Using FME Server and Engines to Convert Large Amounts of DataSafe Software
We at Hexagon/Intergraph have implemented an interesting process using FME Server and FME Engines to convert large quantities of data across multiple servers.
We are in the process of repeating this for two other major projects.
The implementation contains
1. FME Server Core (with Failover support)
2. Multiple FME Engines on 7 servers
3. C++ Front End application for job submission built with FME Server API
4. Data staged across the servers
5. Load balancing the servers
6. Check pointing the process
7. Redundancy built in
Various other parts built and automated to complete data processing consistently and accurately.
The initial project was successful without any errors over 6 months where there was new data delivered every other week.
А.Храмцовский - Инструкции по установке пакета КРИТ для системы MATLAB, январь 2009 года
Andrew Khramtsovsky "Krit Toolbox for Matlab. Installation Instructions", 2009
Evolving capacity requirements and new technologies like IoT, SDR, vRAN, 5G and OpenRAN are making network management and optimization increasingly complex. With multiple vendors and technologies in play, it is not always easy or cost-effective to simplify. Making it easy requires a lot of innovation to mask real complexity.
As a Performance Management Solution Platform specifically designed for Mobile Networks, both Focus and FocusWeb increase the visibility of all mobile network layer performance (RAN/UTRAN, Transport, Core) through a vendor-free environment, and support all access technologies (GSM, WCDMA and LTE). All-in-one features move Focus and FocusWeb ahead of their competitors.
Wide ranging functionalities including alerts, trends, degraded node data and trend templates make possible to track expert-level technically detailed information and oversee management level information, supported with GIS reporting.
Using FME Server and Engines to Convert Large Amounts of DataSafe Software
We at Hexagon/Intergraph have implemented an interesting process using FME Server and FME Engines to convert large quantities of data across multiple servers.
We are in the process of repeating this for two other major projects.
The implementation contains
1. FME Server Core (with Failover support)
2. Multiple FME Engines on 7 servers
3. C++ Front End application for job submission built with FME Server API
4. Data staged across the servers
5. Load balancing the servers
6. Check pointing the process
7. Redundancy built in
Various other parts built and automated to complete data processing consistently and accurately.
The initial project was successful without any errors over 6 months where there was new data delivered every other week.
А.Храмцовский - Инструкции по установке пакета КРИТ для системы MATLAB, январь 2009 года
Andrew Khramtsovsky "Krit Toolbox for Matlab. Installation Instructions", 2009
Evolving capacity requirements and new technologies like IoT, SDR, vRAN, 5G and OpenRAN are making network management and optimization increasingly complex. With multiple vendors and technologies in play, it is not always easy or cost-effective to simplify. Making it easy requires a lot of innovation to mask real complexity.
As a Performance Management Solution Platform specifically designed for Mobile Networks, both Focus and FocusWeb increase the visibility of all mobile network layer performance (RAN/UTRAN, Transport, Core) through a vendor-free environment, and support all access technologies (GSM, WCDMA and LTE). All-in-one features move Focus and FocusWeb ahead of their competitors.
Wide ranging functionalities including alerts, trends, degraded node data and trend templates make possible to track expert-level technically detailed information and oversee management level information, supported with GIS reporting.
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by FlinkFlink Forward
We are HanSight, a leading security startup based in China. We provide solutions for enterprise cybersecurity with a main focus on User Behavior Analytics(UBA). Typical UBA deployment in large scale enterprise needs to handle 10k+ unique users over 10+ dimensions. Real-time analysis and detection on that scale of data has become a must have functionality yet a challenge for traditional security solutions. Most of the products on the market usually struggles with high throughput(100k TPS) and real-time analysis accuracy. With Flink’s streaming nature, we are able to present a next generation UBA system that tackles the large scale real-time data analysis challenge. Basically, Flink serves as a CEP engine processing data in a streaming fashion. And UBA engine (anomaly detection algorithms, rule engine) runs on top of Flink to achieve dynamic ETL rule configuration and hot deployment. Also we provide a stunning UI design for rule configuration, incident response and system monitoring.
