In part one, we learned how to ingest, transform, and enrich raw, semi-structured data, in multiple formats, using Amazon S3, AWS Glue, Amazon Athena, and AWS Lambda. We built an S3-based data lake and learned how AWS leverages open-source technologies, including Presto, Apache Hive, and Apache Parquet. In part two of this post, we will use the transformed and enriched data sources, stored in the data lake, to create compelling visualizations using Amazon QuickSight.
If you recall the demonstration from part one of the post, we had adopted the persona of a large, US-based electric energy provider. The energy provider had developed and sold its next-generation Smart Electrical Monitoring Hub (Smart Hub) to residential customers. Customers can analyze their electrical usage with a fine level of granularity, per device and over time. The goal of the Smart Hub is to enable the customers, using data, to reduce their electrical costs. The provider benefits from a reduction in load on the existing electrical grid and a better distribution of daily electrical load as customers shift usage to off-peak times to save money.
Data Visualization and BI
The data analysis process in the demonstration was divided into four logical stages: 1) Raw Data Ingestion, 2) Data Transformation, 3) Data Enrichment, and 4) Data Visualization and Business Intelligence (BI).
In the final, Data Visualization and Business Intelligence (BI) stage, the enriched data is presented and analyzed. There are many enterprise-grade services available for data visualization and business intelligence, which integrate with Amazon Athena. Amazon services include Amazon QuickSight, Amazon EMR, and Amazon SageMaker. Third-party solutions from AWS Partners, many available on the AWS Marketplace, include Tableau, Looker, Sisense, and Domo.
In this demonstration, we will focus on Amazon QuickSight. Amazon QuickSight is a fully managed business intelligence (BI) service. QuickSight lets you create and publish interactive dashboards that include ML Insights. Dashboards can be accessed from any device, and embedded into your applications, portals, and websites. QuickSight serverlessly scales automatically from tens of users to tens of thousands without any infrastructure management.
Amazon recently added a full set of
aws quicksight APIs for interacting with QuickSight. For example, to preview the three QuickSight data sets created for this part of the demo, with the AWS CLI, we would use the
|aws quicksight list-data-sets –aws-account-id 123456789012|
To examine details of a single data set, with the AWS CLI, we would use the
|aws quicksight describe-data-set \|
|–aws-account-id 123456789012 \|
However, for this final part of the demonstration, we will be working from the Amazon QuickSight Console, as opposed to the AWS CLI, AWS CDK, or CloudFormation templates.
Signing Up for QuickSight
To use Amazon QuickSight, you must sign up for QuickSight.
There are two Editions of Amazon QuickSight, Standard and Enterprise. For this demonstration, the Standard Edition will suffice.
QuickSight Data Sets
Amazon QuickSight uses Data Sets as the basis for all data visualizations. According to AWS, QuickSight data sets can be created from a wide variety of data sources, including Amazon RDS, Amazon Aurora, Amazon Redshift, Amazon Athena, and Amazon S3. You can also upload Excel spreadsheets or flat files (CSV, TSV, CLF, ELF, and JSON), connect to on-premises databases like SQL Server, MySQL, and PostgreSQL and import data from SaaS applications like Salesforce. Below, we see a list of the latest data sources available in the QuickSight New Data Set Console.
Demonstration Data Sets
For the demonstration, I have created three QuickSight data sets, all based on Amazon Athena. You have two options when using Amazon Athena as a data source. The first option is to select a table from an AWS Glue Data Catalog database, such as the database we created in part one of the post, ‘smart_hub_data_catalog.’ The second option is to create a custom SQL query, based on one or more tables in an AWS Glue Data Catalog database.
Of the three data sets created for part two of this demonstration, two data sets use tables directly from the Data Catalog, including ‘etl_output_parquet’ and ‘electricity_rates_parquet.’ The third data set uses a custom SQL query, based on the single Data Catalog table, ‘smart_hub_locations_parquet.’ All three tables used to create the data sets represent the enriched, highly efficient Parquet-format data sources in the S3-based Data Lake.
Data Set Features
There are a large number of features available when creating and configuring data sets. We cannot possibly cover all of them in this post. Let’s look at three features: geospatial field types, calculated fields, and custom SQL.
Geospatial Data Types
QuickSight can intelligently detect common types of geographic fields in a data source and assign QuickSight geographic data type, including Country, County, City, Postcode, and State. QuickSight can also detect geospatial data, including Latitude and Longitude. We will take advantage of this QuickSight feature for our three data set’s data sources, including the State, Postcode, Latitude, and Longitude field types.
