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This document summarizes the growth and strategy of InfluxData, a time-series database company. It discusses how InfluxData was founded in 2013, has grown to over 450 customers with 175,000 instances in use today. It outlines InfluxData's platform strategy to be the platform of choice for metrics and event workloads across infrastructure, IoT, and business applications. The document characterizes InfluxData's customers and their use cases, and highlights some example customers including Bethesda Games, Wayfair, and Playtech. It discusses InfluxData's core focus on developer happiness, being purpose-built for time-series data, and commitment to open source.
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Auto-Incident Creation and Routing. Automatic incident creation and routing using alert escalation policies to auto-assign incidents based on prior alert, incident, and notification data. Machine learning-driven alert escalation uses specific learned patterns (assignee groups, business impact, urgency, and priority) to route incident assignments for different types of alerts.
Augmented Training for Inference Models. OpsRamp’s machine learning-based inference models correlate alerts linked by a common cause using historical alert data. Opsramp’s OpsQ now allows users to augment these models with additional user-provided training data. With such augmented training, IT operations teams can bootstrap OpsQ to recognize alert sequences that are uncommon in everyday operations, but important to identify when they occur.
Frequency-Driven Alert Escalation. OpsQ now supports policies to escalate alerts based on how often an alert has recently occurred. With frequency-based alerting, operations teams can filter out alerts that flap only occasionally and escalate alerts that flap repeatedly.
Cloud Native Monitoring. Discover and monitor Kubernetes environments across on-prem and cloud services like Azure Kubernetes Services, Google Kubernetes Engine, and Amazon Elastic Container Service for Kubernetes.
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Business breakout during Confluent’s streaming event in Munich, presented by Lyndon Hedderly, Director of Customer Solutions at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Alteryx and Tableau: Iron Mountain's Sherpa to business insight
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Big Data 'State of the Union' (BDT101) | AWS re:Invent 2013
Big Data is more than petabytes and capacity. It is the opportunity to use data to your advantage to make smart decisions that increase productivity and grow your business. In this session, you'll learn about the latest advancements in data analytics, databases, storage, and high performance computing (HPC) at AWS and discover how to put data to work in your own organization.
Building a Modern FinTech Big Data Infrastructure
The cloud is now the first choice for large-scale analytics, but organizations that have sunk investment into Hadoop on-premises are also challenged with maintaining operations. This can make a move to modern analytics platforms like Spark difficult or impossible. Learn about innovations for large-scale migration that can take full advantage of cloud-based analytics without disrupting operations.
new-D2
Radical Stack is an end-to-end architecture that offers business analysts access to data and the ability to ask new questions quickly without needing additional personnel for programming or administration. Traditional ELT and data warehousing approaches require months of work from programmers and administrators to prepare the data for business analysts, who then have limited control and must repeat the process to get new insights. Radical Stack uses Hadoop technologies like SQL on Hadoop, MapReduce, Hive and Pig to make data easily accessible to data administrators, data scientists and business analysts within minutes without additional hiring, allowing for faster collaboration and exploration of the data.
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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:
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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.
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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:
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Their entire system architecture — including InfluxDB Cloud, Grafana, Kafka, and DigitalOcean
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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
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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:
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Tips and tricks for using Node-RED and InfluxDB together
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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
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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
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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
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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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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
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Scott Anderson [InfluxData] | New & Upcoming Flux Features | InfluxDays 2022
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and functionality to your data workflow—polymorphic
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This document contains the agenda for Day 2 of InfluxDays 2022, which includes:
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- Sessions on the InfluxDB developer console, APIs, client libraries, getting involved in the community, accelerating time to awesome with InfluxDB University, and tips for analyzing IoT data with InfluxDB.
- Closing thoughts from Zoe Steinkamp and Jay Clifford, as well as
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
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一比一原版新西兰奥塔哥大学毕业证（otago毕业证）如何办理
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【业务选择办理准则】
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二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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[To download this presentation, visit:
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6. Doblin’s Ten Types of Innovation
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To download this presentation, visit:
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https://www.britannica.com/event/Expo-Shanghai-2010
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Roadshow Chicago - Introduction
- 1. Mark Herring, CMO November, 2018 Welcome!