Orbit is an end-to-end data management solution for Extract-Transform-Load (ETL) processes where they can be defined, managed and monitored from a single point with a centralized approach.
Flink Forward Berlin 2018: Timo Walther - "Flink SQL in Action"Flink Forward
SQL is the lingua franca of data processing, and everybody working with data knows SQL. Apache Flink provides SQL support for querying and processing batch and streaming data. Flink's SQL support powers large-scale production systems at Alibaba, Huawei, and Uber. Based on Flink SQL, these companies have built systems for their internal users as well as publicly offered services for paying customers.In my talk I will show how to leverage the simplicity and power of SQL on Flink. I’ll explain why unified batch and stream processing is important and what it means to run SQL queries on streams of data. Once we’ve covered the basics, I will spend the remainder of the talk demonstrating the capabilities of Flink SQL. We will explore different use cases that Flink SQL was designed for by running queries on Flink’s SQL shell. In particular, I will demonstrate the unified batch and streaming engine by running the same query on batch and streaming data and show how to build a real-time dashboard that is powered by a streaming SQL query, which continuously updates an external result table.
Flink Forward Berlin 2018: Ravi Suhag & Sumanth Nakshatrithaya - "Managing Fl...Flink Forward
At GO-JEK, we build products that help millions of Indonesians At GO-JEK, we build products that help millions of Indonesians commute, shop, eat and pay, daily. Data Engineering team is responsible to create a reliable data infrastructure across all of GO-JEK’s 18+ products. We use Flink extensively to provide real-time streaming aggregation and analytics for billions of data points generated on daily basis. Working at such a large scale makes it really important to automate operations from infrastructure, failover, and monitoring. This way we can push features faster without causing chaos and disruption to the production environment. 1. Provisioning and deployment: With the nature of business at GoJek, we found ourselves provisioning Flink clusters quite often. Currently we run around 1000 jobs across 10 clusters for different data streams with increasing number of requests day by day. We also provision on the fly clusters with custom configuration for load testing, experimentation and chaos engineering. Provisioning these many clusters from ground up required lot of man hours and involved setting up virtual machines, monitoring agents, access management, configuration management, load testing and data stream integration. Our current setup has Flink over Yarn clusters as well as Kubernetes. We use our in-house provisioning tool Odin, built on top of Terraform and Chef for Yarn clusters and Kubernetes controllers for Kubernetes based deployments. It enables us to safely and predictably create and modify Flink infrastructure. Odin has helped us reduce provisioning time by 99% despite increasing number of requests. 2. Isolation and access control: Given the real-time and distributed nature of GoJek's services, events are classified into different streams depending on nature, time and transactional criticality, sensitivity and volume of data. Which requires setting up separate clusters based on security concerns, team segregation, job loads and criticality which comes at the cost of handling large volume data replication and maintenance. 3. Data quality control: The quality of ingestion events are controlled by Protobuf based version controlled strict event type schema with fully automated deployment pipeline. Deployed jobs are locked to a certain data schema and version which helps us accidental breaking schema changes and backward compatibility during migration and failover. 4. Monitoring and alerting: All the clusters are monitored using dedicated TICK setup. We monitors clusters for resource utilization, job stats and business impact per job. 5. Failover and Upgrading: Failover and upgrade operations are fully automated for yarn cluster failover, input stream failovers e.g. Kafka failover with stateless job strategies. Which helps us moving jobs from one cluster to another without any data loss or broken metric flow. 6. Chaos engineering and load testing: Loki is our disaster simulation tool that helps ensure the Flink infrastructure can tolerat
Flink Forward Berlin 2018: Oleksandr Nitavskyi - "Data lossless event time st...Flink Forward
One of the main characteristics of the good streaming pipeline is correctness for event time processing. Real challenges become when such pipeline should be resilient to different types of failures. In this talk, we describe how Criteo runs Flink on one of the biggest Yarn clusters in Europe and computes 100k messages per second to acknowledge revenue of our platform within the delay of 5 minutes. Real-time revenue monitoring system calculates data under 1% of discrepancies and minimizes business impact in case of revenue anomalies.
