Fluentd and Docker - running fluentd within a docker container
•
4 likes•1,466 views
Fluentd is a data collection tool for unified logging that allows for extensible and reliable data collection. It uses a simple core with plugins to provide buffering, high availability, load balancing, and streaming data transfer based on JSON. Fluentd can collect log data from various sources and output to different destinations in a flexible way using its plugin architecture and configuration files. It is widely used in production for tasks like log aggregation, filtering, and forwarding.
Report
Share
Report
Share
Download to read offline
Recommended
Fluentd and Docker - running fluentd within a docker container
Walks through the basics of Fluentd, as run on Docker. Introduces a demo where fluentd is run in a docker container.
Using Embulk at Treasure Data
Muga Nishizawa discusses Embulk, an open-source bulk data loader. Embulk loads records from various sources to various targets in parallel using plugins. Treasure Data customers use Embulk to upload different file formats and data sources to their TD database. While Embulk is focused on bulk loading, TD also develops additional tools to generate Embulk configurations, manage loads over time, and scale Embulk using a MapReduce executor on Hadoop clusters for very large data loads.
Fluentd - Unified logging layer
Fluentd is an open source data collector that allows for flexible and extensible logging. It provides a unified way to collect logs, metrics, and events from various sources and send them to multiple destinations. It handles concerns like buffering, retries, and failover to provide reliable data transfer. Fluentd uses a plugin-based architecture so it can support many use cases like simple forwarding, lambda architectures, stream processing, and logging for Docker and Kubernetes.
Unifying Events and Logs into the Cloud
1. Eduardo Silva discussed unifying event and log data from multiple sources into the cloud using Fluentd and Fluent Bit.
2. Fluentd is an open source data collector that allows for parsing and storing data from multiple sources through its pluggable input and output plugins.
3. Fluent Bit is designed for collecting data from IoT and embedded devices to transport it to third party services, with a focus on performance and lightweight resource usage.
Augmenting Mongo DB with treasure data
How to get the best of both: MongoDB is great for low latency quick access of recent data; Treasure Data is great for infinitely growing store of historical data. In the latter case, one need not worry about scaling.
Open source data ingestion
This document discusses data collection and ingestion tools. It begins with an overview of data collection versus ingestion, with collection happening at the source and ingestion receiving the data. Examples of data collection tools include rsyslog, Scribe, Flume, Logstash, Heka, and Fluentd. Examples of ingestion tools include RabbitMQ, Kafka, and Fluentd. The document concludes with a case study of asynchronous application logging and challenges to consider.
Real Time Data Analytics with MongoDB and Fluentd at Wish
Wish uses Fluentd and MongoDB for analytics. Fluentd is used to centrally collect and aggregate logs from application servers. The aggregated logs are then stored in MongoDB for fast querying and analysis. Hadoop and Hive are also used for log analysis but running a Hadoop cluster can be difficult, so analysis results are stored in MongoDB for quick access. Tools like Dashy and Perimeter are used to visualize analytics data and report on A/B tests. The analytics platform aims to enable faster experimentation and growth for Wish.
Lightning talk: elasticsearch at Cogenta
This document discusses how Elasticsearch is used by Cogenta to power their real-time retail intelligence platform. It tracks hundreds of eCommerce sites daily, organizing large amounts of data into a high-quality market view. Elasticsearch allows Cogenta to scale their processing and analytics capabilities, provide high availability, and power various use cases like logging, internal analytics, and reporting.
Recommended
Fluentd and Docker - running fluentd within a docker container
Walks through the basics of Fluentd, as run on Docker. Introduces a demo where fluentd is run in a docker container.
Using Embulk at Treasure Data
Muga Nishizawa discusses Embulk, an open-source bulk data loader. Embulk loads records from various sources to various targets in parallel using plugins. Treasure Data customers use Embulk to upload different file formats and data sources to their TD database. While Embulk is focused on bulk loading, TD also develops additional tools to generate Embulk configurations, manage loads over time, and scale Embulk using a MapReduce executor on Hadoop clusters for very large data loads.
Fluentd - Unified logging layer
Fluentd is an open source data collector that allows for flexible and extensible logging. It provides a unified way to collect logs, metrics, and events from various sources and send them to multiple destinations. It handles concerns like buffering, retries, and failover to provide reliable data transfer. Fluentd uses a plugin-based architecture so it can support many use cases like simple forwarding, lambda architectures, stream processing, and logging for Docker and Kubernetes.
Unifying Events and Logs into the Cloud
1. Eduardo Silva discussed unifying event and log data from multiple sources into the cloud using Fluentd and Fluent Bit.
2. Fluentd is an open source data collector that allows for parsing and storing data from multiple sources through its pluggable input and output plugins.
3. Fluent Bit is designed for collecting data from IoT and embedded devices to transport it to third party services, with a focus on performance and lightweight resource usage.
Augmenting Mongo DB with treasure data
How to get the best of both: MongoDB is great for low latency quick access of recent data; Treasure Data is great for infinitely growing store of historical data. In the latter case, one need not worry about scaling.
Open source data ingestion
This document discusses data collection and ingestion tools. It begins with an overview of data collection versus ingestion, with collection happening at the source and ingestion receiving the data. Examples of data collection tools include rsyslog, Scribe, Flume, Logstash, Heka, and Fluentd. Examples of ingestion tools include RabbitMQ, Kafka, and Fluentd. The document concludes with a case study of asynchronous application logging and challenges to consider.
Real Time Data Analytics with MongoDB and Fluentd at Wish
Wish uses Fluentd and MongoDB for analytics. Fluentd is used to centrally collect and aggregate logs from application servers. The aggregated logs are then stored in MongoDB for fast querying and analysis. Hadoop and Hive are also used for log analysis but running a Hadoop cluster can be difficult, so analysis results are stored in MongoDB for quick access. Tools like Dashy and Perimeter are used to visualize analytics data and report on A/B tests. The analytics platform aims to enable faster experimentation and growth for Wish.
Lightning talk: elasticsearch at Cogenta
This document discusses how Elasticsearch is used by Cogenta to power their real-time retail intelligence platform. It tracks hundreds of eCommerce sites daily, organizing large amounts of data into a high-quality market view. Elasticsearch allows Cogenta to scale their processing and analytics capabilities, provide high availability, and power various use cases like logging, internal analytics, and reporting.
Log Files
Log files provide insights into systems like web servers and databases by recording details of requests, responses, and operations over time. They can be used for monitoring systems, troubleshooting issues, and analyzing usage patterns. However, the large volumes of log data produced require efficient processing and aggregation approaches to gain these insights in real-time or through batch analysis. Common techniques include shipping logs to a central aggregator, using group communication protocols for real-time distribution, and batch processing with Hadoop/MapReduce.
GOTO Aarhus 2014: Making Enterprise Data Available in Real Time with elastics...
