This document introduces the TICK stack, which is a collection of open source software tools for collecting, processing, storing, and visualizing metrics and events. It summarizes the main components: Telegraf collects metrics from servers and services and writes them to InfluxDB; InfluxDB is a time series database that stores metrics; Chronograf provides visualization of metrics stored in InfluxDB; and Kapacitor processes data from InfluxDB to perform tasks like anomaly detection and alerting. Examples are provided of how these tools can be used together in a workflow to monitor systems and applications.
InfluxDB is an open source time series database written in Go that stores metric data and performs real-time analytics. It has no external dependencies. InfluxDB stores data as time series with measurements, tags, and fields. Data is written using a line protocol and can be visualized using Grafana, an open source metrics dashboard.
A quick walk through InfluxDB and TICK Stack.
Telegraf (Collect), InfluxDB (Store), Chrongraf (Visualize), and Kapacitor (Process).
- What is time series data?
- Why TICK Stack?
- Where could TICK Stack be used?
Influx/Days 2017 San Francisco | Dan Cech InfluxData
DATA VISUALIZATION & ALERTING WITH GRAFANA
Grafana is the leading graph and dashboard builder for visualizing time series, which is a great tool for visual monitoring of InfluxData. This session will provide an intro to Grafana and talk about adding data sources, creating dashboards and getting the most out of your data visualization. The talk will look into some new features Grafana has to offer, as well as explain why different graphs are important and specifically how you can use them to analyze data performance and troubleshoot operational issues.
Developing Ansible Dynamic Inventory Script - Nov 2017Ahmed AbouZaid
A session about my experience with writing an external inventory script from scratch for "Netbox" (IPAM and DCIM tool from DigitalOcean network engineering team) and push it to upstream to became an official inventory script.
Repo:
https://github.com/AAbouZaid/netbox-as-ansible-inventory
The "Dynamic inventory" is one of nice features in Ansible, where you can use an external service as inventory for Ansible instead the basic text-based ini file. So you can use AWS EC2 as inventory of your hosts, or maybe OpenStack, or whatever ... you actually can use any source inventory for Ansible, and you can write your own "External Inventory Script".
Measure your app internals with InfluxDB and Symfony2Corley S.r.l.
This document discusses using InfluxDB, a time-series database, to measure application internals in Symfony. It describes sending data from a Symfony app to InfluxDB using its PHP client library, and visualizing the data with Grafana dashboards. Key steps include setting up the InfluxDB client via dependency injection, dispatching events from controllers, listening for them to send data to InfluxDB, and building Grafana dashboards to view measurements over time.
Grafana is an open source analytics and monitoring tool that uses InfluxDB to store time series data and provide visualization dashboards. It collects metrics like application and server performance from Telegraf every 10 seconds, stores the data in InfluxDB using the line protocol format, and allows users to build dashboards in Grafana to monitor and get alerts on metrics. An example scenario is using it to collect and display load time metrics from a QA whitelist VM.
This document introduces the TICK stack, which is a collection of open source software tools for collecting, processing, storing, and visualizing metrics and events. It summarizes the main components: Telegraf collects metrics from servers and services and writes them to InfluxDB; InfluxDB is a time series database that stores metrics; Chronograf provides visualization of metrics stored in InfluxDB; and Kapacitor processes data from InfluxDB to perform tasks like anomaly detection and alerting. Examples are provided of how these tools can be used together in a workflow to monitor systems and applications.
InfluxDB is an open source time series database written in Go that stores metric data and performs real-time analytics. It has no external dependencies. InfluxDB stores data as time series with measurements, tags, and fields. Data is written using a line protocol and can be visualized using Grafana, an open source metrics dashboard.
A quick walk through InfluxDB and TICK Stack.
Telegraf (Collect), InfluxDB (Store), Chrongraf (Visualize), and Kapacitor (Process).
- What is time series data?
- Why TICK Stack?
- Where could TICK Stack be used?
Influx/Days 2017 San Francisco | Dan Cech InfluxData
DATA VISUALIZATION & ALERTING WITH GRAFANA
Grafana is the leading graph and dashboard builder for visualizing time series, which is a great tool for visual monitoring of InfluxData. This session will provide an intro to Grafana and talk about adding data sources, creating dashboards and getting the most out of your data visualization. The talk will look into some new features Grafana has to offer, as well as explain why different graphs are important and specifically how you can use them to analyze data performance and troubleshoot operational issues.