Flink Forward Berlin 2018: Aljoscha Krettek & Till Rohrmann - Keynote: "A Yea...Flink Forward
Stream processing still evolves and changes at a speed that can make it hard to keep up with the developments. Being at the forefront of stream processing technology, the evolution of Apache Flink has mirrored many of these developments and continues to do so.
We will take you on a journey through the major milestones of stream processing technology in past years, diving into the latest additions that Apache Flink and other communities introduced to the stream processing landscape, such as Streamng SQL, Time Versioned Tables, cluster-library-duality, language portability, etc.
We will take a sneak peek into our crystal ball and present in what the Flink community is working on next.
Apache Flink offers a fast, distributed, and failure-tolerant data-processing engine along with APIs for many different use cases, chief among them stateful stream processing. We give a quick overview of the capabilities of Flink before discussing the current state of Flink, the upcoming new release, and future developments.
Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...Flink Forward
Distributed tracing is used to analyze performance and error cases in service oriented architectures. The Observability team at Airbnb recently created Upshot, a data pipeline that uses Flink to analyze over 40 million trace events per minute. Summaries of the resulting data are sent to Druid, Datadog, and other downstream datastores. This talk will focus on how we use Flink and how we analyzed and addressed scaling issues we encountered while building Upshot.
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...Flink Forward
Streaming engines like Apache Flink are redefining ETL and data processing. Data can be extracted, transformed, filtered and written out in real-time with an ease matching that of batch processing. However the real challenge of matching the prowess of batch ETL remains in doing joins, in maintaining state and to have the data be paused or rested dynamically. Netflix has a microservices architecture. Different microservices serve and record different kind of user interactions with the product. Some of these live services generate millions of events per second, all carrying meaningful but often partial information. Things start to get exciting when we want to combine the events coming from one high-traffic microservice to another. Joining these raw events generates rich datasets that are used to train the machine learning models that serve Netflix recommendations. Historically we have done this joining of large volume data-sets in batch. However we asked ourselves if the data is being generated in real-time, why must it not be processed downstream in real time? Why wait a full day to get information from an event that was generated a few mins ago? In this talk, we will share how we solved a complex join of two high-volume event streams using Flink. We will talk about maintaining large state, fault tolerance of a stateful application and strategies for failure recovery.
Flink Forward Berlin 2017: Patrick Gunia - Migration of a realtime stats prod...Flink Forward
Counting things might sound like a trivial thing to do. But counting things consistently at scale can create unique and difficult challenges. At ResearchGate we count things for different reasons. On the one hand we provide numbers to our members to give them insights about their scientific impact and reach. At the same time, we use numbers ourselves as a basis for data-driven product development. We continuously tune our statistics infrastructure to improve our platform, adapt to new business requirements or fix bugs. A milestone in this improvement process has been the strategic decision to move our stats infrastructure from Storm to Flink. This significantly reduced complexity and required resources, including decreasing the load on our database backend by more than 30%. We will discuss the challenges we’ve encountered and overcome on the way, including handling of state and the need for online and offline processing using streaming and batch processors on the same data.
Flink Forward Berlin 2018: Stephan Ewen - Keynote: "Unlocking the next wave o...Flink Forward
Stream Processing as helped to turn many monolithic database-centric applications into fast, scalable, and flexible real time applications. However, there are still entire classes of applications that are built against databases, because today's streaming processing model is not yet rich enough to support these applications.
We present what we believe is still missing in today's stream processing models, and how we envision to evolve stream processing for the next wave of application to be able to move to a stream processing architecture.
Microsoft Infrastructure Monitoring using OpManagerManageEngine
Microsoft is a well known vendor in Enterprise IT market. This presentation explains how to monitor Microsoft products using ManageEngine OpManager. and basic of Microsoft infrastructure monitoring.
