How do you analyze data visually with cloud tools?

1 Choose a cloud platform

The first step is to choose a cloud platform that suits your data needs and preferences. There are many options available, but some of the most widely used are Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure. Each platform offers a range of tools and services for data storage, processing, analytics, and visualization, as well as security, scalability, and integration features. You can compare the pricing, functionality, and compatibility of different platforms before making a decision.

2 Upload or connect your data

The next step is to upload or connect your data to the cloud platform. Depending on the type and source of your data, you can use different methods and formats to transfer it to the cloud. For example, you can use CSV, JSON, XML, or other file formats to upload data from your local machine or a web server. You can also use APIs, SDKs, or connectors to access data from external databases, applications, or services. You can also use cloud storage services, such as Google Cloud Storage, Amazon S3, or Azure Blob Storage, to store and manage your data in the cloud.

3 Explore and prepare your data

The third step is to explore and prepare your data for analysis and visualization. You can use various cloud tools and services to perform data cleaning, transformation, aggregation, and filtering tasks. For example, you can use Google Data Studio, Amazon QuickSight, or Azure Data Explorer to query, manipulate, and visualize your data using SQL or other languages. You can also use Google BigQuery, Amazon Redshift, or Azure Synapse Analytics to store and analyze large-scale data using data warehouses and data lakes.

4 Choose a visualization tool

The fourth step is to choose a visualization tool that allows you to create and customize data visualizations according to your goals and audience. There are many cloud-based tools and services that offer different types of charts, graphs, maps, dashboards, and reports. For example, you can use Google Data Studio, Amazon QuickSight, or Azure Power BI to create interactive and dynamic data visualizations using drag-and-drop features and templates. You can also use Google Charts, Amazon Quicksight ML Insights, or Azure Machine Learning Studio to create advanced data visualizations using machine learning and artificial intelligence.

5 Publish and share your data visualizations

The final step is to publish and share your data visualizations with your stakeholders, clients, or collaborators. You can use various cloud tools and services to export, embed, or share your data visualizations in different formats and platforms. For example, you can use Google Data Studio, Amazon QuickSight, or Azure Power BI to export your data visualizations as PDF, PNG, or CSV files. You can also use Google Data Studio, Amazon QuickSight, or Azure Power BI to embed or share your data visualizations via email, web, or social media. You can also use Google Cloud Pub/Sub, Amazon SNS, or Azure Event Grid to send notifications or alerts based on your data visualizations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?