Developing Ansible Dynamic Inventory Script - Nov 2017Ahmed AbouZaid
A session about my experience with writing an external inventory script from scratch for "Netbox" (IPAM and DCIM tool from DigitalOcean network engineering team) and push it to upstream to became an official inventory script.
Repo:
https://github.com/AAbouZaid/netbox-as-ansible-inventory
The "Dynamic inventory" is one of nice features in Ansible, where you can use an external service as inventory for Ansible instead the basic text-based ini file. So you can use AWS EC2 as inventory of your hosts, or maybe OpenStack, or whatever ... you actually can use any source inventory for Ansible, and you can write your own "External Inventory Script".
Measure your app internals with InfluxDB and Symfony2Corley S.r.l.
This document discusses using InfluxDB, a time-series database, to measure application internals in Symfony. It describes sending data from a Symfony app to InfluxDB using its PHP client library, and visualizing the data with Grafana dashboards. Key steps include setting up the InfluxDB client via dependency injection, dispatching events from controllers, listening for them to send data to InfluxDB, and building Grafana dashboards to view measurements over time.
Grafana is an open source analytics and monitoring tool that uses InfluxDB to store time series data and provide visualization dashboards. It collects metrics like application and server performance from Telegraf every 10 seconds, stores the data in InfluxDB using the line protocol format, and allows users to build dashboards in Grafana to monitor and get alerts on metrics. An example scenario is using it to collect and display load time metrics from a QA whitelist VM.
Virtual training Intro to InfluxDB & TelegrafInfluxData
How to setup InfluxDB & Telgraf to pull metrics into your InfluxDB. An introduction to querying data with InfluxQL. Learn more and download the open source version of Telegraf now: https://www.influxdata.com/time-series-platform/telegraf/
InfluxDB is an open source time series database that is written in Go. It is designed for storing large amounts of time series data and providing rapid query results. Data is stored in measurements, which contain tags, fields, and a timestamp. Queries use a SQL-like language to retrieve and aggregate time series data. Continuous queries allow data to be resampled and written to a different measurement on a periodic basis.
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
Intro to InfluxDB 2.0 and Your First Flux Query by Sonia GuptaInfluxData
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.
InfluxDB 2.0: Dashboarding 101 by David G. SimmonsInfluxData
InfluxDB 2.0 has some new dashboarding and querying capabilities that will make using a time series database even easier. This InfluxDays NYC 2019 presentation presented by David G. Simmons (Senior Developer Evangelist at InfluxData), walks you through how to set up your first dashboard.
Grafana 7.0 introduces new features including a tracing data viewer that allows users to view and correlate metrics, logs, and traces across data sources. It also includes new data transformations that allow users to transform data before it is queried. Additionally, Grafana 7.0 features a new plugin architecture that splits core functionality into packages and supports official backend plugins running as a separate process.
This document discusses InfluxDB, an open-source time series database. It stores time stamped numeric data in structures called time series. The document provides an overview of time series data, describes how to install and use InfluxDB, and discusses features like its HTTP API, client libraries, Grafana integration for visualization, and benchmark results showing it has better performance for time series data than other databases.
This document provides an overview of Kafka including its architecture, key concepts, and performance tuning. It describes how Kafka is a distributed streaming platform popular for use cases like logging, metrics, and messaging. The architecture explained includes Kafka brokers that make up clusters, Zookeeper for coordination, producers that publish messages, consumers that subscribe to messages, and topics for categorizing data. It also covers message delivery guarantees, monitoring tools, and ways to optimize producer, broker, consumer, and JVM performance such as configuration settings for throughput, latency, and durability.
Getting started with influx Db and Grafana Installation GuideSoumil Shahsoumil
This document discusses InfluxDB, an open source time series database, and Grafana, an open source analytics and visualization suite commonly used with InfluxDB. It provides instructions for installing InfluxDB and Grafana on Mac OS using Brew, and installing the Python plugin for InfluxDB.
This document discusses using Grafana to visualize test data in real time. It provides an introduction to Grafana and monitoring. Test data can be represented as time series data and metrics can be built around test runtime and results. Grafana allows querying and visualizing metrics from various sources. The document demonstrates collecting test class and method results as time series data points in InfluxDB and then querying and visualizing the results in Grafana dashboards. This provides real-time monitoring of test data.
