leader in Industrial Connectivity platforms, and is helping companies bridge the gap between IT and OT. For the past 25 years, Kepware has developed industrial connectivity tools including over 150 industrial drivers, and supporting over 300 protocols – from traditional automation protocols such as OPC UA or OPC DA to more modern IIoT protocols like MQTT or REST. Together with InfluxDB, customers achieve quick time to value, gaining insights from their industrial time-stamped data. Developers are able to collect, process, store and analyze thousands of metrics faster! Join this webinar to learn multiple approaches to sending IIoT data to a time series database.
In this webinar, Kyle Carreau and Jay Clifford dive into:
How to use Telegraf to send OPC UA and MQTT metrics to InfluxDB
The new Kepware IoT Gateway Advanced Template features to send data directly to InfluxDB
Best practices for using InfluxDB + Kepware for industrial automation – stick around for a demo!
Tanny Ng, Nadeem Syed [WP Engine] | How WP Engine Transformed Monitoring Into...
WP Engine powers more of the top 10M sites than anyone else in WordPress. We serve 5.2 billion web requests per day! Our solution gives our customers performance, intelligence and integrations they need to drive their business forward faster. We needed a monitoring solution without single failure points to support our scale. Discover how WP Engine transformed their monitoring solution into an Observability Platform with InfluxDB, gaining better visibility and reducing infrastructure downtime.
Upgrading Made Easy: Moving to InfluxDB 2.x or InfluxDB Cloud with Cribl LogS...
Many organizations agree that migrating workloads to the cloud or to a newer version of existing tooling can result in cost savings and flexibility. A well-designed observability pipeline is often the key to a quick and painless transition, leading to positive impacts on cost optimization, data visibility, and performance. Cribl’s LogStream product helps teams implement such an observability pipeline.
In this hands-on technical discussion, the audience will learn how to leverage Cribl LogStream to successfully upgrade from InfluxDB 1.x to InfluxDB 2.x or move to InfluxDB Cloud. Join us as we walk through the pros and cons of workload migration, share architecture best practices, and give a live demo on how to combine Cribl LogStream with the latest version of InfluxDB.
Evan Kaplan [InfluxData] | InfluxDays Opening Remarks | InfluxDays EMEA 2021
This document appears to be an agenda for InfluxDays EMEA 2021, an event hosted by InfluxData. It includes opening remarks by the CEO, keynotes on InfluxDB IOx and managing dashboards/alerts. There is a networking break and sessions on integrating Telegraf/InfluxDB/Mist and an InfluxDB roadmap update. Day 2 includes sessions on Grafana, InfluxDB Cloud, IIoT monitoring and an InfluxDB community update. The document thanks InfluxData commercial customers and contributors for their support.
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Introduction to InfluxDB 2.0 & Your First Flux Query by Sonia Gupta, Develope...
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Ana-Maria Calin [InfluxData] | Migrating from OSS to InfluxDB Cloud | InfluxD...
Ana-Maria Calin [InfluxData] | Migrating from OSS to InfluxDB Cloud | InfluxD...
Paul Dix [InfluxData] | InfluxDays Opening Keynote | InfluxDays EMEA 2021
Paul Dix [InfluxData] | InfluxDays Opening Keynote | InfluxDays EMEA 2021
Tim Hall [InfluxData] | InfluxDays Keynote: InfluxDB Roadmap | InfluxDays NA ...
Tim Hall [InfluxData] | InfluxDays Keynote: InfluxDB Roadmap | InfluxDays NA ...
Sensor Data in InfluxDB by David Simmons, IoT Developer Evangelist | InfluxData
Sensor Data in InfluxDB by David Simmons, IoT Developer Evangelist | InfluxData
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Alex Nauda [Nobl9] | How Not to Build an SLO Platform | InfluxDays NA 2021
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InfluxDB + Kepware: Start Monitoring Industrial Data Quickly
Tanny Ng, Nadeem Syed [WP Engine] | How WP Engine Transformed Monitoring Into...
