Why Architecting for Disaster Recovery is Important for Your Time Series Data by Capital One

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Time Series data at Capital One consists of Infrastructure, Application, and Business Process Metrics. The combination of these metrics are what the internal stakeholders rely on for observability which allows them to deliver better service and uptime for their customers, so protecting this critical data with a proven and tested recovery plan is not a “nice to have” but a “must have.” In this talk, the members of IT staff, Saravanan Krisharaju, Rajeev Tomer, and Karl Daman will share how they built a fault-tolerant solution based on InfluxEnterprise and AWS that collects and stores metrics and events. They added to this, Machine Learning, which uses the collected time series to model predictions which are then brought back into InfluxDB time series database for real-time access. This Capital One team shares the journey they took to architect and build this solution as well as plan and execute on their disaster recovery plan.

Tecnología

Confidential
Time Series Data Management Evolution at Capital
One
Building a highly resilient Enterprise Influx across multiple regions
Monitoring Intelligence
October 23, 2018

Confidential
Agenda
2
1. Influx at Capital One
2. Architecture
How Influx Architecture evolved
3. Resiliency
Complete protection against entire region failure
4. Performance Metrics
Critical Platform Metrics
5. Q & A

Confidential
Influx at Capital One
3
• Business Transaction Metrics
• Infrastructure Health metrics
• Application Performance Metrics
• Service Adoption Metrics

Confidential
Agenda
4
1. Influx at Capital One
2. Architecture
How Influx Architecture evolved
3. Resiliency
Complete protection against entire region failure
4. Performance Metrics
Critical Platform Metrics
5. Q & A

Confidential
Architecture – Gen1
5
InfluxDB
( Active )
InfluxDB
(Standby)
DR Site
Visualization
https
Primary Site
LB
Backup/Restore
Features
1. Grafana for visualization
Challenges
1. High Data Retention ( > 400 days )
2. Unstable DR Solution
Splunk
Direct API
Telegraf

Confidential
Architecture – Gen2
6
InfluxDB
( Active )
InfluxDB
(Standby)
DR Site
Visualization
Primary Site
LB
AWS S3
Data Lake
ML Model
Execution
Model
Governance
ML Model
Dev/Train
Backup/Restore
Daily batch
Features
1. Grafana for visualization
2. Raw Data Exported Daily to One Lake
3. Raw Data available for ML
Challenges
High Data Retention ( > 400 days )
2. Unstable DR Solution
A
B. ReviewC. Deploy
E
D
Splunk
Direct API
Telegraf
https

Confidential
Architecture – Current
7
InfluxDB
( Active )
InfluxDB
(Passive)
DR Site
Visualization
Primary Site
LB
AWS S3
Data Lake
ML Model
Execution
Model
Governance
ML Model
Dev/Train
AWS S3
ExportImport Mini batch
Features
1. Grafana for visualization
2. Raw Data Exported every 30 minutes
to One Lake
3. Raw Data available for ML
4. Stable DR Solution with Passive
Cluster
Challenges
High Data Retention ( > 400 days )
Unstable DR Solution
A
B. ReviewC. Deploy
E
D
Splunk
Direct API
Telegraf
https

Confidential
Agenda
8
1. Influx at Capital One
2. Architecture
How Influx Architecture evolved
3. Resiliency
Complete protection against entire region failure
4. Performance Metrics
Critical Platform Metrics
5. Q & A

Confidential
Resiliency – Region 1 Active
9
https
D
M
D
M
D
M
Region 1 ( Active)
Zone 1 Zone 2 Zone 3
LB
S3
Cluster 1
D
M
D
M
D
M
Region 2
(DR)
Zone 1 Zone 2 Zone 3
LB
S3
Cluster 2
Route53
(DNS Switch)
Cross Region
Replication
Admin
A
ASGASG
M Meta Node D Data Node A Admin
Node
3
A
Route53
(DNS Switch)
1
4
2
Splunk
Direct API
Telegraf
All Traffic routed to Region 11
Influx Export Script every 15 min2
Data Replicated to Region 23
Influx Import Script every 15 min4

