Macey  Kling

Macey Kling

1598614500

Data Persistent Prometheus-Grafana Intergration with Jenkins

Prometheus is an open-source application monitoring and alerting software solution. It is a web application which can be deployed anywhere — in a PC, virtual machine, or even in a container. It scrapes data from the exporters (small programs convert system data to Prometheus metrics) periodically and records the real-time metrics in a time series database.

On the other hand: Grafana is an open-source metric analytics & visualization application. It is used for visualizing time series data for infrastructure and application analytics.

It is also a web application which can be deployed anywhere that users want. It can target a data source from Prometheus and use its customizable panels to give users powerful visualization of the data from any infrastructure under management.

Why Prometheus + Grafana?

The relationship between Prometheus and** Grafana** is like a backend and frontend for a data monitoring system. Prometheus acts as the data collection and storage backend and Grafana as the interface for analysis and visualization.

One of the significant advantages of Prometheus is the exporter feature. The official Prometheus website has already provided many useful exporters for users, and the Prometheus communities also have many customized exporters built by users themselves. By using different kinds of the exporters, Prometheus is able to monitor applications in all types of environments. This is especially efficient for docker & Kubernetes container environments.

One of the significant advantages of Grafana is its customization features. It’s effortless to customize the visualization for massive amounts of data. Users can choose a linear graph, a single number panel, a gauge, a table, or a heatmap to display their data. They can also sort all their data with various labels; data with different labels will go to different panels. The dashboard can be rearranged and resized simply by dragging panels around. These customization features make Grafana useful and powerful for many different types of users, such as DevOps, Security, Operations, and Networking.

Problem Statement:

Integrate Prometheus and Grafana and perform in following way:

1. Deploy them as pods on top of Kubernetes by creating resources Deployment, Replica Set, Pods or Services.

2. And make their data to be remain persistent.

3. And both of them should be exposed to outside world.

#devops #prometheus #grafana #jenkins #automation #github #docker #data

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Buddha Community

Data Persistent Prometheus-Grafana Intergration with Jenkins
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Macey  Kling

Macey Kling

1598614500

Data Persistent Prometheus-Grafana Intergration with Jenkins

Prometheus is an open-source application monitoring and alerting software solution. It is a web application which can be deployed anywhere — in a PC, virtual machine, or even in a container. It scrapes data from the exporters (small programs convert system data to Prometheus metrics) periodically and records the real-time metrics in a time series database.

On the other hand: Grafana is an open-source metric analytics & visualization application. It is used for visualizing time series data for infrastructure and application analytics.

It is also a web application which can be deployed anywhere that users want. It can target a data source from Prometheus and use its customizable panels to give users powerful visualization of the data from any infrastructure under management.

Why Prometheus + Grafana?

The relationship between Prometheus and** Grafana** is like a backend and frontend for a data monitoring system. Prometheus acts as the data collection and storage backend and Grafana as the interface for analysis and visualization.

One of the significant advantages of Prometheus is the exporter feature. The official Prometheus website has already provided many useful exporters for users, and the Prometheus communities also have many customized exporters built by users themselves. By using different kinds of the exporters, Prometheus is able to monitor applications in all types of environments. This is especially efficient for docker & Kubernetes container environments.

One of the significant advantages of Grafana is its customization features. It’s effortless to customize the visualization for massive amounts of data. Users can choose a linear graph, a single number panel, a gauge, a table, or a heatmap to display their data. They can also sort all their data with various labels; data with different labels will go to different panels. The dashboard can be rearranged and resized simply by dragging panels around. These customization features make Grafana useful and powerful for many different types of users, such as DevOps, Security, Operations, and Networking.

Problem Statement:

Integrate Prometheus and Grafana and perform in following way:

1. Deploy them as pods on top of Kubernetes by creating resources Deployment, Replica Set, Pods or Services.

2. And make their data to be remain persistent.

3. And both of them should be exposed to outside world.

#devops #prometheus #grafana #jenkins #automation #github #docker #data

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling

1597579680

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data