A commonly-used QuickSight data set feature is the ‘Calculated field.’ For the ‘etl_output_parquet’ data set, I have created a new field (column),
cost field is the electrical cost of the device, over a five minute time interval, in cents (¢). The calculated
cost_dollar field is the quotient of the
cost field divided by 100. This value represents the electrical cost of the device, over a five minute time interval, in dollars ($). This is a straightforward example. However, a calculated field can be very complex, built from multiple arithmetic, comparison, and conditional functions, string calculations, and data set fields.
Data set calculated fields can also be created and edited from the QuickSight Analysis Console (discussed later).
The third QuickSight data set is based on an Amazon Athena custom SQL query.
|SELECT lon, lat, postcode, hash, tz, state|
Although you can write queries in the QuickSight Data Prep Console, I prefer to write custom Athena queries using the Athena Query Editor. Using the Editor, you can write, run, debug, and optimize queries to ensure they function correctly, first.
The Athena query can then be pasted into the Custom SQL window. Clicking ‘Finish’ in the window is the equivalent of ‘Run query’ in the Athena Query Editor Console. The query runs and returns data.
Similar to the Athena Query Editor, queries executed in the QuickSight Data Prep Console will show up in the Athena History tab, with a
/* QuickSight */ comment prefix.
You will notice the three QuickSight data sets are labeled, ‘SPICE.’ According to AWS, the acronym, SPICE, stands for ‘Super-fast, Parallel, In-memory, Calculation Engine.’ QuickSight’s in-memory calculation engine, SPICE, achieves blazing fast performance at scale. SPICE automatically replicates data for high availability allowing thousands of users to simultaneously perform fast, interactive analysis while shielding your underlying data infrastructure, saving you time and resources. With the Standard Edition of QuickSight, as the first Author, you get 1 GB of SPICE in-memory data for free.
The QuickSight Analysis Console is where Analyses are created. A specific QuickSight Analysis will contain a collection of data sets and data visualizations (visuals). Each visual is associated with a single data set.
Types of QuickSight Analysis visuals include: horizontal and vertical, single and stacked bar charts, line graphs, combination charts, area line charts, scatter plots, heat maps, pie and donut charts, tree maps, pivot tables, gauges, key performance indicators (KPI), geospatial diagrams, and word clouds. Individual visual titles, legends, axis, and other visual aspects can be easily modified. Visuals can contain drill-downs.
A data set’s fields can be modified from within the Analysis Console. Field types and formats, such as date, numeric, currency fields, can be customized for display. The Analysis can include a Title and subtitle. There are some customizable themes available to change the overall look of the Analysis.
Data displayed in the visuals can be further shaped using a combination of Filters, Conditional formatting, and Parameters. Below, we see an example of a typical filter based on a range of dates and times. The data set contains two full days’ worth of data. Here, we are filtering the data to a 14-hour peak electrical usage period, between 8 AM and 10 PM on the same day, 12/21/2019.
Drill-Down, Drill-Up, Focus, and Exclude
According to AWS, all visual types except pivot tables offer the ability to create a hierarchy of fields for a visual element. The hierarchy lets you drill down or up to see data at different levels of the hierarchy. Focus allows you to concentrate on a single element within a hierarchy of fields. Exclude allows you to remove an element from a hierarchy of fields. Below, we see an example of all four of these features, available to apply to the ‘Central Air Conditioner’. Since the AC unit is the largest consumer of electricity on average per day, applying these filters to understand its impact on the overall electrical usage may be useful to an analysis. We can also drill down to minutes from hours or up to days from hours.
Example QuickSight Analysis
A QuickSight Analysis is shared by the Analysis Author as a QuickSight Dashboard. Below, we see an example of a QuickSight Dashboard, built and shared for this demonstration. The ‘Residential Electrical Usage Analysis’ is built from the three data sets created earlier. From those data sets, we have constructed several visuals, including a geospatial diagram, donut chart, heat map, KPI, combination chart, stacked vertical bar chart, and line graph. Each visual’s title, layout, and field display has all customized. The data displayed in the visuals have been filtered differently, including by date and time, by customer id (loc_id), and by state. Conditional formatting is used to enhance the visual appearance of visuals, such as the ‘Total Electrical Cost’ KPI.
In part one, we learned how to ingest, transform, and enrich raw, semi-structured data, in multiple formats, using Amazon S3, AWS Glue, Amazon Athena, and AWS Lambda. We built an S3-based data lake and learned how AWS leverages open-source technologies, including Presto, Apache Hive, and Apache Parquet. In part two of this post, we used the transformed and enriched datasets, stored in the data lake, to create compelling visualizations using Amazon QuickSight.
All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.