- 2. Thank you
- 3. Rahji Abdurehman Robert Abraham Colby Ackerfield Nikita Petrov Jonathan Phillips Evan Phoenix Kushal Halder Martin Hamrle Sangyun Han Thanks to Our Contributors
- 4. Our Journey to Today
- 5. Our Journey to Today… Founded Apr 2013 InfluxDB Nov. 2013 Series A Dec 2014 InfluxData Platform Dec 2015 Series B Influx Enterprise Sep 2016 450 Customers 175K Instances Today Series C Feb 2016
- 6. Thanks to the Community InfluxDB Growth InfluxDB Announced Nov 2013 14,656 309 ~175,000+ databases in use today
- 7. Time Series and InfluxData Time Series fastest growing category InfluxData is the leading time-series database Trend of the Last 24 Months DB Engine Scores of the Last 24 Months Source: DB-Engines Source: DB-Engines
- 8. © 2018 InfluxData. All rights reserved.8 Platform Strategy: Be The Platform of Choice for All Metrics and Event Workloads Application, Custom Logs & Traces Business Metrics Infrastructure & Application Metrics IoT Sensor Events Common Metrics and Events Platform
- 9. Strong Customer Momentum Infrastructure/ Application/ Business Process Monitoring 45% IoT Monitoring 35% Real-Time Analytics 20%
- 10. Characterization of Use Cases ü Mini-historian – Equinor and A&S Energie ü Device Monitoring (Consumer IoT) – Tesla and Tado ü Network Monitoring – NFL Network and IBM ü Infrastructure Monitoring – Expedia and Nordstrom ü SaaS Monitoring and Control – Mulesoft and SAP ü Game interaction Monitoring – Bethesda and King.com ü …
- 11. Characterization of Need ü “Full stack” Monitoring ü Metrics First Approach ü Cost effective ü Flexible
- 12. Some Customers…
- 13. Bethesda Games uses InfluxCloud to collect user metrics on their newest game, Fallout 76. They are currently offering this on a limited release to XBox users. They have 80,000 servers, reporting on 10s intervals at their peak when they released this to the general public on Nov 14, 2018.
- 14. Wayfair is using InfluxData to monitor system metrics & events across datacenters, and Real User monitoring (RUM) to understand user experience on their e- commerce site. The goal is to marry these with business process events and provide better business insight and competitive advantage influxdata.com/customer/wayfair/
- 15. InfluxDB is a major component to providing observability across the board at Playtech. It is better to have simpler and faster monitoring systems with tools for post hoc analysis and avoid “magic” systems that try to automatically detect causality. Aleksandr Tavgen Technical Archtiect, Playtech influxdata.com/customer/playtech/
- 16. A&S Energie is a Belgian biomass plant that supplies electricity by burning non-recyclable wood. They replaced their existing solution with Factry Historian, powered by open source InfluxDB. This allowed A&S Energie to increase use of the process data to make their staff more efficient as well as find failures faster. influxdata.com/partners/factry/
- 17. Our Core Focus
- 18. Time To Awesome ü Focus on Developers ü Purpose Built ü Metrics First Approach ü Ease of Scale out
- 19. begin developer_happiness rescue easy_to_get_started give_developers open_source, no_dependency, cloud rescue easy_to_scale give_developers no_dependency, cloud rescue fast_time_to_awesome give_developers time_functions, compression, downsample rescue recognition_and_help give_developers meetups, community, meetups, swag … PRINCIPLE: DEVELOPER HAPPINESS
- 21. PRINCIPLE: COMMITMENT TO OPEN SOURCE
- 22. Take our quick survey Tell us what you think! https://www.surveymonkey.com/r/3KNL53Z Denver Help us build the Time Series Chicago Meetup
- 23. Thank you