How Sensor Data Can Help Manufacturers Gain Insight to Reduce Waste, Energy C...InfluxData
In this webinar, learn how a long-time Industrial IT Consultant helps his customer make the leap into providing visibility of their processes to everyone in the plant. This journey led to the discovery of untapped opportunity to improve operations, reduce energy consumption, and minimize plant downtime. The collection of data from the individual sensors has led to powerful Grafana dashboards shared across the organization.
This document discusses using InfluxDB and Kubernetes for monitoring. It provides an overview of deploying InfluxDB and Chronograf using Helm charts. It also describes monitoring Kubernetes infrastructure by deploying Telegraf as a DaemonSet to collect metrics from nodes. Additionally, it covers monitoring applications by deploying Telegraf as a single pod to scrape metrics or as a sidecar. Lastly, it discusses future plans for an InfluxData operator and running InfluxEnterprise outside Kubernetes clusters.
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...Hakka Labs
In this presentation, Paul introduces InfluxDB, a distributed time series database that he open sourced based on the backend infrastructure at Errplane. He talks about why you'd want a database specifically for time series and he covers the API and some of the key features of InfluxDB, including:
• Stores metrics (like Graphite) and events (like page views, exceptions, deploys)
• No external dependencies (self contained binary)
• Fast. Handles many thousands of writes per second on a single node
• HTTP API for reading and writing data
• SQL-like query language
• Distributed to scale out to many machines
• Built in aggregate and statistics functions
• Built in downsampling
Presented at Stream Processing Meetup (7/19/2018)(https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/251481797/).
At Uber, we operate 20+ Kafka clusters to collect system and application logs as well as event data from rider and driver apps. We need a Kafka replication solution to replicate data between Kafka clusters across multiple data centers for different purposes. This talk will introduce the history behind uReplicator and the high level architecture. As the original uReplicator ran into scalability challenges and operational overhead as the scale of Kafka clusters increased, we built the Federated uReplicator which addressed above issues and provide an extensible architecture for further scaling.
Why Architecting for Disaster Recovery is Important for Your Time Series Data...InfluxData
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.
This document discusses the need for a time series database and introduces OpenTSDB as an option. Some key points:
- Time series data is useful for analyzing metrics and patterns over time but is currently scattered across different databases.
- OpenTSDB is an open source time series database that can store trillions of data points, scale using HBase, and never loses precision.
- It is optimized for write throughput and can handle thousands of data points per second. Reads depend on the cardinality of metrics but it supports time-based queries.
- OpenTSDB uses HBase under the hood and stores tags with metrics to allow for flexible filtering of time series data without affecting performance.
How Sysbee Manages Infrastructures and Provides Advanced Monitoring by Using ...InfluxData
Discover how Sysbee helps organizations bring DevOps culture to small and medium enterprises. Their team helps their customers by improving stability, security, scalability — by providing cost-effective IT infrastructure. Learn how monitoring everything can improve your processes and simplify debugging!
Sysbee’s introspection on monitoring tools over the years
How TSDB’s, and specifically InfluxDB, fits into improving observability
Their approach to using the TICK Stack to improve the web hosting industry
GrafanaCon 2015 - http://grafanacon.org/
Tobias will be giving an overview of Prometheus, an open-source monitoring system with a multi-dimensional label system, expressive query language and dashboard editor called PromDash. Learn about the highlights and differences of PromDash compared to Grafana and discuss the options to make Grafana the primary dashboard editor of the Prometheus project.
Custom DevOps Monitoring System in MelOn (with InfluxDB + Telegraf + Grafana)Seungmin Yu
2016년도 데이터야놀자에서 발표한 자료입니다.
멜론에서 InfluxDB + Telegraf + Grafana 조합으로 모니터링 시스템을 구축하고 활용한 사례를 발표한 내용입니다. 다양한 메트릭데이터와 DevOps 측면의 활용 가치에 대해서도 생각해 볼 수 있을 것 같습니다.
This document discusses tools for working with time series data, including InfluxDB for storing time series data, Telegraf for collecting metrics, and Kapacitor for processing and alerting on metrics. It provides an overview of how to install and use InfluxDB, describes its HTTP and UDP APIs, query language, and advantages over alternatives. Continuous queries, input and output plugins for Telegraf, and alerting capabilities of Kapacitor are also summarized. The document encourages representing log lines and other time-indexed data as compact time series for scalability.