FBTFTP: an opensource framework to build dynamic tftp serversAngelo Failla
Talk given at EuroPython2016, Bilbao:
https://ep2016.europython.eu/conference/talks/fbtftp-facebooks-python3-framework-for-tftp-servers
TFTP was first standardized in ’81 (same year I was born!) and one of its primary uses is in the early stage of network booting. TFTP is very simple to implement, and one of the reasons it is still in use is that its small footprint allows engineers to fit the code into very low resource, single board computers, system-on-a-chip implementations and mainboard chipsets, in the case of modern hardware.
It is therefore a crucial protocol deployed in almost every data center environment. It is used, together with DHCP, to chain load Network Boot Programs (NBPs), like Grub2 and iPXE. They allow machines to bootstrap themselves and install operating systems off of the network, downloading kernels and initrds via HTTP and starting them up.
At Facebook, we have been using the standard in.tftpd daemon for years, however, we started to reach its limitations. Limitations that were partially due to our scale and the way TFTP was deployed in our infrastructure, but also to the protocol specifications based on requirements from the 80’s.
To address those limitations we ended up writing our own framework for creating dynamic TFTP servers in Python3, and we decided to open source it.
I will take you thru the framework and the features it offers. I’ll discuss the specific problems that motivated us to create it. We will look at practical examples of how touse it, along with a little code, to build your own server that are tailored to your own infra needs.
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by FlinkFlink Forward
We are HanSight, a leading security startup based in China. We provide solutions for enterprise cybersecurity with a main focus on User Behavior Analytics(UBA). Typical UBA deployment in large scale enterprise needs to handle 10k+ unique users over 10+ dimensions. Real-time analysis and detection on that scale of data has become a must have functionality yet a challenge for traditional security solutions. Most of the products on the market usually struggles with high throughput(100k TPS) and real-time analysis accuracy. With Flink’s streaming nature, we are able to present a next generation UBA system that tackles the large scale real-time data analysis challenge. Basically, Flink serves as a CEP engine processing data in a streaming fashion. And UBA engine (anomaly detection algorithms, rule engine) runs on top of Flink to achieve dynamic ETL rule configuration and hot deployment. Also we provide a stunning UI design for rule configuration, incident response and system monitoring.
Orbit is an end-to-end data management solution for Extract-Transform-Load (ETL) processes where they can be defined, managed and monitored from a single point with a centralized approach.
Flink Forward Berlin 2018: Timo Walther - "Flink SQL in Action"Flink Forward
SQL is the lingua franca of data processing, and everybody working with data knows SQL. Apache Flink provides SQL support for querying and processing batch and streaming data. Flink's SQL support powers large-scale production systems at Alibaba, Huawei, and Uber. Based on Flink SQL, these companies have built systems for their internal users as well as publicly offered services for paying customers.In my talk I will show how to leverage the simplicity and power of SQL on Flink. I’ll explain why unified batch and stream processing is important and what it means to run SQL queries on streams of data. Once we’ve covered the basics, I will spend the remainder of the talk demonstrating the capabilities of Flink SQL. We will explore different use cases that Flink SQL was designed for by running queries on Flink’s SQL shell. In particular, I will demonstrate the unified batch and streaming engine by running the same query on batch and streaming data and show how to build a real-time dashboard that is powered by a streaming SQL query, which continuously updates an external result table.
