Amazon QuickSight S3 Data Connection and Visualization Guide

Amazon QuickSight is an fast, cloud-powered business intelligence service that allows you to easily and quickly visualize and analyze data stored in Amazon S3.

To connect your S3 data to QuickSight, you'll need to create an S3 data source, which can be done in just a few clicks.

You can connect to an S3 bucket that's in the same AWS Region as your QuickSight account.

To connect Amazon S3 to Amazon QuickSight, you'll need to create a connection through CData Connect Cloud. This involves logging into Connect Cloud, clicking Connections, and adding a new connection.

You can connect to Amazon S3 using either an administrator account or an IAM user with custom permissions. To do this, set the AccessKey to the access key ID and the SecretKey to the secret access key. It's recommended to use IAM user credentials for accessing AWS services.

To use Amazon S3 as a data source, a manifest file is required. This file contains information about the files to select for the datasets and meta information such as the availability of header or delimiters. The manifest file should specify up to 1000 files and be located in a readable location by the QuickSight service role.

Here are some common data sources and connections for Amazon QuickSight:

Connecting to S3 is a straightforward process that requires a few simple steps. To start, you'll need to create a connection to Amazon S3 in the CData Connect Cloud.

To authorize Amazon S3 requests, you'll need to provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id and SecretKey to the secret access key. You can connect as the AWS account administrator, but it's recommended to use IAM user credentials to access AWS services.

To configure the connection, log into Connect Cloud, click Connections, and click Add Connection. Then, select "Amazon S3" from the Add Connection panel and enter the necessary authentication properties.

Here's a step-by-step guide to creating a connection to Amazon S3:

1. Log into Connect Cloud and click Connections.

2. Click Add Connection and select "Amazon S3".

3. Enter the necessary authentication properties, including AccessKey and SecretKey.

4. Click Create & Test to create the connection.

Once you've created the connection, you can navigate to the Permissions tab and update the User-based permissions. This will ensure that your connection is secure and compliant with AWS policies.

With the connection configured, you're ready to connect to Amazon S3 data from Amazon QuickSight. Simply follow the instructions in the QuickSight console to create a new data set and choose Amazon S3 as the data source.

Sets are a crucial part of working with data sources and connections. A QuickSight dataset identifies the specific data in a data source that we want to use.

A QuickSight dataset can be directly queried from its respective data source or stored in Amazon QuickSight SPICE for faster processing. Direct query mode has a timeout of 2 minutes, but some database drivers like Redshift may not react to it.

To create a dataset, we need to create a physical table that describes the data at the input level. Physical tables from non-JSON files in S3 can only have STRING as input column type.

We can build a QuickSight dataset by declaring the physical table and casting columns to the real data types if needed. Supported data types are BIT, BOOLEAN, DATETIME, DECIMAL, INTEGER, STRING, and JSON.

Data visualization is a crucial aspect of data analysis, and Amazon QuickSight makes it incredibly easy to create interactive dashboards and visualizations on top of your Amazon S3 data.

With QuickSight, you can import your data into SPICE, a serverless, columnar storage service that allows for faster analytics, and then build a simple visualization from the data. To do this, you can select fields to visualize and a visual type, such as a bar chart or line graph.

QuickSight also integrates well with Amazon SageMaker, allowing you to import data from any data source and select the appropriate SageMaker model for prediction without having to write any code.

You can use QuickSight's embedded analytics feature to embed interactive data visualizations and analytics into existing applications, without custom development or specialized knowledge. This is especially useful for companies with a growing user base, as QuickSight's serverless architecture helps scale insights with a growing user base.

QuickSight offers a variety of Application Programming Interface's (API) to efficiently integrate dashboards into an application, manage use and group API operations and customize the entire experience. Two of these API operations are GetDashboardEmbedUrl and GetSessionEmbedUrl.

Here are some of the key features of QuickSight's embedded analytics:

QuickSight's Enterprise edition also supports Machine Learning (ML) Insights, which can help customers understand data at a glance, share findings and inform better decisions to achieve goals. ML Insights leverages AWS's proven ML and natural language capabilities to help customers gain deeper insights from their data.

Embedded analytics is a powerful feature in Amazon QuickSight that allows you to embed dashboards and analytics into your existing applications without the need for custom development.

QuickSight's embedded analytics is a cost-effective solution that saves time and money by eliminating the need to develop or manage servers or complex data engineering pipelines.

With QuickSight's embedded analytics, you can embed a dashboard or the entire QuickSight experience into your application, giving users rich interactive data visualizations and analytics without requiring specialized knowledge.

This feature enables users to perform advanced analytics with capabilities such as ad-hoc analyses and ML-based insights, all while scaling insights with a growing user base thanks to QuickSight's serverless architecture.

QuickSight offers a variety of API operations to efficiently integrate dashboards into your application, manage user access, and customize the experience.

Here are the two main API operations available:

By using QuickSight's embedded analytics, you can empower your users to discover actionable insights without the complexities of a separate BI tool, all while enjoying a scalable and cost-effective solution.

To use Amazon S3 as a data source, you'll need to create a manifest file that provides information about the files to select for the datasets and meta information like header or delimiters.

At least one of fileLocations.URIs or fileLocations.URIPrefixes should be provided in the manifest file.

A manifest file for the titanic dataset looks like this, with no text qualifier required since the titanic file doesn't need one.

QuickSight service roles should have read access to the manifest file, as well as files and folders listed in the manifest.

Quicksight expects manifest files to specify up to 1000 files.

To upload the manifest, place it in a bucket named $bucket_name, with the key $manifest_key, and make sure it's located in a folder that's readable by the QuickSight service role, such as $bucket_name/files/.