Virtual training Intro to InfluxDB & TelegrafInfluxData
How to setup InfluxDB & Telgraf to pull metrics into your InfluxDB. An introduction to querying data with InfluxQL. Learn more and download the open source version of Telegraf now: https://www.influxdata.com/time-series-platform/telegraf/
InfluxDB is an open source time series database that is written in Go. It is designed for storing large amounts of time series data and providing rapid query results. Data is stored in measurements, which contain tags, fields, and a timestamp. Queries use a SQL-like language to retrieve and aggregate time series data. Continuous queries allow data to be resampled and written to a different measurement on a periodic basis.
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
Intro to InfluxDB 2.0 and Your First Flux Query by Sonia GuptaInfluxData
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.
InfluxDB 2.0: Dashboarding 101 by David G. SimmonsInfluxData
InfluxDB 2.0 has some new dashboarding and querying capabilities that will make using a time series database even easier. This InfluxDays NYC 2019 presentation presented by David G. Simmons (Senior Developer Evangelist at InfluxData), walks you through how to set up your first dashboard.
Grafana 7.0 introduces new features including a tracing data viewer that allows users to view and correlate metrics, logs, and traces across data sources. It also includes new data transformations that allow users to transform data before it is queried. Additionally, Grafana 7.0 features a new plugin architecture that splits core functionality into packages and supports official backend plugins running as a separate process.
This document discusses InfluxDB, an open-source time series database. It stores time stamped numeric data in structures called time series. The document provides an overview of time series data, describes how to install and use InfluxDB, and discusses features like its HTTP API, client libraries, Grafana integration for visualization, and benchmark results showing it has better performance for time series data than other databases.
This document provides an overview of Kafka including its architecture, key concepts, and performance tuning. It describes how Kafka is a distributed streaming platform popular for use cases like logging, metrics, and messaging. The architecture explained includes Kafka brokers that make up clusters, Zookeeper for coordination, producers that publish messages, consumers that subscribe to messages, and topics for categorizing data. It also covers message delivery guarantees, monitoring tools, and ways to optimize producer, broker, consumer, and JVM performance such as configuration settings for throughput, latency, and durability.
Getting started with influx Db and Grafana Installation GuideSoumil Shahsoumil
This document discusses InfluxDB, an open source time series database, and Grafana, an open source analytics and visualization suite commonly used with InfluxDB. It provides instructions for installing InfluxDB and Grafana on Mac OS using Brew, and installing the Python plugin for InfluxDB.
This document discusses using Grafana to visualize test data in real time. It provides an introduction to Grafana and monitoring. Test data can be represented as time series data and metrics can be built around test runtime and results. Grafana allows querying and visualizing metrics from various sources. The document demonstrates collecting test class and method results as time series data points in InfluxDB and then querying and visualizing the results in Grafana dashboards. This provides real-time monitoring of test data.
How Sensor Data Can Help Manufacturers Gain Insight to Reduce Waste, Energy C...InfluxData
In this webinar, learn how a long-time Industrial IT Consultant helps his customer make the leap into providing visibility of their processes to everyone in the plant. This journey led to the discovery of untapped opportunity to improve operations, reduce energy consumption, and minimize plant downtime. The collection of data from the individual sensors has led to powerful Grafana dashboards shared across the organization.
This document discusses using InfluxDB and Kubernetes for monitoring. It provides an overview of deploying InfluxDB and Chronograf using Helm charts. It also describes monitoring Kubernetes infrastructure by deploying Telegraf as a DaemonSet to collect metrics from nodes. Additionally, it covers monitoring applications by deploying Telegraf as a single pod to scrape metrics or as a sidecar. Lastly, it discusses future plans for an InfluxData operator and running InfluxEnterprise outside Kubernetes clusters.
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...Hakka Labs
In this presentation, Paul introduces InfluxDB, a distributed time series database that he open sourced based on the backend infrastructure at Errplane. He talks about why you'd want a database specifically for time series and he covers the API and some of the key features of InfluxDB, including:
• Stores metrics (like Graphite) and events (like page views, exceptions, deploys)
• No external dependencies (self contained binary)
• Fast. Handles many thousands of writes per second on a single node
• HTTP API for reading and writing data
• SQL-like query language
• Distributed to scale out to many machines
• Built in aggregate and statistics functions
• Built in downsampling
Presented at Stream Processing Meetup (7/19/2018)(https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/251481797/).