Flink Forward Berlin 2018: Ravi Suhag & Sumanth Nakshatrithaya - "Managing Fl...Flink Forward
At GO-JEK, we build products that help millions of Indonesians At GO-JEK, we build products that help millions of Indonesians commute, shop, eat and pay, daily. Data Engineering team is responsible to create a reliable data infrastructure across all of GO-JEK’s 18+ products. We use Flink extensively to provide real-time streaming aggregation and analytics for billions of data points generated on daily basis. Working at such a large scale makes it really important to automate operations from infrastructure, failover, and monitoring. This way we can push features faster without causing chaos and disruption to the production environment. 1. Provisioning and deployment: With the nature of business at GoJek, we found ourselves provisioning Flink clusters quite often. Currently we run around 1000 jobs across 10 clusters for different data streams with increasing number of requests day by day. We also provision on the fly clusters with custom configuration for load testing, experimentation and chaos engineering. Provisioning these many clusters from ground up required lot of man hours and involved setting up virtual machines, monitoring agents, access management, configuration management, load testing and data stream integration. Our current setup has Flink over Yarn clusters as well as Kubernetes. We use our in-house provisioning tool Odin, built on top of Terraform and Chef for Yarn clusters and Kubernetes controllers for Kubernetes based deployments. It enables us to safely and predictably create and modify Flink infrastructure. Odin has helped us reduce provisioning time by 99% despite increasing number of requests. 2. Isolation and access control: Given the real-time and distributed nature of GoJek's services, events are classified into different streams depending on nature, time and transactional criticality, sensitivity and volume of data. Which requires setting up separate clusters based on security concerns, team segregation, job loads and criticality which comes at the cost of handling large volume data replication and maintenance. 3. Data quality control: The quality of ingestion events are controlled by Protobuf based version controlled strict event type schema with fully automated deployment pipeline. Deployed jobs are locked to a certain data schema and version which helps us accidental breaking schema changes and backward compatibility during migration and failover. 4. Monitoring and alerting: All the clusters are monitored using dedicated TICK setup. We monitors clusters for resource utilization, job stats and business impact per job. 5. Failover and Upgrading: Failover and upgrade operations are fully automated for yarn cluster failover, input stream failovers e.g. Kafka failover with stateless job strategies. Which helps us moving jobs from one cluster to another without any data loss or broken metric flow. 6. Chaos engineering and load testing: Loki is our disaster simulation tool that helps ensure the Flink infrastructure can tolerat
Flink Forward Berlin 2018: Oleksandr Nitavskyi - "Data lossless event time st...Flink Forward
One of the main characteristics of the good streaming pipeline is correctness for event time processing. Real challenges become when such pipeline should be resilient to different types of failures. In this talk, we describe how Criteo runs Flink on one of the biggest Yarn clusters in Europe and computes 100k messages per second to acknowledge revenue of our platform within the delay of 5 minutes. Real-time revenue monitoring system calculates data under 1% of discrepancies and minimizes business impact in case of revenue anomalies.
Flink Forward Berlin 2018: Aljoscha Krettek & Till Rohrmann - Keynote: "A Yea...Flink Forward
Stream processing still evolves and changes at a speed that can make it hard to keep up with the developments. Being at the forefront of stream processing technology, the evolution of Apache Flink has mirrored many of these developments and continues to do so.
We will take you on a journey through the major milestones of stream processing technology in past years, diving into the latest additions that Apache Flink and other communities introduced to the stream processing landscape, such as Streamng SQL, Time Versioned Tables, cluster-library-duality, language portability, etc.
We will take a sneak peek into our crystal ball and present in what the Flink community is working on next.
Apache Flink offers a fast, distributed, and failure-tolerant data-processing engine along with APIs for many different use cases, chief among them stateful stream processing. We give a quick overview of the capabilities of Flink before discussing the current state of Flink, the upcoming new release, and future developments.
Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...Flink Forward
Distributed tracing is used to analyze performance and error cases in service oriented architectures. The Observability team at Airbnb recently created Upshot, a data pipeline that uses Flink to analyze over 40 million trace events per minute. Summaries of the resulting data are sent to Druid, Datadog, and other downstream datastores. This talk will focus on how we use Flink and how we analyzed and addressed scaling issues we encountered while building Upshot.
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...Flink Forward
Streaming engines like Apache Flink are redefining ETL and data processing. Data can be extracted, transformed, filtered and written out in real-time with an ease matching that of batch processing. However the real challenge of matching the prowess of batch ETL remains in doing joins, in maintaining state and to have the data be paused or rested dynamically. Netflix has a microservices architecture. Different microservices serve and record different kind of user interactions with the product. Some of these live services generate millions of events per second, all carrying meaningful but often partial information. Things start to get exciting when we want to combine the events coming from one high-traffic microservice to another. Joining these raw events generates rich datasets that are used to train the machine learning models that serve Netflix recommendations. Historically we have done this joining of large volume data-sets in batch. However we asked ourselves if the data is being generated in real-time, why must it not be processed downstream in real time? Why wait a full day to get information from an event that was generated a few mins ago? In this talk, we will share how we solved a complex join of two high-volume event streams using Flink. We will talk about maintaining large state, fault tolerance of a stateful application and strategies for failure recovery.