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Evan Kaplan [InfluxData] | InfluxDays Opening Remarks | InfluxDays EMEA 2021
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Similar to Marina Svicevic, Milos Pavkovic, Mladen Maric, Vijeta Hingorani [Socialgist] | Transforming Email Reports into Self-Serving Dashboards & Detecting Anomalies In-Stream | InfluxDays NA 2021
Building real time data-driven products
This presentation will describe how to go beyond a "Hello world" stream application and build a real-time data-driven product. We will present architectural patterns, go through tradeoffs and considerations when deciding on technology and implementation strategy, and describe how to put the pieces together. We will also cover necessary practical pieces for building real products: testing streaming applications, and how to evolve products over time.
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CouchbasetoHadoop_Matt_Michael_Justin v4
This document discusses LinkedIn's use of Kafka, Hadoop, Storm, and Couchbase in their big data pipeline. It provides an overview of each technology and how LinkedIn uses them together. Specifically, it describes how LinkedIn uses Kafka to stream data to Hadoop for analytics and report generation. It also discusses how LinkedIn uses Hadoop to pre-build and warm Couchbase buckets for improved performance. The presentation includes a use case of streaming member profile and activity data through Kafka to both Hadoop and Couchbase clusters.
Building Streaming Data Applications Using Apache Kafka
Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing.
In this talk you will learn more about:
1. A quick introduction to Kafka Core, Kafka Connect and Kafka Streams: What is and why?
2. Code and step-by-step instructions to build an end-to-end streaming data application using Apache Kafka
Building streaming data applications using Kafka*[Connect + Core + Streams] b...
Abstract:- Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing. In this talk you will learn more about: A quick introduction to Kafka Core, Kafka Connect and Kafka Streams through code examples, key concepts and key features. A reference architecture for building such Kafka-based streaming data applications. A demo of an end-to-end Kafka-based streaming data application.
2015 nov 27_thug_paytm_rt_ingest_brief_final
The document discusses Paytm Labs' transition from batch data ingestion to real-time data ingestion using Apache Kafka and Confluent. It outlines their current batch-driven pipeline and some of its limitations. Their new approach, called DFAI (Direct-From-App-Ingest), will have applications directly write data to Kafka using provided SDKs. This data will then be streamed and aggregated in real-time using their Fabrica framework to generate views for different use cases. The benefits of real-time ingestion include having fresher data available and a more flexible schema.
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This document provides an overview of Flume and Spark Streaming. It describes how Flume is used to reliably ingest streaming data into Hadoop using an agent-based architecture. Events are collected by sources, stored reliably in channels, and sent to sinks. The Flume connector allows ingested data to be processed in real-time using Spark Streaming's micro-batch architecture, where streams of data are processed through RDD transformations. This combined Flume + Spark Streaming approach provides a scalable and fault-tolerant way to reliably ingest and process streaming data.
Serverless brewbox
Lino Telera gave a presentation on serverless computing. He began with introductions and background. The presentation covered serverless concepts like Function as a Service, demonstrated building a simple microservice using AWS Lambda that interacts with S3, and discussed integrating functions with services like S3 using Boto. It also showed how functions can be called from devices using skills and discussed running serverless on-premise using OpenFaaS or Pivotal Container Service. The presentation concluded with a Q&A and thanks to sponsors.
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next Level
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
InfluxDB 2.0 Client Libraries by Noah Crowley
InfluxDB comes with a new set of client libraries to allow you to insert time series data from your applications into the new InfluxDB 2.0. Specifically, Noah will share how to use the Java client library to insert data and query it in your applications. View this InfluxDays NYC 2019 presentation to learn about InfluxDB 2.0 client libraries.
IBM Message Hub service in Bluemix - Apache Kafka in a public cloud
This talk was presented at the Kafka Meetup London meeting on 20 January 2016. You can find more information about Message Hub here: http://ibm.biz/message-hub-bluemix-catalog
Intro to InfluxDB
In this training webinar, we will walk you through the basics of InfluxDB – the purpose-built time series database. InfluxDB has everything you need from a time series platform in a single binary – a multi-tenanted time series database, UI and dashboarding tools, background processing and monitoring agent. This one-hour session will include the training and time for live Q&A.