My talk from GOTO Aarhus, 30th September 2014. Cogenta is a retail intelligence company which tracks ecommerce web sites around the world to provide competitive monitoring and analysis services to retailers. Using its proprietary crawler technology, Lucene and SQL Server, a stream of 20 million raw product data entries is captured and processed each day. This case study looks at how Cogenta uses Elasticsearch to break the shackles imposed by the RDBMS (and a limited budget) to make the data available in real time to its customers.
Cogenta uses SQL as its canonical store & for complex reporting, and Elasticsearch for real-time processing & to drive its SaaS web applications. Elasticsearch is easy to use, delivers the powerful features of Lucene and enables the data & platform cost to scale linearly. But… synchronising your existing data in two places presents some interesting challenges such as aggregation and concurrency control. This talk will take a detailed look at how Cogenta how overcame those challenges, with a perpetually changing and asynchronously updated dataset.
http://gotocon.com/aarhus-2014/presentation/Cogenta%20-%20Making%20Enterprise%20Data%20Available%20in%20Real%20Time%20with%20Elasticsearch
Log analysis with the elk stack
This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for log analysis. It describes the author's experience using Splunk and alternatives like Graylog and Elasticsearch before settling on the ELK stack. The key components - Logstash for input, Elasticsearch for storage and searching, and Kibana for the user interface - are explained. Troubleshooting tips are provided around checking that the components are running and communicating properly.
Fluentd - Flexible, Stable, Scalable
Fluentd - a data collector with many plugin support
This is a talk about Fluentd at Taipei.py on Feb. 26th 2015.
Scaling to Infinity - Open Source meets Big Data
John Hammink's Talk at Great Wide Open 2016. We discuss: 1.) the need for data analytics infrastructure that can scale exponentially and 2.) what such an infrastructure must contain and finally 3.) the need for an infrastructure to be able to handle un - and semi-structured data.
Microservice-based software architecture
Following the classical software architecture patterns we tend to design large monolith of software applications.
These monoliths are typically quite difficult to scale as they often require powerful machines, making the option to scale out very expensive.
In most cases these monoliths of software are designed to run on a single machine only, hence scaling out is complicated or even impossible without refactoring large portions of the application.
Therefore a new design pattern called microservices arose.
The pattern of microservices keeps the need of a clustered server setup in mind and helps to keep the application very modular.
This allows to simplify a scale out of your application and even allows to scale the bottlenecks of your application only and hence reducing the total cost for a scale out approach.
In this talk I will introduce the concept of microservices, how they are defined and how to design an application with them.
Furthermore I will show how to scale the application properly and why this is only possible due to the use of microservices.
Also we will have a look at Node.js and why it is a perfect, though not the only, fit to this design strategy.
However scaling is not the only purpose of microservices, they also increase the flexibility and maintainability of applications, this will also be discussed in the talk.
Rental Cars and Industrialized Learning to Rank with Sean Downes
Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp.
In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
MongoDB on Azure
MongoDB has been conceived for the cloud age. Making sure that MongoDB is compatible and performant around cloud providers is mandatory to achieve complete integration with platforms and systems. Azure is one of biggest IaaS platforms available and very popular amongst developers that work on Microsoft Stack.
Monitoring and scaling postgres at datadog
An overview of how Datadog has built and scaled our Postgres clusters to support the ingestion of trillions of metric data points per day, by Seth Rosenblum, Lead Data Reliability Engineer.
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
This document describes using MongoDB and Python for data analysis pipelines. It discusses using MongoDB for data storage and aggregation, connecting to MongoDB from Python using Monary and PyMongo, and using Python for data processing, analysis and visualization. It provides an example MongoDB aggregation query in Python to sum the population by state using Monary. It also shows an example Airflow DAG for running the aggregation query as part of a scheduled data pipeline with monitoring and dependencies.
Cascalog
Presentation I gave at Bay Area Clojure Meetup Group on May 6th, 2010. Also demoed examples from introductory tutorial:
http://nathanmarz.com/blog/introducing-cascalog/
Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...
My workshop at the Learning Analytics Summer Institute (LASI) 2016: http://lasi16.snola.es/#!/schedule/113
Educational data continues to grow in volume, velocity and variety. Making sense of the educational data in such conditions requires deployment and usage of appropriate scalable, real-time processing tools supporting a flexible data schema. Elasticsearch is one of the popular open-source tools meeting the enlisted requirements. Initially envisioned as a search engine capable of operating at scale and in real time, Elasticsearch is used by organisations such as Wikimedia and Github, which deal with big data on daily basis. In addition, Elasticsearch is used increasingly often as analytics platform thanks to its scalable architecture and expressive query language. Until recently, the exploitation of Elasticsearch for (learning) analytical purposes by practitioners was hindered by a high entrance barrier due to the complexity of the query language and the query specificities. This is currently changing with the ongoing development of Kibana, an open-source tool that allows to conduct analysis and build visualisations of Elasticsearch data through a graphical user interface. Kibana does not require the user to dive into technical details of the queries (although it is still possible) and hence makes big educational data visualisations accessible to regular users. The additional value of Kibana comes in play whenever several visualisations are combined on a single dashboard, enabling to use multiple coordinated views for an interactive explorative analysis. Both Elasticsearch and Kibana, together with Logstash are part of an analytics stack often referred to as ELK. Logstash supports data acquisition from multiple sources (including twitter, RSS, event logs) thanks to its rich set of available connectors. Custom connectors can be developed for case-specific sources. In addition to the mentioned values, ELK enables building analytics infrastructures decoupled from the learning platform, i.e., it allows to host separately the learning environment (with the analytics functionalities) and the data storage without affecting the end-user experience.
ELK Wrestling (Leeds DevOps)
Talk I did on log aggregation with the ELK stack at Leeds DevOps. Covers how we process over 800,000 logs per hour at laterooms, and the cultural changes this has helped drive.
Superset druid realtime
This document discusses using real-time data with Superset by ingesting data streams using Apache Flink and storing the data in Apache Druid. It outlines use cases like operational insights and service debugging. It proposes using Flink for its scalable streaming capabilities and SQL interface and integrating with Druid for its columnar storage and geospatial support. It also describes transforming the data through UDFs before querying and visualizing it in Superset.
Technologies, Data Analytics Service and Enterprise Business
This document discusses technologies for data analytics services for enterprise businesses. It begins by defining enterprise businesses as those "not about IT" and data analytics services as providing insights into business metrics like customer reach, ad views, purchases, and more using data. It then outlines some key technologies needed for such services, including data management systems, distributed processing systems, queues and schedulers, tools for connecting systems, and methods for controlling jobs and workflows with retries to handle failures. Specific challenges around deadlines, idempotent operations, and replay-able workflows are also addressed.
Kibana + timelion: time series with the elastic stack
The document discusses Kibana and Timelion, which are tools for visualizing and analyzing time series data in the Elastic Stack. It provides an overview of Kibana's evolution and capabilities for creating dashboards. Timelion is introduced as a scripting language that allows users to transform, aggregate, and calculate on time series data from multiple sources to create visualizations. The document demonstrates Timelion's expression language, which includes functions, combinations, filtering, and attributes to process and render time series graphs.
Building Data Pipelines in Python
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2nwSwEh.