At Uber, we operate 20+ Kafka clusters to collect system and application logs as well as event data from rider and driver apps. We need a Kafka replication solution to replicate data between Kafka clusters across multiple data centers for different purposes. This talk will introduce the history behind uReplicator and the high level architecture. As the original uReplicator ran into scalability challenges and operational overhead as the scale of Kafka clusters increased, we built the Federated uReplicator which addressed above issues and provide an extensible architecture for further scaling.
Why Architecting for Disaster Recovery is Important for Your Time Series Data...InfluxData
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.
This document discusses the need for a time series database and introduces OpenTSDB as an option. Some key points:
- Time series data is useful for analyzing metrics and patterns over time but is currently scattered across different databases.
- OpenTSDB is an open source time series database that can store trillions of data points, scale using HBase, and never loses precision.
- It is optimized for write throughput and can handle thousands of data points per second. Reads depend on the cardinality of metrics but it supports time-based queries.
- OpenTSDB uses HBase under the hood and stores tags with metrics to allow for flexible filtering of time series data without affecting performance.
How Sysbee Manages Infrastructures and Provides Advanced Monitoring by Using ...InfluxData
Discover how Sysbee helps organizations bring DevOps culture to small and medium enterprises. Their team helps their customers by improving stability, security, scalability — by providing cost-effective IT infrastructure. Learn how monitoring everything can improve your processes and simplify debugging!
Sysbee’s introspection on monitoring tools over the years
How TSDB’s, and specifically InfluxDB, fits into improving observability
Their approach to using the TICK Stack to improve the web hosting industry
GrafanaCon 2015 - http://grafanacon.org/
Tobias will be giving an overview of Prometheus, an open-source monitoring system with a multi-dimensional label system, expressive query language and dashboard editor called PromDash. Learn about the highlights and differences of PromDash compared to Grafana and discuss the options to make Grafana the primary dashboard editor of the Prometheus project.
Custom DevOps Monitoring System in MelOn (with InfluxDB + Telegraf + Grafana)Seungmin Yu
2016년도 데이터야놀자에서 발표한 자료입니다.
멜론에서 InfluxDB + Telegraf + Grafana 조합으로 모니터링 시스템을 구축하고 활용한 사례를 발표한 내용입니다. 다양한 메트릭데이터와 DevOps 측면의 활용 가치에 대해서도 생각해 볼 수 있을 것 같습니다.
This document discusses tools for working with time series data, including InfluxDB for storing time series data, Telegraf for collecting metrics, and Kapacitor for processing and alerting on metrics. It provides an overview of how to install and use InfluxDB, describes its HTTP and UDP APIs, query language, and advantages over alternatives. Continuous queries, input and output plugins for Telegraf, and alerting capabilities of Kapacitor are also summarized. The document encourages representing log lines and other time-indexed data as compact time series for scalability.
Beautiful Monitoring With Grafana and InfluxDBleesjensen
Query your data streams with the time series database InfluxDB and then visualize the results with stunning Grafana dashboards. Quick and easy to set up. Fully scalable to millions of metrics per second.
Implantação de nova ferramenta de monitoração - Sensu - para coleta de checks, métricas (para geração de gráficos), envio de alertas em diversos canais.
The document provides an overview of roles, artifacts, meetings, and processes in Scrum. The Scrum team is cross-functional and self-organizing. Artifacts include the Product Backlog, Sprint Backlog, and Burndown Chart. Meetings include Sprint Planning, Daily Scrum, Sprint Review, and Retrospective. The Product Owner prioritizes the Product Backlog and Scrum Master facilitates the process.
Scrum is a framework for managing product development that divides work into sprints. Key roles include the Product Owner who manages the product backlog, the Development Team who does the work, and the Scrum Master who facilitates the process. The team holds regular stand-up meetings, sprint planning meetings, sprint reviews, and retrospectives. They track progress using artifacts like the product backlog, sprint backlog, and burndown charts. The framework aims to be transparent, inspect progress frequently, and adapt as needed.