Confidential
Resiliency – Region 2 Active
10
https
D
M
D
M
D
M
Region 1 ( DR)
Zone 1 Zone 2 Zone 3
LB
S3
Cluster 1
D
M
D
M
D
M
Region 2 (Active)
Zone 1 Zone 2 Zone 3
LB
S3
Cluster 2
Route53
(DNS Switch)
Cross Region
Replication
Admin
A
ASGASG
M Meta Node D Data Node A Admin
Node
3
A
Route53
(DNS Switch)
1
2
4
Splunk
Direct API
Telegraf
All Traffic routed to Region 21
Influx Export Script every 15 min2
Data Replicated to Region 13
Influx Import Script every 15 min4

Confidential
Resiliency – Export/Import
11
Export*
Import*
* High level pseudo code , Not the actual code

Confidential
Agenda
12
1. Influx at Capital One
2. Architecture
How Influx Architecture evolved
3. Resiliency
Complete protection against entire region failure
4. Performance Metrics
Critical Platform Metrics
5. Q & A

Confidential
Performance Metrics
Performance Metrics collected using Telegraf

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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

How Teréga Replaces Legacy Data Historians with InfluxDB, AWS and IO-Base

InfluxData

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

Build an Edge-to-Cloud Solution with the MING Stack

InfluxData

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

Meet the Founders: An Open Discussion About Rewriting Using Rust

InfluxData

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.

Introducing InfluxDB Cloud Dedicated

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.

Gain Better Observability with OpenTelemetry and InfluxDB

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

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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

How Delft University's Engineering Students Make Their EV Formula-Style Race ...

InfluxData

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

Introducing InfluxDB’s New Time Series Database Storage Engine

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.

Start Automating InfluxDB Deployments at the Edge with balena

InfluxData

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

Understanding InfluxDB’s New Storage Engine

InfluxData

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

Streamline and Scale Out Data Pipelines with Kubernetes, Telegraf, and InfluxDB

InfluxData

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Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

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Passkeys: Developing APIs to enable passwordless authentication Cody Salas, Sr Developer Advocate | Solutions Architect - Yubico Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

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Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

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Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

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debabhi2

When you’re building (micro)services, you have lots of framework options. Spring Boot is no doubt a popular choice. But there’s more! Take Quarkus, a framework that’s considered the rising star for Kubernetes-native Java. It always depends on what's best for your situation, but how to choose the best solution if you're comparing 2 frameworks? Both Spring Boot and Quarkus have their positives and negatives. Let us compare the two by live coding a couple of common use cases in Spring Boot and Quarkus. After this talk, you’ll be ready to get started with Quarkus yourself, and know when to select Quarkus or Spring Boot.

Spring Boot vs Quarkus the ultimate battle - DevoxxUK

Jago de Vreede

Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

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Architecting Cloud Native Applications

WSO2

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

UiPathCommunity

The action of the next cyber saga takes place in the mystical lands of the Asia-Pacific region, where the main characters began their digital activities in the middle of 2021 and qualitatively strengthened it in 2022. Corporate espionage, document theft, audio recordings, and data leaks from messaging platforms were all a matter of one day for Dark Pink. Their geographical focus may have started in the Asia-Pacific region, but their ambitions knew no bounds, targeting a European government ministry in a bold move to expand their portfolio. Their victim profile was as diverse as a UN meeting, targeting military organizations, government agencies, and even a religious organization. Because discrimination is not a fashionable agenda. In the world of cybercrime, they serve as a reminder that sometimes the most serious threats come in the most unassuming packages with a pink bow.