Marco Bonzanini discusses the process of building data pipelines, e.g. extraction, cleaning, integration, pre-processing of data; in general, all the steps necessary to prepare data for a data-driven product. In particular, he focuses on data plumbing and on the practice of going from prototype to production. Filmed at qconlondon.com.
Marco Bonzanini is Data Scientist and co-organizer of PyData London Meetup.
Deep Dive on ArangoDB
ArangoDB is a multi-model database that can be used as a document store, key-value store, and graph database. It offers queries across different data models using its query language AQL. Developers can also extend its functionality through JavaScript modules. ArangoDB can run on Apache Mesos clusters to provide automatic scaling and fault tolerance. It aims to provide polyglot persistence through a single database technology.
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
"Elasticsearch has come a long way: Started as a distributed search engine in 2009, it's now the tool of choice for even the largest websites (e.g. Facebook, Github, Ebay). Half-way to 2016 the ELK stack helped it become firmly embedded in many centralised log management systems (e.g. Netflix, Uber).
We're now midair in the next step, with the first folks using it for metrics. NASA is using it to monitor the Curiosity rover, Blizzard and Riot to monitor vast online gaming worlds.
This talk will focus on what makes this transition from more unstructured to structured data possible.
"
Building highly reliable data pipeline @datadog par Quentin François
Certaines fonctionnalités au cœur du produit de Datadog reposent sur des pipelines de données construits avec Spark qui traitent des milliers de milliards de points chaque jour. Dans cette présentation, nous verrons les grands principes que nous appliquons chez Datadog pour assurer que nos pipelines restent fiables malgré la croissance exponentielle du volume de données, les pannes matérielles, les données corrompues et les erreurs humaines.
Paris Data Eng' Meetup du 26 février 2019 @Datadog
Augmenting Mongo DB with Treasure Data
Sometimes , some things work better than other things. MongoDB is great for quick access to low-latency data; Treasure Data is great for infinitely scalable historical data store. A lambda architecture is also explained.
Building a system for machine and event-oriented data with Rocana
In this session, we’ll follow the flow of data through an end-to-end system built to handle tens of terabytes an hour of event-oriented data, providing real-time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive can be stitched together to form the base platform; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality. Finally, a brief demo of Rocana Ops, an application for large scale data center operations, will be given, along with an explanation about how it uses the underlying platform.
More Related Content
What's hot
Log Files
Log files provide insights into systems like web servers and databases by recording details of requests, responses, and operations over time. They can be used for monitoring systems, troubleshooting issues, and analyzing usage patterns. However, the large volumes of log data produced require efficient processing and aggregation approaches to gain these insights in real-time or through batch analysis. Common techniques include shipping logs to a central aggregator, using group communication protocols for real-time distribution, and batch processing with Hadoop/MapReduce.
GOTO Aarhus 2014: Making Enterprise Data Available in Real Time with elastics...
My talk from GOTO Aarhus, 30th September 2014. Cogenta is a retail intelligence company which tracks ecommerce web sites around the world to provide competitive monitoring and analysis services to retailers. Using its proprietary crawler technology, Lucene and SQL Server, a stream of 20 million raw product data entries is captured and processed each day. This case study looks at how Cogenta uses Elasticsearch to break the shackles imposed by the RDBMS (and a limited budget) to make the data available in real time to its customers.
Cogenta uses SQL as its canonical store & for complex reporting, and Elasticsearch for real-time processing & to drive its SaaS web applications. Elasticsearch is easy to use, delivers the powerful features of Lucene and enables the data & platform cost to scale linearly. But… synchronising your existing data in two places presents some interesting challenges such as aggregation and concurrency control. This talk will take a detailed look at how Cogenta how overcame those challenges, with a perpetually changing and asynchronously updated dataset.
http://gotocon.com/aarhus-2014/presentation/Cogenta%20-%20Making%20Enterprise%20Data%20Available%20in%20Real%20Time%20with%20Elasticsearch
Log analysis with the elk stack
This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for log analysis. It describes the author's experience using Splunk and alternatives like Graylog and Elasticsearch before settling on the ELK stack. The key components - Logstash for input, Elasticsearch for storage and searching, and Kibana for the user interface - are explained. Troubleshooting tips are provided around checking that the components are running and communicating properly.
Fluentd - Flexible, Stable, Scalable
Fluentd - a data collector with many plugin support
This is a talk about Fluentd at Taipei.py on Feb. 26th 2015.
Scaling to Infinity - Open Source meets Big Data
John Hammink's Talk at Great Wide Open 2016. We discuss: 1.) the need for data analytics infrastructure that can scale exponentially and 2.) what such an infrastructure must contain and finally 3.) the need for an infrastructure to be able to handle un - and semi-structured data.
Microservice-based software architecture
Following the classical software architecture patterns we tend to design large monolith of software applications.
These monoliths are typically quite difficult to scale as they often require powerful machines, making the option to scale out very expensive.
In most cases these monoliths of software are designed to run on a single machine only, hence scaling out is complicated or even impossible without refactoring large portions of the application.
Therefore a new design pattern called microservices arose.
The pattern of microservices keeps the need of a clustered server setup in mind and helps to keep the application very modular.
This allows to simplify a scale out of your application and even allows to scale the bottlenecks of your application only and hence reducing the total cost for a scale out approach.
In this talk I will introduce the concept of microservices, how they are defined and how to design an application with them.
Furthermore I will show how to scale the application properly and why this is only possible due to the use of microservices.
Also we will have a look at Node.js and why it is a perfect, though not the only, fit to this design strategy.
However scaling is not the only purpose of microservices, they also increase the flexibility and maintainability of applications, this will also be discussed in the talk.
Rental Cars and Industrialized Learning to Rank with Sean Downes
Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp.
In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
MongoDB on Azure
MongoDB has been conceived for the cloud age. Making sure that MongoDB is compatible and performant around cloud providers is mandatory to achieve complete integration with platforms and systems. Azure is one of biggest IaaS platforms available and very popular amongst developers that work on Microsoft Stack.
Monitoring and scaling postgres at datadog
An overview of how Datadog has built and scaled our Postgres clusters to support the ingestion of trillions of metric data points per day, by Seth Rosenblum, Lead Data Reliability Engineer.
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
This document describes using MongoDB and Python for data analysis pipelines. It discusses using MongoDB for data storage and aggregation, connecting to MongoDB from Python using Monary and PyMongo, and using Python for data processing, analysis and visualization. It provides an example MongoDB aggregation query in Python to sum the population by state using Monary. It also shows an example Airflow DAG for running the aggregation query as part of a scheduled data pipeline with monitoring and dependencies.
Cascalog
Presentation I gave at Bay Area Clojure Meetup Group on May 6th, 2010. Also demoed examples from introductory tutorial:
http://nathanmarz.com/blog/introducing-cascalog/
Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...