InfluxDb: como monitorar milhares de dados por segundo em real time Umbler
Slides da Palestra apresentada na Trilha Banco de Dados do The Developers Conference 2016 - São Paulo.
A palestra aborda os principais conceitos sobre Time series database (TSDB), e demonstra como utilizar a stack TICK (Telegraf, InfluxDb, Conograph, Kapacitor) da InfluxData para resolver problemas de monitoria de dados em grande escala, gerando gráficos e alertas em tempo real.
Pre-Con Ed: Deep Dive into CA Workload Automation Agent Job TypesCA Technologies
The document discusses various job types available in CA Workload Automation Agent (CA WA AE). It provides examples of configuring different agent plugins like database, proxy, etc. and modifying the agentparm.txt file. It also demonstrates how to set up the owner attribute and job security for different job types like FTP, WBSVC, JMSPUB, HTTP etc. The document aims to provide a deep dive into the various agent job types in CA WA AE and how to install plugins, configure the agent and set up job security.
This document discusses time series databases and the Apache Parquet columnar storage format. It notes that time series databases store data for each point in time, such as weather or stock price data. Storage is optimized to minimize input/output by reading the minimum number of records. Apache Parquet provides a columnar storage format that allows for better compression, reduced input/output by scanning subset of columns, and encoding of data types. It discusses Parquet terminology, encodings, and techniques for query optimization such as projection and predicate push down and choosing an appropriate Parquet block size.
Salesforce.com is an enterprise Cloud Computing Leader that specializes in Software as a Service. With several hundred teams working on our diverse product suite, releasing three times a year is not an easy endeavor. Our Agile processes are the key to our success. In this deck, learn the 5 fundamental elements of our successful enterprise implementation of Agile software development methodologies.
This document discusses working with time series data using InfluxDB. It provides an overview of time series data and why InfluxDB is useful for storing and querying it. Key features of InfluxDB covered include its SQL-like query language, retention policies for managing data storage, continuous queries for aggregation, and tools for data collection, visualization and monitoring.
Este documento discute os principais conceitos e benefícios da arquitetura de microserviços, incluindo isolamento de times e recursos para permitir entregas independentes, e eventual consistência para lidar com transações distribuídas.
Nick Gardner has created a 90-day plan to achieve success in his new role at Salesforce. The plan involves 3 stages: Days 1-30 focus on learning about Salesforce's products, industry, and sales processes. Days 30-60 focus on developing sales skills like qualifying leads and disqualifying prospects. Days 60-90 focus on continued development through activities like shadowing sales calls. Key to staying on track are setting goals, reviewing metrics with his manager, and completing a monthly V2MOM (Vision, Values, Methods, Obstacles, Measures) worksheet. The plan aims to make Nick the top performing SDR in his class by the end of three months.
As one of the most requested features in our last survey, and one of the most active open GitHub issues, alerting in Grafana is both an exciting and contentious topic. This presentation details our approach to tackling the alerting question in Grafana, and what’s coming down the pipe to allow people to manage their alerts side-by-side with their visualizations.
The document provides an overview of roles, artifacts, meetings, and processes in Scrum. The Scrum team is cross-functional and self-organizing. Artifacts include the Product Backlog, Sprint Backlog, and Burndown Chart. Meetings include Sprint Planning, Daily Scrum, Sprint Review, and Retrospective. The Product Owner prioritizes the Product Backlog and Scrum Master facilitates the team.
SREcon 2016 Performance Checklists for SREsBrendan Gregg
Talk from SREcon2016 by Brendan Gregg. Video: https://www.usenix.org/conference/srecon16/program/presentation/gregg . "There's limited time for performance analysis in the emergency room. When there is a performance-related site outage, the SRE team must analyze and solve complex performance issues as quickly as possible, and under pressure. Many performance tools and techniques are designed for a different environment: an engineer analyzing their system over the course of hours or days, and given time to try dozens of tools: profilers, tracers, monitoring tools, benchmarks, as well as different tunings and configurations. But when Netflix is down, minutes matter, and there's little time for such traditional systems analysis. As with aviation emergencies, short checklists and quick procedures can be applied by the on-call SRE staff to help solve performance issues as quickly as possible.
