Kibana is a data visualizer tool of ELK. As a beginner of KLK, you have to know this tool inevitably. However, Kibana isn’t the only tool to visualize elasticsearch results. There are many other tools, like as Grafana and others will help you, if you are more familiar with them. In this article, we will step into the world of Kibana, trying to know what is Kibana, and how to use it.
Kibana is an exploration and visualization tool. You can use it to analyze elasticsearch’s logs or time series data and monitor the specific application. Different from Grafana, Kibana needs to rely on the elasticsearch service. By default, it will listen to the elasticsearch service on port 9200, and start the service on port 5601.(Of course, like as elasticsearch, you can customize these configuration at specific yaml file.)
brew tap elastic/tap brew install elastic/tap/kibana-full
download kibana zip file from this, and unzip it following Kibana tutorial
curl -O https://artifacts.elastic.co/downloads/kibana/kibana-7.8.0-linux-x86_64.tar.gz curl https://artifacts.elastic.co/downloads/kibana/kibana-7.8.0-linux-x86_64.tar.gz.sha512 | shasum -a 512 -c - tar -xzf kibana-7.8.0-linux-x86_64.tar.gz
image credit: https://www.youtube.com/watch?v=gQ1c1uILyKI
Before learning how to use Kibana, I think we must to know why Kibana and what things that Kibana could help us. Base on
edurekal’s video, there are three features in Kibana.
First, “enable search and interact with elasticsearch”. We could request elasticsearch RestFulapis with devtools in Kibana. Besides, Kibana also provides us caching our query syntax, that means, even you close your browser without saving, you could use the caching query syntax at the next time. Second, “allow performing advanced analytics and creation of reports”. With Kibana, we could analyze our raw data advanced and discover more data properties which is not focused on by us. Last, **“enable creation and sharing dynamic dashboards that get updated in realtime”. **Undoubtedly, this is the most well-known feature in Kibana.
We could use dev_tools to request elasticsearch apis, and get readable output data from Kibana.
grok filterto parse input logs at
Logstash. Kibana supports us using the GUI board to test our grok syntax. It will make our debugging time short.
Monitoring, we can get an overview of the current status of elasticsearch and Kibana.
After setting index patterns from Kibana management, you could find the time distribution of indices, as well as details of each piece of data on
Visualize make your data visualize, which means translate raw data into various chart you need.
2. brings the data to life: We could create a nested chart and map with geoips to enrich our charts.
Once adding the
dashboard, no matter when you open the Kibana webpage, you could get real-time data graphes.
As the visualizer and log analyzer in ELK system, Kibana includes of many functions which is highly compatible with elasticsearch to help us injecting new meaning into raw data. This article just introduce you guys some basic operations; therefore, you could explore other rich functions in Kibana continuously(There are multiple functions at the newest version of Kibana).
This article just share a few of Kibana’s usual features with you. If there is something wrong or defects, please leave a message to me. What’s more, I would mostly welcome your discussion with me at topics of ELK.
#elk-stack #elasticsearch #dashboard #kibana #visualization
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
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.
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
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
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-
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
Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:
Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.
#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data