Flink Forward Berlin 2017: Patrick Gunia - Migration of a realtime stats prod...Flink Forward
Counting things might sound like a trivial thing to do. But counting things consistently at scale can create unique and difficult challenges. At ResearchGate we count things for different reasons. On the one hand we provide numbers to our members to give them insights about their scientific impact and reach. At the same time, we use numbers ourselves as a basis for data-driven product development. We continuously tune our statistics infrastructure to improve our platform, adapt to new business requirements or fix bugs. A milestone in this improvement process has been the strategic decision to move our stats infrastructure from Storm to Flink. This significantly reduced complexity and required resources, including decreasing the load on our database backend by more than 30%. We will discuss the challenges we’ve encountered and overcome on the way, including handling of state and the need for online and offline processing using streaming and batch processors on the same data.
Flink Forward Berlin 2018: Stephan Ewen - Keynote: "Unlocking the next wave o...Flink Forward
Stream Processing as helped to turn many monolithic database-centric applications into fast, scalable, and flexible real time applications. However, there are still entire classes of applications that are built against databases, because today's streaming processing model is not yet rich enough to support these applications.
We present what we believe is still missing in today's stream processing models, and how we envision to evolve stream processing for the next wave of application to be able to move to a stream processing architecture.
Microsoft Infrastructure Monitoring using OpManagerManageEngine
Microsoft is a well known vendor in Enterprise IT market. This presentation explains how to monitor Microsoft products using ManageEngine OpManager. and basic of Microsoft infrastructure monitoring.
FBTFTP: an opensource framework to build dynamic tftp serversAngelo Failla
Talk given at EuroPython2016, Bilbao:
https://ep2016.europython.eu/conference/talks/fbtftp-facebooks-python3-framework-for-tftp-servers
TFTP was first standardized in ’81 (same year I was born!) and one of its primary uses is in the early stage of network booting. TFTP is very simple to implement, and one of the reasons it is still in use is that its small footprint allows engineers to fit the code into very low resource, single board computers, system-on-a-chip implementations and mainboard chipsets, in the case of modern hardware.
It is therefore a crucial protocol deployed in almost every data center environment. It is used, together with DHCP, to chain load Network Boot Programs (NBPs), like Grub2 and iPXE. They allow machines to bootstrap themselves and install operating systems off of the network, downloading kernels and initrds via HTTP and starting them up.
At Facebook, we have been using the standard in.tftpd daemon for years, however, we started to reach its limitations. Limitations that were partially due to our scale and the way TFTP was deployed in our infrastructure, but also to the protocol specifications based on requirements from the 80’s.
To address those limitations we ended up writing our own framework for creating dynamic TFTP servers in Python3, and we decided to open source it.
I will take you thru the framework and the features it offers. I’ll discuss the specific problems that motivated us to create it. We will look at practical examples of how touse it, along with a little code, to build your own server that are tailored to your own infra needs.
Data Transformations on Ops Metrics using Kafka Streams (Srividhya Ramachandr...confluent
How Priceline uses Kafka Streams technology to effectively save TBs on daily licenses of our monitoring systems. Kafka Streams powers a big part of our analytics and monitoring pipelines and delivers operational metrics transformations in real time. All logs and operational metrics from all of the APIs of Priceline’s products flow into Kafka and is ingested into our Monitoring System Splunk for Alerting and Monitoring. We have now implemented data transformations, aggregations and summarizations using Kafka Streams technologies to effectively eliminate PCI/PII violations on the log data; do aggregations on metrics to avoid ingesting sub-second metrics and ingest metrics only at the granularity that we need to. We will cover the need for custom Serdes, custom partitioners, and why we don’t use the confluent registry. You will also learn how Priceline uses a self service model to configure its streams, topics and consumers using Data Collection Console, which is our UI for managing the Kafka streaming pipelines.