In this talk, I'll cover a checklist for Linux performance analysis in 60 seconds, as well as other methodology-derived checklists and procedures for cloud computing, with examples of performance issues for context. Whether you are solving crises in the SRE war room, or just have limited time for performance engineering, these checklists and approaches should help you find some quick performance wins. Safe flying."
In this training webinar, Samantha Wang will walk you through the basics of Telegraf. Telegraf is the open source server agent which is used to collect metrics from your stacks, sensors and systems. It is InfluxDB’s native data collector that supports nearly 300 inputs and outputs. Learn how to send data from a variety of systems, apps, databases and services in the appropriate format to InfluxDB. Discover tips and tricks on how to write your own plugins. The know-how learned here can be applied to a multitude of use cases and sectors. This one-hour session will include the training and time for live Q&A.
INTERFACE by apidays 2023 - Data Collection Basics, Anais Dotis-Georgiou, Inf...apidays
INTERFACE by apidays 2023
APIs for a “Smart” economy. Embedding AI to deliver Smart APIs and turn into an exponential organization
June 28 & 29, 2023
Data Collection Basics
Anais Dotis-Georgiou, Lead Developer Advocate at InfluxData
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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
Maximizing Real-Time Data Processing with Apache Kafka and InfluxDB: A Compre...HostedbyConfluent
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.
Speaker: Matt Howlett, Software Engineer, Confluent
This presentation provides a technical overview of Apache Kafka® and covers some of its popular use cases.
Mauricio Roman discusses detecting anomalies in Nginx log data through multi-dimensional analysis. He explores his company's Nginx logs, extracting over 100 features to identify unexpected error patterns. Parsing logs with open source tools, he sends data to Kafka and finds: 1) 408 errors correlate with large GET payloads, 2) most 4xx errors come from Opera, 3) Opera 4xx errors originate from specific countries. His vision is to automate such exploration and correlate HTTP and application logs in real time to monitor error rates and identify true anomalies.
Designing Event-Driven Applications with Apache NiFi, Apache Flink, Apache Spark
DevNexus 2022 Atlanta
https://devnexus.com/presentations/7150/
This talk is a quick overview of the How, What and WHY of Apache Pulsar, Apache Flink and Apache NiFi. I will show you how to design event-driven applications that scale the cloud native way.
This talk was done live in person at DevNexus across from the booth in room 311
Tim Spann
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
This document discusses Lyft's use of DynamoDB change logs to ingest real-time data into Elasticsearch. It describes how Flink jobs are used to stream data from DynamoDB streams to Kafka and then from Kafka to Elasticsearch. It addresses challenges like handling 429 errors from Elasticsearch and access control using VPC security groups. Finally, it discusses how the pipeline was designed to allow seamless upgrades of Elasticsearch without downtime by buffering changes in Kafka during migration.
Distributed & Highly Available server applications in Java and ScalaMax Alexejev
This document summarizes a presentation about distributed and highly available server applications in Java and Scala. It discusses the Talkbits architecture, which uses lightweight SOA principles with stateless edge services and specialized systems to manage state. The presentation describes using the Finagle library as a distributed RPC framework with Apache Zookeeper for service discovery. It also covers configuration, deployment, monitoring and logging of services using tools like SLF4J, Logback, CodaHale metrics, Jolokia, Fabric, and Datadog.
This document discusses distributed and highly available server applications built in Java and Scala. It describes an architecture using lightweight microservices called Talkbits that communicate over the Finagle distributed RPC framework. Key principles for Talkbits include stateless services, service discovery with Zookeeper, and functional composition of RPC calls. The document also covers configuration, deployment, logging, metrics collection and monitoring of the distributed system using tools like Loggly, CodaHale, Jolokia, Datadog, and Fabric.
ClickHouse Paris Meetup. Pragma Analytics Software Suite w/ClickHouse, by Mat...Altinity Ltd
Pragma Innovation is an IT services company focused on time series data solutions. Their PASS (Pragma Analytics Software Suite) allows companies to analyze, report on, and make decisions from time series network data using open source software. It is designed for ISPs, hosting providers, and telecom companies. The solution ingests network and log data, standardizes it, enriches it using tools like GeoIP, and stores it in a time series database. This allows customers to build applications for traffic engineering, security, and business intelligence use cases. Key challenges addressed in version 2.0 of the solution include data sampling, IPv4/IPv6 support, and using ClickHouse as the time series database for its performance and simplicity
This document provides an introduction to time series data and InfluxDB. It defines time series data as measurements taken from the same source over time that can be plotted on a graph with one axis being time. Examples of time series data include weather, stock prices, and server metrics. Time series databases like InfluxDB are optimized for storing and processing huge volumes of time series data in a high performance manner. InfluxDB uses a simple data model where points consist of measurements, tags, fields, and timestamps.