My workshop at the Learning Analytics Summer Institute (LASI) 2016: http://lasi16.snola.es/#!/schedule/113
Educational data continues to grow in volume, velocity and variety. Making sense of the educational data in such conditions requires deployment and usage of appropriate scalable, real-time processing tools supporting a flexible data schema. Elasticsearch is one of the popular open-source tools meeting the enlisted requirements. Initially envisioned as a search engine capable of operating at scale and in real time, Elasticsearch is used by organisations such as Wikimedia and Github, which deal with big data on daily basis. In addition, Elasticsearch is used increasingly often as analytics platform thanks to its scalable architecture and expressive query language. Until recently, the exploitation of Elasticsearch for (learning) analytical purposes by practitioners was hindered by a high entrance barrier due to the complexity of the query language and the query specificities. This is currently changing with the ongoing development of Kibana, an open-source tool that allows to conduct analysis and build visualisations of Elasticsearch data through a graphical user interface. Kibana does not require the user to dive into technical details of the queries (although it is still possible) and hence makes big educational data visualisations accessible to regular users. The additional value of Kibana comes in play whenever several visualisations are combined on a single dashboard, enabling to use multiple coordinated views for an interactive explorative analysis. Both Elasticsearch and Kibana, together with Logstash are part of an analytics stack often referred to as ELK. Logstash supports data acquisition from multiple sources (including twitter, RSS, event logs) thanks to its rich set of available connectors. Custom connectors can be developed for case-specific sources. In addition to the mentioned values, ELK enables building analytics infrastructures decoupled from the learning platform, i.e., it allows to host separately the learning environment (with the analytics functionalities) and the data storage without affecting the end-user experience.
ELK Wrestling (Leeds DevOps)
Talk I did on log aggregation with the ELK stack at Leeds DevOps. Covers how we process over 800,000 logs per hour at laterooms, and the cultural changes this has helped drive.
Superset druid realtime
This document discusses using real-time data with Superset by ingesting data streams using Apache Flink and storing the data in Apache Druid. It outlines use cases like operational insights and service debugging. It proposes using Flink for its scalable streaming capabilities and SQL interface and integrating with Druid for its columnar storage and geospatial support. It also describes transforming the data through UDFs before querying and visualizing it in Superset.
Technologies, Data Analytics Service and Enterprise Business
This document discusses technologies for data analytics services for enterprise businesses. It begins by defining enterprise businesses as those "not about IT" and data analytics services as providing insights into business metrics like customer reach, ad views, purchases, and more using data. It then outlines some key technologies needed for such services, including data management systems, distributed processing systems, queues and schedulers, tools for connecting systems, and methods for controlling jobs and workflows with retries to handle failures. Specific challenges around deadlines, idempotent operations, and replay-able workflows are also addressed.
Kibana + timelion: time series with the elastic stack
The document discusses Kibana and Timelion, which are tools for visualizing and analyzing time series data in the Elastic Stack. It provides an overview of Kibana's evolution and capabilities for creating dashboards. Timelion is introduced as a scripting language that allows users to transform, aggregate, and calculate on time series data from multiple sources to create visualizations. The document demonstrates Timelion's expression language, which includes functions, combinations, filtering, and attributes to process and render time series graphs.
Building Data Pipelines in Python
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2nwSwEh.
Marco Bonzanini discusses the process of building data pipelines, e.g. extraction, cleaning, integration, pre-processing of data; in general, all the steps necessary to prepare data for a data-driven product. In particular, he focuses on data plumbing and on the practice of going from prototype to production. Filmed at qconlondon.com.
Marco Bonzanini is Data Scientist and co-organizer of PyData London Meetup.
Deep Dive on ArangoDB
ArangoDB is a multi-model database that can be used as a document store, key-value store, and graph database. It offers queries across different data models using its query language AQL. Developers can also extend its functionality through JavaScript modules. ArangoDB can run on Apache Mesos clusters to provide automatic scaling and fault tolerance. It aims to provide polyglot persistence through a single database technology.
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
"Elasticsearch has come a long way: Started as a distributed search engine in 2009, it's now the tool of choice for even the largest websites (e.g. Facebook, Github, Ebay). Half-way to 2016 the ELK stack helped it become firmly embedded in many centralised log management systems (e.g. Netflix, Uber).
We're now midair in the next step, with the first folks using it for metrics. NASA is using it to monitor the Curiosity rover, Blizzard and Riot to monitor vast online gaming worlds.
This talk will focus on what makes this transition from more unstructured to structured data possible.
"
Building highly reliable data pipeline @datadog par Quentin François
Certaines fonctionnalités au cœur du produit de Datadog reposent sur des pipelines de données construits avec Spark qui traitent des milliers de milliards de points chaque jour. Dans cette présentation, nous verrons les grands principes que nous appliquons chez Datadog pour assurer que nos pipelines restent fiables malgré la croissance exponentielle du volume de données, les pannes matérielles, les données corrompues et les erreurs humaines.
Paris Data Eng' Meetup du 26 février 2019 @Datadog
What's hot (20)
GOTO Aarhus 2014: Making Enterprise Data Available in Real Time with elastics...
GOTO Aarhus 2014: Making Enterprise Data Available in Real Time with elastics...
Rental Cars and Industrialized Learning to Rank with Sean Downes
Rental Cars and Industrialized Learning to Rank with Sean Downes
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
MongoDB Days UK: Using MongoDB and Python for Data Analysis Pipelines
Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...
Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...
Technologies, Data Analytics Service and Enterprise Business
Technologies, Data Analytics Service and Enterprise Business
Kibana + timelion: time series with the elastic stack
Kibana + timelion: time series with the elastic stack
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
Building highly reliable data pipeline @datadog par Quentin François
Building highly reliable data pipeline @datadog par Quentin François
Viewers also liked
Augmenting Mongo DB with Treasure Data
Sometimes , some things work better than other things. MongoDB is great for quick access to low-latency data; Treasure Data is great for infinitely scalable historical data store. A lambda architecture is also explained.
Building a system for machine and event-oriented data with Rocana
In this session, we’ll follow the flow of data through an end-to-end system built to handle tens of terabytes an hour of event-oriented data, providing real-time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive can be stitched together to form the base platform; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality. Finally, a brief demo of Rocana Ops, an application for large scale data center operations, will be given, along with an explanation about how it uses the underlying platform.
What is support_engineer_in_treasuredata
This document provides an overview of the role of a support engineer at TreasureData. It discusses the tools and services used to provide support, including Desk.com, Olark, Jira, and Slack. It describes how support engineers help customers by answering questions, improving queries, and investigating logs. Support engineers also aim to improve the product by sharing customer feedback. Challenges mentioned include streamlining internal support processes, migrating to a new support system, building a customer database, and establishing support key performance indicators.
Packaging Ecosystems -Monki Gras 2017
How to make your open source project MATTER
Let’s face it: most open source projects die. “For every Rails, Docker and React, there are thousands of projects that never take off. They die in the lonely corners of GitHub, only to be discovered by bots scanning for SSH private keys.
Over the last 5 years, I worked on and off on marketing a piece of infrastructure middleware called Fluentd. We tried many things to ensure that it did not die: From speaking at events, speaking to strangers, giving away stickers, making people install Fluentd on their laptop. Most everything I tried had a small, incremental effect, but there were several initiatives/hacks that raised Fluentd’s awareness to the next level. As I listed up these “ideas that worked”, I noticed the common thread: they all brought Fluentd into a new ecosystem via packaging.”