DEVNET-1175 OpenDaylight Service Function ChainingCisco DevNet
This tutorial will overview the OpenDaylight Service Function Chaining (SFC) architecture, implementation and operation. A description of the SFC components and the Network Service Header (NSH) will be presented. This talk will conclude with a step-by-step demonstration of SFC configuration and operation using the GUI and REST interfaces.
Near real-time anomaly detection at Lyftmarkgrover
Near real-time anomaly detection at Lyft, by Mark Grover and Thomas Weise at Strata NY 2018.
https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/69155
Access Assurance Suite Tips & Tricks - Lisa Lombardo Principal Architect Iden...Core Security
Everyone loves a good tip, like using toothpaste to clear up hazy car headlights. In this session, Identity users will learn from the master, our lead architect, Lisa Lombardo, as she goes through tips and tricks to make sure you’re getting the most out of your IAM deployment. Come with your questions about Core Access, Core Compliance, and Core Password.
MeetUp Monitoring with Prometheus and Grafana (September 2018)Lucas Jellema
This presentation introduces the concept of monitoring - focusing on why and how and finally on the tools to use. It introduces Prometheus (metrics gathering, processing, alerting), application instrumentation and Prometheus exporters and finally it introduces Grafana as a common companion for dashboarding, alerting and notifications. This presentations also introduces the handson workshop - for which materials are available from https://github.com/lucasjellema/monitoring-workshop-prometheus-grafana
Slides from the recording of April Mainframe Virtual User Group with our special guest from TCF Bank. Troy Tomlinson, AVP of Operations, shares the bank's journey from legacy version control systems and lack of visibility to complete control using ChangeMan ZMF. Troy discusses the issues and challenges that drove the decision to upgrade to Serena’s solution and how the bank has benefited from implementing ChangeMan ZMF on Z/OS.
The need for gleaning answers from unbounded data streams is moving from nicety to a necessity. Netflix is a data driven company, and has a need to process over 1 trillion events a day amounting to 3 PB of data to derive business insights.
To ease extracting insight, we are building a self-serve, scalable, fault-tolerant, multi-tenant "Stream Processing as a Service" platform so the user can focus on data analysis. I'll share our experience using Flink to help build the platform.
Learn how Site24x7 gives you end-to-end application performance visibility for your Java, .NET and Ruby web transactions with metrics of all components starting from URLs to SQL queries.
Flopsar, basado en el concepto de APM (application performance monitoring), es además una herramienta para análisis conductural de aplicaciones y procesos de negocio. Se destaca por su interfaz principal, que es muy innovadora e intuitiva.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
4. RightFax Management Pack
Features
Monitoring
• Availability
– Database
– Common Disk Storage
– SNMP agent
• Licensing for RightFax SNMP agent
• Listen to all RightFax SNMP traps (with
duplicate suppression)
• Current Faxes Queue against configurable
threshold
• RightFax Performance Counters collection
– Available Disk Space %
– Internal Event Queue in Use %
– Faxes Sent
– Faxes Received
– Faxes Scheduled
– Faxes Queued
– Pages Queued
• Tasks - get Time Running for RightFax
critical services
Discoveries
• RightFax deployment(s) - common
database
• RightFax Fax Server
• RightFax WorkServer and WorkServer
Module(s) (local or remote)
• RightFax DocTransport (local or remote)
• Common Disk Storage
• RightFax Database
• RightFax SNMP Agent
5. Infront RightFax Management
Pack Features (cont)
Monitoring (cont)
• Services (all RightFax services per roles)
– RightFax Database Module Service
– RightFax Doc Transport Module Service
– RightFax Email Gateway Module Service
– RightFax eTransport Module Service
– RightFax Integration Module Service
– RightFax Paging Server Module Service
– RightFax Queue Handler Service
– RightFax Remoting Service
– RightFax RPC Server Module Service
– RightFax Server Module Service
– RightFax Work Server Module Services
Views
• RightFax All Alerts
• RightFax All Performance Counters
• RightFax Deployments Diagram
• RightFax DocTransport Server State
• RightFax Fax Servers State
• RightFax WorkServer Module State