What you will learn
Core concepts of time series databases
An overview of the InfluxDB platform
How to ingesting and query data in InfluxDB
InfluxDB Live Product Training
COOL WAYS TO GET STARTED
Join us for a live InfluxDB training to learn how to easily ingest at scale in a matter of seconds to help you build powerful time-series-based applications. Join our 45-minute demos with experts who will showcase key InfluxDB features and answer questions live from the audience.
After attending this training, attendees will be able to:
Use sample data sets to try out various visualization options
Utilize the available data ingestion methods to construct a data pipeline to InfluxDB
Leverage Notebooks to collaborate with team members
Gain best practices for InfluxDB, Telegraf and Flux
2.0 Client Libraries & Using the Java Client by Noah Crowley, Developer Advoc...
InfluxDB 2.0 brings in support for many new client libraries. In this session, Noah will walk through how to use the new Java client library to access InfluxDB 2.0. InfluxDB comes with a new set of client libraries to allow you to insert time series data from your applications into the new InfluxDB 2.0. Specifically, Noah will share how to use the Java client library to insert data and query it in your applications.
Using the Java Client Library by Noah Crowley, DevRel | InfluxData
The document discusses InfluxDB 2.0's new approach to client libraries. It notes that in 1.x, client libraries were community-led with inconsistencies, but in 2.0 the client libraries will be developed as part of the platform and will have a consistent API. The document outlines how the InfluxDB API is defined using Swagger and how code generation and guidelines help develop consistent client libraries. It provides examples of critical path client libraries being built for various languages like Go, JavaScript, and Java.
Delivering big content at NBC News with RavenDB
RavenDB is a schema-less document database that offers fully ACID transactions, fast and flexible search, replication, sharding, and a simple RESTful API wrapped by clients in a growing number of languages. In this session, we will discuss the experience of developing and maintaining a RavenDB-backed CMS for one of the largest news sites in the US.
We'll cover:
- Supporting rapid evolution of the content/data model.
- Indexing for full-text, map-reduce, geospatial and other types of search.
- Replicating and sharding across servers and data centers for high-availability.
- Deploying with no downtime.
- Handling huge traffic spikes.
Maximizing Real-Time Data Processing with Apache Kafka and InfluxDB: A Compre...
Combining Apache Kafka and InfluxDB can provide a powerful data pipeline for processing and analyzing real-time data. Kafka can be used to ingest data from various sources and stream it to InfluxDB for storage and processing. InfluxDB can then be used to analyze and visualize the data, providing insights and actionable information in real-time. This architecture can be especially useful for IoT applications, where large volumes of sensor data are generated in real-time and need to be processed and analyzed quickly. InfluxDB now offers storage in a parquet file format built on top of the Apache Arrow project that allows for querying in SQL and integration to a larger variety of visualization and analysis tools that Kafka users can now take advantage of. This talk will go into connecting the two platforms and the why, how, and what you can accomplish by doing so.
Intro to InfluxDB 2.0 and Your First Flux Query by Sonia Gupta
In this InfluxDays NYC 2019 talk, InfluxData Developer Advocate Sonia Gupta will provide an introduction to InfluxDB 2.0 and a review of the new features. She will demonstrate how to install it, insert data, and build your first Flux query.
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This document discusses Indix's evolution from its initial Data Platform 1.0 to a new Data Platform 2.0 based on the Lambda Architecture. The Lambda Architecture uses three layers - batch, serving, and speed layers - to process streaming and batch data. This provides robustness, fault tolerance, and the ability to query both real-time and batch processed views. The new system uses technologies like Spark, HBase, and Solr to implement the Lambda Architecture principles.
Stream processing on mobile networks
In this presentation I describe the architecture of two of our Flink projects. Both developed for our customers from telco industry.
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
1) StumbleUpon uses open source tools like Kafka, HBase, Hive and Pig to build a scalable big data infrastructure to process large amounts of data from its services in real-time and batch.
2) Data is collected from various services using Kafka and stored in HBase for real-time analytics. Batch processing is done using Pig and data is loaded into Hive for ad-hoc querying.
3) The infrastructure powers various applications like recommendations, ads and business intelligence dashboards.
