Build a Monitoring Dashboard With QuestDB and Grafana | Time Series Data, Faster

In this tutorial you will learn how to use QuestDB as a data source for your Grafana dashboards and create visualizations using aggregate functions and sampling

What Is Grafana?

Grafana is an open-source visualization tool. It consists of a server that connects to one or more data-sources to retrieve data, which is then visualized by the user in a browser.

Grafana’s Concepts

Before going ahead, let’s review 3 essential Grafana concepts that we will use in this tutorial:

1. Data source - this is how you tell Grafana where your data is stored and how you want to access it. For the purposes of our tutorial, we will have a QuestDB server running and we will access it via Postgres Wire using the PostgreSQL data source plugin.
2. Dashboard - A group of widgets that are displayed together on the same screen.
3. Panel - A single visualization. Think of it as a graph/table.

Setup

Running Grafana

docker run -p 3000:3000 grafana/grafana

Once the Grafana server has started, you can access it via port 3000 (http://locahost:3000).

Running QuestDB

docker run -p 8812:8812 questdb/questdb

Downloading the Dataset

On our live demo, you can find 10+ years of taxi data. For this tutorial, we have a subset of that data, the data for the whole of February 2018. You can download the compressed dataset from Amazon S3.

Importing the Dataset

Now that we have the dataset, you can import the data by following out the documentation.

#database #tutorial #grafana #time series data #visualizations #data-science

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Build a Monitoring Dashboard With QuestDB and Grafana | Time Series Data, Faster

In this tutorial you will learn how to use QuestDB as a data source for your Grafana dashboards and create visualizations using aggregate functions and sampling

What Is Grafana?

Grafana is an open-source visualization tool. It consists of a server that connects to one or more data-sources to retrieve data, which is then visualized by the user in a browser.

Grafana’s Concepts

Before going ahead, let’s review 3 essential Grafana concepts that we will use in this tutorial:

1. Data source - this is how you tell Grafana where your data is stored and how you want to access it. For the purposes of our tutorial, we will have a QuestDB server running and we will access it via Postgres Wire using the PostgreSQL data source plugin.
2. Dashboard - A group of widgets that are displayed together on the same screen.
3. Panel - A single visualization. Think of it as a graph/table.

Setup

Running Grafana

docker run -p 3000:3000 grafana/grafana

Once the Grafana server has started, you can access it via port 3000 (http://locahost:3000).

Running QuestDB

docker run -p 8812:8812 questdb/questdb

Downloading the Dataset

On our live demo, you can find 10+ years of taxi data. For this tutorial, we have a subset of that data, the data for the whole of February 2018. You can download the compressed dataset from Amazon S3.

Importing the Dataset

Now that we have the dataset, you can import the data by following out the documentation.

#database #tutorial #grafana #time series data #visualizations #data-science

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

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).

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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

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Operational Analytics: Building a Real-time Data Environment for Business

Disruptive technologies, cloud computing and IoT devices continue to evolve and proliferate. As a result, businesses are generating and collecting more data than ever before. However, the challenge here is not gathering the data, but using it in the right way. Businesses are leveraging futuristic analytics features to better understand the data. One such solution is operational analytics.

Data is exponentially increasing every movement. Every time a customer interacts with a website or device, an unimaginable amount of data is generated. Meanwhile, when employees use a company-issued tablet or device to do their jobs, they add more data to the company’s data house. The data goes useless if it is not utilized properly. Henceforth, businesses are adopting operational analytics to increase workplace efficiency, driving competitive advantages, and delighting customers. Operational analytics is at the beginning of gaining ground in the business industry. A survey conducted by Capgemini Consulting on around 600 executives from the US, Europe and China suggests that over 70% of organizations now put more emphasis on operations than on consumer-focused processes for their analytics initiatives. However, only 39% of organizations in the survey said they have extensively integrated their operational analytics initiatives with their business processes and barely 29% of them have successfully achieved their desired objectives from their initiatives.

#big data #data management #latest news #operational analytics: building a real-time data environment for business #operational analytics #building a real-time data environment for business