Unifying Events and Logs into the Cloud
This presentation describes the common issues when doing application logging and introduce how to solve most of the problems through the implementation of an unified logging layer with Fluentd.
Insight Data Engineering: Open source data ingestion
Kiyoto Tamura gave a lecture at Insight Data Engineering about Open Source Data Collection and Ingestion.
Introduction to New features and Use cases of Hivemall
This document provides an introduction and overview of Hivemall, an open source machine learning library built as a collection of Hive UDFs. It begins with background on the presenter, Makoto Yui, and then covers the following key points:
- What Hivemall is and its vision of bringing machine learning capabilities to SQL users
- Popular algorithms supported in current and upcoming versions, such as random forest, factorization machines, gradient boosted trees
- Real-world use cases at companies such as for click-through rate prediction, user profiling, and churn detection
- How to use algorithms like random forest, matrix factorization, and factorization machines from Hive queries
- The development roadmap, with plans to support NLP
글로벌 사례로 보는 데이터로 돈 버는 법 - 트레저데이터 (Treasure Data)
* 행사 정보 :2016년 10월 14일 MARU180 에서 진행된 '데이터야 놀자' 1day 컨퍼런스 발표 자료
* 발표자 : Dylan Ko (고영혁) Data Scientist / Data Architect at Treasure Data
* 발표 내용
- 데이터사이언티스트 고영혁 소개
- Treasure Data (트레저데이터) 소개
- 데이터로 돈 버는 글로벌 사례 #1
>> MUJI : 전통적 리테일에서 데이터 기반 O2O
- 데이터로 돈 버는 글로벌 사례 #2
>> WISH : 개인화&자동화를 통한 쇼핑 최적화
- 데이터로 돈 버는 글로벌 사례 #3
>> Oisix : 머신러닝으로 이탈고객 예측&방지
- 데이터로 돈 버는 글로벌 사례 #4
>> 워너브로스 : 프로세스 자동화로 시간과 돈 절약
- 데이터로 돈 버는 글로벌 사례 #5
>> Dentsu 등의 애드테크(Adtech) 회사들
- 데이터로 돈을 벌고자 할 때 반드시 체크해야 하는 것
Keynote - Fluentd meetup v14
Keynote on Fluentd Meetup Summer
Related Slide
- Fluentd ServerEngine Integration & Windows Support http://www.slideshare.net/RittaNarita/fluentd-meetup-2016-serverengine-integration-windows-support
- Fluentd v0.14 Plugin API Details http://www.slideshare.net/tagomoris/fluentd-v014-plugin-api-details
Viewers also liked (9)
Building a system for machine and event-oriented data with Rocana
Building a system for machine and event-oriented data with Rocana
Insight Data Engineering: Open source data ingestion
Insight Data Engineering: Open source data ingestion
Introduction to New features and Use cases of Hivemall
Introduction to New features and Use cases of Hivemall
Similar to Fluentd and Docker - running fluentd within a docker container
Logging for Production Systems in The Container Era
Logging for Production Systems in The Container Era discusses how to effectively collect and analyze logs and metrics in microservices-based container environments. It introduces Fluentd as a centralized log collection service that supports pluggable input/output, buffering, and aggregation. Fluentd allows collecting logs from containers and routing them to storage systems like Kafka, HDFS and Elasticsearch. It also supports parsing, filtering and enriching log data through plugins.
Collect distributed application logging using fluentd (EFK stack)
This document discusses using Fluentd to collect distributed application logging in a containerized environment. It provides an overview of Fluentd, including its pluggable architecture and configuration. It then demonstrates capturing logging from Docker containers and HTTP services and storing the logs in MongoDB and Elasticsearch as part of the ELK stack. It shows filtering and parsing logs from a Spring Boot application. Finally, it discusses setting up Fluentd for high availability.
Fluentd and Embulk Game Server 4
This document summarizes Masahiro Nakagawa's presentation on Fluentd and Embulk. Fluentd is a data collector for unified logging that allows for streaming data transfer based on JSON. It is written in Ruby and uses plugins to collect, process, and output data. Embulk is a bulk loading tool that allows high performance parallel processing of data to load it into various databases and storage systems. Both tools use a pluggable architecture to provide flexibility in handling different data sources and targets.
Fluentd at HKOScon
Fluentd is a data collector for unified logging that provides a robust core and plugins. It allows for reliable data transfer through error handling and retries. The core handles common concerns like parsing, buffering, and writing data, while plugins handle input, output, and other use cases. Fluentd has a pluggable architecture and processes data through a pipeline of input, parser, filter, buffer, formatter, and output plugins.
«Что такое serverless-архитектура и как с ней жить?» Николай Марков, Aligned ...
The document discusses what serverless computing is and how it can be used for building applications. Serverless applications rely on third party services to manage server infrastructure and are event-triggered. Popular serverless frameworks like AWS Lambda, Google Cloud Functions, Microsoft Azure Functions, and Zappa allow developers to write code that runs in a serverless environment and handle events and triggers without having to manage servers.
Big Data Day LA 2016/ Big Data Track - Fluentd and Embulk: Collect More Data,...
Since Doug Cutting invented Hadoop and Amazon Web Services released S3 ten years ago, we've seen quite a bit of innovation in large-scale data storage and processing. These innovations have enabled engineers to build data infrastructure at scale, many of them fail to fill their scalable systems with useful data, struggling to unify data silos or failing to collect logs from thousands of servers and millions of containers. Fluentd and Embulk are two projects that I've been involved to solve the unsexy yet critical problem of data collection and transport. In this talk, I will give an overview of Fluentd and Embulk and give a survey of how they are used at companies like Microsoft and Atlassian or in projects like Docker and Kubernetes.
CodeCamp Iasi - Creating serverless data analytics system on GCP using BigQuery
Teaser: provide developers a new way of understanding advanced analytics and choosing the right cloud architecture
The new buzzword is #serverless, as there are many great services that helps us abstract away the complexity associated with managing servers. In this session we will see how serverless helps on large data analytics backends.
We will see how to architect for Cloud and implement into an existing project components that will take us into the #serverless architecture that will ingest our streaming data, run advanced analytics on petabytes of data using BigQuery on Google Cloud Platform - all this next to an existing stack, without being forced to reengineer our app.
BigQuery enables super-fast, SQL/Javascript queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, SQL 2011 standard, working with streaming inserts, User Defined Functions written in Javascript, reference external JS libraries, and several use cases for everyday backend developer: funnel analytics, email heatmap, custom data processing, building dashboards, extracting data using JS functions, emitting rows based on business logic.
Fluentd Unified Logging Layer At Fossasia
Masahiro Nakagawa is a senior software engineer at Treasure Data and the main maintainer of Fluentd. Fluentd is a data collector for unified logging that provides a streaming data transfer based on JSON. It has a simple core with plugins written in Ruby to provide functionality like input/output, buffering, parsing, filtering and formatting of data.