Cyberprint. Dark Pink Apt Group [EN].pdf

Overkill Security

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sudhanshuwaghmare1

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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MINDCTI Revenue Release Quarter One 2024

MIND CTI

Why Architecting for Disaster Recovery is Important for Your Time Series Data by Capital One

1. Confidential Time Series Data Management Evolution at Capital One Building a highly resilient Enterprise Influx across multiple regions Monitoring Intelligence October 23, 2018

2. Confidential Agenda 2 1. Influx at Capital One 2. Architecture How Influx Architecture evolved 3. Resiliency Complete protection against entire region failure 4. Performance Metrics Critical Platform Metrics 5. Q & A

3. Confidential Influx at Capital One 3 • Business Transaction Metrics • Infrastructure Health metrics • Application Performance Metrics • Service Adoption Metrics

4. Confidential Agenda 4 1. Influx at Capital One 2. Architecture How Influx Architecture evolved 3. Resiliency Complete protection against entire region failure 4. Performance Metrics Critical Platform Metrics 5. Q & A

5. Confidential Architecture – Gen1 5 InfluxDB ( Active ) InfluxDB (Standby) DR Site Visualization https Primary Site LB Backup/Restore Features 1. Grafana for visualization Challenges 1. High Data Retention ( > 400 days ) 2. Unstable DR Solution Splunk Direct API Telegraf

6. Confidential Architecture – Gen2 6 InfluxDB ( Active ) InfluxDB (Standby) DR Site Visualization Primary Site LB AWS S3 Data Lake ML Model Execution Model Governance ML Model Dev/Train Backup/Restore Daily batch Features 1. Grafana for visualization 2. Raw Data Exported Daily to One Lake 3. Raw Data available for ML Challenges High Data Retention ( > 400 days ) 2. Unstable DR Solution A B. ReviewC. Deploy E D Splunk Direct API Telegraf https

7. Confidential Architecture – Current 7 InfluxDB ( Active ) InfluxDB (Passive) DR Site Visualization Primary Site LB AWS S3 Data Lake ML Model Execution Model Governance ML Model Dev/Train AWS S3 ExportImport Mini batch Features 1. Grafana for visualization 2. Raw Data Exported every 30 minutes to One Lake 3. Raw Data available for ML 4. Stable DR Solution with Passive Cluster Challenges High Data Retention ( > 400 days ) Unstable DR Solution A B. ReviewC. Deploy E D Splunk Direct API Telegraf https

8. Confidential Agenda 8 1. Influx at Capital One 2. Architecture How Influx Architecture evolved 3. Resiliency Complete protection against entire region failure 4. Performance Metrics Critical Platform Metrics 5. Q & A

9. Confidential Resiliency – Region 1 Active 9 https D M D M D M Region 1 ( Active) Zone 1 Zone 2 Zone 3 LB S3 Cluster 1 D M D M D M Region 2 (DR) Zone 1 Zone 2 Zone 3 LB S3 Cluster 2 Route53 (DNS Switch) Cross Region Replication Admin A ASGASG M Meta Node D Data Node A Admin Node 3 A Route53 (DNS Switch) 1 4 2 Splunk Direct API Telegraf All Traffic routed to Region 11 Influx Export Script every 15 min2 Data Replicated to Region 23 Influx Import Script every 15 min4

10. Confidential Resiliency – Region 2 Active 10 https D M D M D M Region 1 ( DR) Zone 1 Zone 2 Zone 3 LB S3 Cluster 1 D M D M D M Region 2 (Active) Zone 1 Zone 2 Zone 3 LB S3 Cluster 2 Route53 (DNS Switch) Cross Region Replication Admin A ASGASG M Meta Node D Data Node A Admin Node 3 A Route53 (DNS Switch) 1 2 4 Splunk Direct API Telegraf All Traffic routed to Region 21 Influx Export Script every 15 min2 Data Replicated to Region 13 Influx Import Script every 15 min4

11. Confidential Resiliency – Export/Import 11 Export* Import* * High level pseudo code , Not the actual code

12. Confidential Agenda 12 1. Influx at Capital One 2. Architecture How Influx Architecture evolved 3. Resiliency Complete protection against entire region failure 4. Performance Metrics Critical Platform Metrics 5. Q & A

13. Confidential Performance Metrics Performance Metrics collected using Telegraf

14. Confidential Q & A

Why Architecting for Disaster Recovery is Important for Your Time Series Data by Capital One

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