Sub-Second SQL Search, Aggregations and Joins with Kafka and Rockset | Dhruba...HostedbyConfluent
We often need to build applications that analyze Kafka data to unlock the most value from event streams, so how can organizations build these real-time analytics applications? In this talk, we examine an indexing approach that enables fast SQL analytics on data from Kafka, without data flattening or denormalization. Rockset is the real-time indexing database that builds an inverted index, a columnar index and a row index on all fields of your Kafka messages, including nested fields and arrays. This Converged Index accelerates various types of analytic queries–search, aggregations and joins–without the need to denormalize or transform data for performance reasons. With indexing delivering significant gains in query performance, we also need to index new data in a timely manner. We discuss several strategies used for efficient ingestion and indexing from Kafka, including rollups, write optimizations on the underlying RocksDB storage engine, and the disaggregation of ingest and query compute.
From Batch to Streaming with Apache Apex Dataworks Summit 2017Apache Apex
This document discusses transitioning from batch to streaming data processing using Apache Apex. It provides an overview of Apex and how it can be used to build real-time streaming applications. Examples are given of how to build an application that processes Twitter data streams and visualizes results. The document also outlines Apex's capabilities for scalable stream processing, queryable state, and its growing library of connectors and transformations.
From Batch to Streaming ET(L) with Apache Apex at Berlin Buzzwords 2017Thomas Weise
https://berlinbuzzwords.de/17/session/batch-streaming-etl-apache-apex
Stream data processing is increasingly required to support business needs for faster actionable insight with growing volume of information from more sources. Apache Apex is a true stream processing framework for low-latency, high-throughput and reliable processing of complex analytics pipelines on clusters. Apex is designed for quick time-to-production, and is used in production by large companies for real-time and batch processing at scale.
This session will use an Apex production use case to walk through the incremental transition from a batch pipeline with hours of latency to an end-to-end streaming architecture with billions of events per day which are processed to deliver real-time analytical reports. The example is representative for many similar extract-transform-load (ETL) use cases with other data sets that can use a common library of building blocks. The transform (or analytics) piece of such pipelines varies in complexity and often involves business logic specific, custom components.
Topics include:
Pipeline functionality from event source through queryable state for real-time insights.
API for application development and development process.
Library of building blocks including connectors for sources and sinks such as Kafka, JMS, Cassandra, HBase, JDBC and how they enable end-to-end exactly-once results.
Stateful processing with event time windowing.
Fault tolerance with exactly-once result semantics, checkpointing, incremental recovery
Scalability and low-latency, high-throughput processing with advanced engine features for auto-scaling, dynamic changes, compute locality.
Recent project development and roadmap.
Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
This document provides an overview of Weather.com's analytics architecture using Apache Cassandra and Spark. It summarizes Weather.com's initial attempts using Cassandra, lessons learned, and its improved architecture. The improved architecture uses Cassandra for streaming event data with time-window compaction, stores all other data in Amazon S3 for batch processing in Spark, and replaces Kafka with Amazon SQS for event ingestion. It discusses best practices for data modeling in Cassandra including partitioning, secondary indexes, and avoiding wide rows and nulls. The document also highlights how Weather.com uses Apache Zeppelin notebooks for data exploration and visualization.
A brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will introduce some of the newer components of Kafka that will help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.
Similar to InfluxDB and Grafana: An Introduction to Time-Based Data Storage and Visualization (20)
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
7. Performance
● Decide a variable whether tag or field is important.
Series Cardinality : The number of unique measurement and tag set
combinations in an instance. When tag values are dependent they don't
increase cardinality at all
● Retention Policy and Continuous Queries
Continuous Query: An InfluxQL query that runs automatically and periodically
within a database.
9. Grafana
● Querying and Visualizing time series and metrics
● Supports InfluxDB, Elasticsearch, Cloudwatch, Prometheus, Graphite
● Plugins can be installed