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system using tools like: Web Services,Spark,Cassandra,MongoDB,AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Fluentd Overview, Now and Then
Fluentd is an open source data collector that allows flexible data collection, processing, and output. It supports streaming data from sources like logs and metrics to destinations like databases, search engines, and object stores. Fluentd's plugin-based architecture allows it to support a wide variety of use cases. Recent versions of Fluentd have added features like improved plugin APIs, nanosecond time resolution, and Windows support to make it more suitable for containerized environments and low-latency applications.
Treasure Data and OSS
- Treasure Data is a cloud data service that provides data acquisition, storage, and analysis capabilities.
- It collects data from various sources using Fluentd and Embulk and stores it in its own columnar database called Plazma DB.
- It offers various computing frameworks like Hive, Pig, and Presto for analytics and visualization with tools like Tableau.
- Presto is an interactive SQL query engine that can query data in HDFS, Hive, Cassandra and other data stores.
Automating Workflows for Analytics Pipelines
This document discusses automating analytics pipelines and workflows using a workflow engine. It describes the challenges of managing workflows across multiple cloud services and database technologies. It then introduces a multi-cloud workflow engine called Digdag that can automate workflows, handle errors, enable parallel execution, support modularization and parameterization. Examples are given of using Digdag to define and run workflows across services like BigQuery, Treasure Data, Redshift, and Tableau. Key features of Digdag like loops, parameters, parallel tasks and pushing workflows to servers with Docker are also summarized.
Designing the Call of Cthulhu app with Google App Engine
These are slides from a talk I gave at UCSB to the Senior Capstone class on 02/10/10 on how I developed the Call of Cthulhu application using Google App Engine.
Building Kick Ass Video Games for the Cloud
Christian Schalk gave a presentation on building games for the cloud using PlayN and Google cloud technologies. He began with an overview of PlayN, a cross-platform game development framework. He then reviewed Google App Engine and Google Cloud Storage as options for deploying and storing game data in the cloud. The presentation concluded with demonstrations of getting started with PlayN, deploying a game to App Engine, and setting up remote procedure calls between the client and server.
Thug: a new low-interaction honeyclient
Thug is a new low-interaction honeyclient for analyzing malicious web content and browser exploitation. It uses the Google V8 JavaScript engine and emulates different browser personalities to detect exploits. Thug analyzes content using static and dynamic analysis and logs results using MAEC format. Future work includes improving DOM emulation and JavaScript analysis to better identify vulnerabilities and exploit kits. The source code for Thug will be publicly released after the presentation.
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Berlin 2017
This document discusses monitoring big data systems in a simple way. It begins with introducing the speaker and their background. The rest of the document outlines monitoring concepts, common big data architectures involving Spark and Cassandra, and potential problems that can arise. It then provides recommendations for setting up a monitoring stack involving metrics collection, logging, dashboards, and alerting. Specifically, it recommends using Graphite, Grafana, Coralogix, and Redash. The document emphasizes the importance of correlating application and system metrics and asking the right monitoring questions.
Integrating Splunk into your Spring Applications
How much visibility do you really have into your Spring applications? How effectively are you capturing,harnessing and correlating the logs, metrics, & messages from your Spring applications that can be used to deliver this visibility ? What tools and techniques are you providing your Spring developers with to better create and utilize this mass of machine data ? In this session I'll answer these questions and show how Splunk can be used to not only provide historical and realtime visibility into your Spring applications , but also as a platform that developers can use to become more "devops effective" & easily create custom big data integrations and standalone solutions.I'll discuss and demonstrate many of Splunk's Java apps,frameworks and SDK and also cover the Spring Integration Adaptors for Splunk.
Customer Intelligence: Using the ELK Stack to Analyze ForgeRock OpenAM Audit ...
Warren Strange, Principal Systems Engineer, ForgeRock, presents a Breakout Session on the ELK Stack at the 2014 IRM Summit in Phoenix, Arizona.
Power your apps with Gmail, Google Drive, Calendar, Sheets, Slides & more
This is a ~90-minute technical introduction to G Suite/Google Apps developer tools, platforms, and APIs. Code samples are in Python+JS. Motivation: encourage developers to write apps integrating with G Suite APIs so they can monetize, taking advantage of the many schools & enterprises that are G Suite users. Delivered sessions at ISTE (Jun 2019), Google Cloud NEXT (Jul 2018), Google Cloud Summit - São Paulo (Nov 2017), DevFest DC (May 2017), DevFest NYC (Nov 2016), and GDG LA DevFest (Dec 2016).
Similar to Fluentd and Docker - running fluentd within a docker container (20)
Logging for Production Systems in The Container Era
Logging for Production Systems in The Container Era
Collect distributed application logging using fluentd (EFK stack)
Collect distributed application logging using fluentd (EFK stack)
«Что такое serverless-архитектура и как с ней жить?» Николай Марков, Aligned ...
«Что такое serverless-архитектура и как с ней жить?» Николай Марков, Aligned ...
Big Data Day LA 2016/ Big Data Track - Fluentd and Embulk: Collect More Data,...
Big Data Day LA 2016/ Big Data Track - Fluentd and Embulk: Collect More Data,...
CodeCamp Iasi - Creating serverless data analytics system on GCP using BigQuery
CodeCamp Iasi - Creating serverless data analytics system on GCP using BigQuery
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...
Designing the Call of Cthulhu app with Google App Engine
Designing the Call of Cthulhu app with Google App Engine
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Berlin 2017
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Berlin 2017
Customer Intelligence: Using the ELK Stack to Analyze ForgeRock OpenAM Audit ...
Customer Intelligence: Using the ELK Stack to Analyze ForgeRock OpenAM Audit ...
Power your apps with Gmail, Google Drive, Calendar, Sheets, Slides & more
Power your apps with Gmail, Google Drive, Calendar, Sheets, Slides & more
More from Treasure Data, Inc.
GDPR: A Practical Guide for Marketers
The new GDPR regulation went into effect on May 25th. While a majority of conversations have revolved around the security and IT aspects of the law, marketing teams will play a crucial role in helping organizations meet GDPR standards and playing a strategic role across the organization . Join us to learn more, engage with your peers and get prepared.
This webinar will cover:
- How complying with the GDPR will drive better marketing and raise the standard of the quality of your customer engagement
- The GDPR elements marketers must know about
- The elements of PII that will be affected and what marketers need to do about it
- A deep dive on how GDPR regulations will affect your marketing channels - email, programmatic advertising, cold calls, etc.
- Tactical marketing updates needed to meet GDPR guidelines
AR and VR by the Numbers: A Data First Approach to the Technology and Market
The document discusses trends in the augmented reality (AR) and virtual reality (VR) markets. It notes that the combined AR and VR market is estimated to reach $120 billion by 2020, with AR's market estimated at $89.9 billion and VR's at $29.9 billion. While VR growth is clear, the exact size is unclear. The document outlines challenges like the need for improved headsets and continued developer investment outside of mobile. It emphasizes that AR currently focuses on using data to project context and enable interaction with the real world, and that collecting user data is important for defining the experience.