Similar to Marina Svicevic, Milos Pavkovic, Mladen Maric, Vijeta Hingorani [Socialgist] | Transforming Email Reports into Self-Serving Dashboards & Detecting Anomalies In-Stream | InfluxDays NA 2021 (20)
Building Streaming Data Applications Using Apache Kafka
Building Streaming Data Applications Using Apache Kafka
Building streaming data applications using Kafka*[Connect + Core + Streams] b...
Building streaming data applications using Kafka*[Connect + Core + Streams] b...
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next Level
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next Level
IBM Message Hub service in Bluemix - Apache Kafka in a public cloud
IBM Message Hub service in Bluemix - Apache Kafka in a public cloud
2.0 Client Libraries & Using the Java Client by Noah Crowley, Developer Advoc...
2.0 Client Libraries & Using the Java Client by Noah Crowley, Developer Advoc...
Using the Java Client Library by Noah Crowley, DevRel | InfluxData
Using the Java Client Library by Noah Crowley, DevRel | InfluxData
Maximizing Real-Time Data Processing with Apache Kafka and InfluxDB: A Compre...
Maximizing Real-Time Data Processing with Apache Kafka and InfluxDB: A Compre...
Intro to InfluxDB 2.0 and Your First Flux Query by Sonia Gupta
Intro to InfluxDB 2.0 and Your First Flux Query by Sonia Gupta
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
More from InfluxData
Announcing InfluxDB Clustered
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 Ecosystem
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...
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 InfluxDB
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
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 Stack
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 Rust
The document is an agenda for a discussion between the CTO and founder of Ockam, Mrinal Wadhwa, and the CTO and founder of InfluxData, Paul Dix, about rewriting products using the Rust programming language. It includes an introduction of the founders, an overview of the discussion topics like why they decided to rewrite in Rust and the challenges they faced, how they got their engineers comfortable with Rust, tips they learned in the process, benefits gained from moving to Rust, and how their communities responded to the switch.
Introducing InfluxDB Cloud Dedicated
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
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...
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 ...
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 Engine
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
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 Engine
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
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- 1. A platform that provides the tools & services to connect research & analytics companies to the data which they depend upon.
- 3. © 2021 InfluxData Inc. All Rights Reserved. © 2021 InfluxData Inc. All Rights Reserved. Project Overview : Transforming Email Reports • Consume messages from Apache Kafka • Aggregate and store in Influxdb • Goal is to generate self serving dashboards • High volume mismatch issue solved by • adding unique tag • building point cache • randomizing timestamp
- 4. © 2021 InfluxData Inc. All Rights Reserved. © 2021 InfluxData Inc. All Rights Reserved. Architecture & Usage of InfluxDB
- 5. © 2021 InfluxData Inc. All Rights Reserved. © 2021 InfluxData Inc. All Rights Reserved. Self Serving Dashboard
- 6. © 2021 InfluxData Inc. All Rights Reserved. © 2021 InfluxData Inc. All Rights Reserved. Project Overview : Anomaly Detection • into a problem of counts mismatch for high traffic Kafka topics and we solved it by randomizing timestamp and building cache. • Collecting tons of data from various sources • Storing count of collected data in InfluxDB • Goal is to find anomalies in data • There are three types of anomalies: • sudden drops • volume decay • deadman
- 7. © 2021 InfluxData Inc. All Rights Reserved. © 2021 InfluxData Inc. All Rights Reserved. Usage of InfluxDB • into a problem of counts mismatch for high traffic Kafka topics and we solved it by randomizing timestamp and building cache. • Storing results of Anomaly Detection into InfluxDB • Alerts - Tick scripts • Using dynamic thresholds • Alert handling: • slack channel • email (sms)
- 8. © 2021 InfluxData Inc. All Rights Reserved. © 2021 InfluxData Inc. All Rights Reserved. Visualizing • Slack alert • which stream • anomaly strength • threshold • Visualization in Grafana • Obtaining data from InfluxDB
- 9. © 2021 InfluxData Inc. All Rights Reserved. Q&A
- 10. © 2021 InfluxData Inc. All Rights Reserved. Thank You