Introduction to Customer Data Platforms
An overview of Customer Data Platforms (CDP) with the industry leader who coined the term, David Raab. Find out how to use Live Customer Data to create a better customer experience and how Live Data Management can give you a competitive edge with a 360 degree view of your clients.
Learn:
- The definition and requirements for Customer Data Platforms
- The differences between Customer Data Platforms and comparative technologies such as Data Warehousing and Marketing Automation
- Reference architectures/approaches to building CDP
- How Treasure Data is used to build Customer Data Platforms
And here's the song: https://youtu.be/RalMozVq55A
Hands On: Javascript SDK
In this hands-on webinar we will cover how to leverage the Treasure Data Javascript SDK library to ensure user stitching of web data into the Treasure Data Customer Data Platform to provide a holistic view of prospects and customers.
We will demo the native SDK, as well as deploying the SDK inside of Adobe DTM and Google Tag Manager.
Hands-On: Managing Slowly Changing Dimensions Using TD Workflow
In this hands-on webinar we'll explore the data warehousing concept of Slowly Changing Dimensions (SCDs) and common use cases for managing SCDs when dealing with customer data. This webinar will demonstrate different methods for tracking SCDs in a data warehouse, and how Treasure Data Workflow can be used to create robust data pipelines to handle these processes.
Brand Analytics Management: Measuring CLV Across Platforms, Devices and Apps
Gaming companies with multiple products often struggle to calculate accurate Customer Lifetime Value (CLTV) across their portfolio. This is because user data is often analyzed in silos so companies are unable to get a clear picture of ROI and CLTV across platforms, devices and apps.
In this webinar we’ll look at how you can apply a holistic and complete approach to your CLTV and ROI through the lens of gaming companies, though this technique is applicable for any company who has products spanning platforms.
We’ll also explore:
How the integral power of data in business has shifted over the past 10 years.
Discover the current technologies and processes used to analyze data across different platforms by combining multiple data streams, looking at examples in brand and portfolio-based LTV.
How to process and centralize dozens of varying data streams.
Nicolas Nadeau will speak from his extensive experience and show how leveraging data from multiple product strategies spanning many platforms can be highly beneficial for your company.
How to Power Your Customer Experience with Data
Do you know what your top ten 'happy' customers look like? Would you like to find ten more just like them? Come learn how to leverage 1st & 3rd party data to map your customer journey and drive users down a path where every interaction is personalized, fun, & data-driven. No more detractors, power your Customer Experience with data!
In this webinar you will learn:
-When, why, and how to leverage 1st, 2nd, and 3rd party data
-Tips & Tricks for marketers to become more data driven when launching their campaigns
-Why all marketers needs a 360 degree customer view
Why Your VR Game is Virtually Useless Without Data
The reality is virtual, but successful VR games still require cold, hard data. For wildly popular games like Survios’ Raw Data, the first VR-exclusive game to reach #1 on Steam’s Global Top Sellers list, data and analytics are the key to success.
And now online gaming companies have the full-stack analytics infrastructure and tools to measure every aspect of a virtual reality game and its ecosystem in real time. You can keep tabs on lag, which ruins a VR experience, improve gameplay and identify issues before they become showstoppers, and create fully personalized, completely immersive experiences that blow minds and boost adoption, and more. All with the right tools.
Make success a reality: Register now for our latest interactive VB Live event, where we’ll tap top experts in the industry to share insights into turning data into winning VR games.
Attendees will:
* Understand the role of VR in online gaming
* Find out how VR company Survios successfully leverages the Exostatic analytics infrastructure for commercial and gaming success
* Discover how to deploy full-stack analytics infrastructure and tools
Speakers:
Nicolas Nadeau, President, Exostatic
Kiyoto Tamura, VP Marketing, Treasure Data
Ben Solganik, Producer, Survios
Stewart Rogers, Director of Marketing Technology, VentureBeat
Wendy Schuchart, Moderator, VentureBeat
Connecting the Customer Data Dots
The document discusses how marketers can better leverage customer data to improve the customer experience. It provides tips from various experts on developing a robust data strategy, asking the right questions of data to uncover insights, owning customer data to stay compliant with regulations, and how IoT can be used to inform and deploy customer experience solutions. The overall message is that marketers need to stop data from being fragmented and better connect customer touchpoints to deliver personalized experiences.
Harnessing Data for Better Customer Experience and Company Success
As big data has exploded, the ability for companies to easily leverage it has imploded. Organizations are drowning in their own information, unable to see the forest through the trees, while the big players consistently outperform in their ability to deliver a great customer experience, faster, cheaper…As a result, the vast majority of companies are scrambling to catch up and become more agile, data-driven, to use their data more effectively so they can attract and retain their elusive customers...
In this joint deck by 451 Research and Treasure Data, you will learn how to enable your line of business team to own their own data (instead of relying on IT) to be able to:
- deliver a single, persistent view of your customer based on behavior data
- make that data accessible to the right people at the right time
- Increase organizational effectiveness by (finally) breaking down silos with data
- enable powerful marketing tools to enhance the customer experience
Scalable Hadoop in the cloud
This document summarizes Johan Gustavsson's presentation on scalable Hadoop in the cloud. It discusses (1) replacing an on-premise Hadoop cluster with Plazma storage on S3 and job execution in containers, (2) how jobs are isolated either through individual JobClients or resource pools, and (3) ongoing architecture changes through the Patchset Treasure Data initiative to support multiple Hadoop versions and improve high availability of job submission services.
Treasure Data: Move your data from MySQL to Redshift with (not much more tha...
This document discusses migrating data from MySQL to Amazon Redshift. It describes MySQL and Redshift, and some of the challenges of migrating between the two systems, such as incompatible schemas and manual processes. The proposed solution is to use a cloud data lake with schema-on-read to store JSON event data, which can then be loaded into Redshift, a cloud data warehouse with schema-on-write, providing an automated way to migrate data between different systems and schemas.
Treasure Data From MySQL to Redshift
This document discusses migrating data from MySQL to Amazon Redshift. It describes MySQL and Redshift, and some of the challenges of migrating between the two systems, such as incompatible schemas and manual processes. The proposed solution is to use a cloud data lake with schema-on-read to store JSON event data, which can then be loaded into Redshift, a cloud data warehouse with schema-on-write, providing an automated way to migrate data between different systems and schemas.
Partner webinar presentation aws pebble_treasure_data
Pebble uses data science and analytics to improve its smartwatch products. Pebble's data team analyzes over 60 million records per day from the watches to measure user engagement, identify issues, and inform new product design. Their first problem was setting an engagement threshold using the accelerometer. Rapid testing of different thresholds against "backlight data" validated the optimal threshold. Pebble has since solved many problems using their analytics infrastructure at Treasure Data to query, explore, and gain insights from massive user data in real-time.
Introduction to Hivemall
This document discusses a tech talk given by Makoto Yui at Treasure Data on May 14, 2015. It includes an introduction to Hivemall, an open source machine learning library built on Apache Hive. The talk covers how to use Hivemall for tasks like data preparation, feature engineering, model training, and prediction. It also discusses doing real-time prediction by training models offline on Hadoop and performing online predictions using the models on a relational database management system.
More from Treasure Data, Inc. (15)
AR and VR by the Numbers: A Data First Approach to the Technology and Market
AR and VR by the Numbers: A Data First Approach to the Technology and Market
Hands-On: Managing Slowly Changing Dimensions Using TD Workflow
Hands-On: Managing Slowly Changing Dimensions Using TD Workflow
Brand Analytics Management: Measuring CLV Across Platforms, Devices and Apps
Brand Analytics Management: Measuring CLV Across Platforms, Devices and Apps
Why Your VR Game is Virtually Useless Without Data
Why Your VR Game is Virtually Useless Without Data
Harnessing Data for Better Customer Experience and Company Success
Harnessing Data for Better Customer Experience and Company Success
Treasure Data: Move your data from MySQL to Redshift with (not much more tha...
Treasure Data: Move your data from MySQL to Redshift with (not much more tha...
Partner webinar presentation aws pebble_treasure_data
Partner webinar presentation aws pebble_treasure_data
Recently uploaded
社内勉強会資料_Hallucination of LLMs .
社内エンジニア・リサーチャー勉強会の発表資料「Hallucination of LLMs」を公開しました！
大規模言語モデル(LLM)に起こるハルシネーションのメカニズムと、それらを軽減する方法について紹介しています。
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
Gran libro. Lo recomiendo
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
办理【微信号:176555708】【办理(UofT毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号:176555708】文凭即可
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【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【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：客户在留信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！
Sid Sigma educational and problem solving power point- Six Sigma.ppt
Sid Sigma educational and problem solving power point
一比一原版卡尔加里大学毕业证（uc毕业证）如何办理
原版一模一样【微信：741003700 】【卡尔加里大学毕业证（uc毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
原版办【微信号:BYZS866】【英属哥伦比亚大学毕业证(UBC毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
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【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【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：客户在留信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！
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
一比一原版莱斯大学毕业证（rice毕业证）如何办理
原版一模一样【微信：741003700 】【莱斯大学毕业证（rice毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理莱斯大学毕业证（rice毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版南昆士兰大学毕业证如何办理
原版一模一样【微信：741003700 】【南昆士兰大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
办理南昆士兰大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理南昆士兰大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理南昆士兰大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理南昆士兰大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
How To Control IO Usage using Resource Manager
How To Control IO Usage using Resource Manager
In Multitenant and Exadata architecture
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Telemetry Solution for Gaming (AWS Summit'24)
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
Recently uploaded (20)
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
Sid Sigma educational and problem solving power point- Six Sigma.ppt
Sid Sigma educational and problem solving power point- Six Sigma.ppt
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Fluentd and Docker - running fluentd within a docker container
- 1. FLUENTD: UNIFIED LOGGING LAYER John Hammink October 14, 2015 Cask Big Data Application Meetup
- 2. About Me • A recovering software & QA engineer turned digital artist once interested in fractals; • now into data visualization based on large datasets rendered directly to GPU (RGL, various Python GL libraries, etc.) • github: jammink2; twitter: rijksband
- 3. Tweet NOW! “At @caskdata learning how to collect more event data using #Fluentd”
- 4. WHAT’S FLUENTD? An extensible & reliable data collection tool simple core + plugins buffering, HA (failover), load balancing, etc. like syslogd
- 5. What’s Fluentd? > Data collector for unified logging layer > Streaming data transfer based on JSON > Written in Ruby > Gem based various plugins > http://www.fluentd.org/plugins > Working in production > http://www.fluentd.org/testimonials
- 7. ✓ duplicated code for error handling... ✓ messy code for retrying mechnism... Blueflood MongoDB Hadoop Metrics Amazon S3 Analysis Archiving MySQL Apache Frontend Access logs syslogd App logs System logs Backend Your system bash scripts ruby scripts rsync log file bash python scripts custom loggger cron other custom scripts...
- 9. Blueflood MongoDB Hadoop Metrics Amazon S3 Analysis Archiving MySQL Apache Frontend Access logs syslogd App logs System logs Backend Your system filter / buffer / route
- 10. extensible
- 11. CORE PLUGINS • Divide & Conquer • Buffering & Retries • Error Handling • Message Routing • Parallelism • Read Data • Parse Data • Buffer Data • Write Data • Format Data Common Concerns Use Case Specific
- 12. architecture
- 13. INTERNAL ARCHITECTURE “input-ish” “output-ish” Input Parser Buffer Output FormatterFilter
- 14. Internal Architecture (Simplified) Input Buffer Output Plugin Plugin Plugin 2012-02-04 01:33:51 myapp.buylog { “user”: ”me”, “path”: “/buyItem”, “price”: 150, “referer”: “/landing” } time tag record
- 15. Architecture :: Input plugins Input HTTP+JSON (in_http) File tail (in_tail) Syslog (in_syslog) ... Plugin ✓ Receive logs ✓ Or pull logs from data sources ✓ in non-blocking manner
- 16. Architecture :: Output plugins Plugin ✓ Write or send event logs Output File (out_file) Amazon S3 (out_s3) MongoDB (out_mongo) ...
- 17. Architecture :: Buffer plugins Plugin ✓ Improve performance ✓ Provide reliability ✓ Provide thread-safety Buffer Memory (buf_memory) File (buf_file)
- 18. Architecture :: Buffer plugins Plugin ✓ Improve performance ✓ Provide reliability ✓ Provide thread-safety chunk chunk chunk output Input
- 21. DIVIDE & CONQUER & RETRY error retry error retry retry retry
- 22. reliable process
- 23. THIS?
- 24. OR THIS?
- 25. M X N → M + N Nagios MongoDB Hadoop Alerting Amazon S3 Analysis Archiving MySQL Apache Frontend Access logs syslogd App logs System logs Backend Databases buffer/filter/route
- 26. use cases
- 28. # logs from a file <source> type tail path /var/log/httpd.log format apache2 tag backend.apache </source> # logs from client libraries <source> type forward port 24224 </source> # store logs to MongoDB <match backend.*> type mongo database fluent collection test </match>
- 31. # logs from a file <source> type tail path /var/log/httpd.log format apache2 tag web.access </source> # logs from client libraries <source> type forward port 24224 </source> # store logs to ES and HDFS <match *.*> type copy <store> type elasticsearch logstash_format true </store> <store> type webhdfs host namenode port 50070 path /path/on/hdfs/ </store> </match>
- 32. FLUENTD ON KUBERNETES (NOV 2015)
- 33. FLUENTD LOGGING DRIVER (APR 2015)
- 34. Tweet Again! “Happy v1 #k8s and congrats #Fluentd for becoming a #docker logging driver”
- 35. DEMO: FLUENTD + DOCKER
- 36. THANK YOU! AND TREASURE DATA IS HIRING! WWW.TREASUREDATA.COMC/CAREERS