- Case Study -
How We Transformed Legacy Dashboards to Amazon QuickSight
The Evolution of SchoolTool Dashboards to Advanced Analytics
Back in 2009, we introduced SchoolTool Dashboards (IQ) with a strong focus on providing valuable educational insights. However, we encountered a set of challenges – our dashboards required time-consuming updates, featured an outdated interface, and heavily relied on a dedicated development team. Determined to overcome these hurdles, we made the decision to revamp our dashboards into a scalable, cloud-based solution.
Fast forward to today, and we're thrilled to share the exciting success of our initial SchoolTool Advanced Analytics rollouts. Leveraging the capabilities of Amazon QuickSight, our pilot test dashboards are now live and fully customizable by Regional Information Centers (RICs) and districts. We are now able to respond to dashboard requirements quickly and seamlessly integrate third-party data.
In this case study, we will share our experience of transitioning from custom JavaScript legacy dashboards to Amazon QuickSight. This move not only makes data more accessible for everyone but also accelerates deployment and eliminates the need for a dedicated development team.
Opportunity
The SchoolTool dashboards, originally introduced almost 15 years ago and integral to the SchoolTool product, have been managed by a small development team. However, the challenge lay in the energy required to bring new data sets and visualizations to the market. Updating each dashboard involved a series of steps, including updates to the data warehouse, application layer, and front-end components. Unfortunately, this process took a considerable amount of time, often stretching into months rather than weeks for a new dashboard to reach the market. Additionally, our custom JavaScript UI started to show signs of aging, appearing somewhat tired and outdated.
Solution
To address these challenges, we selected AWS QuickSight as our advanced Business Intelligence tool. This allowed us to eliminate the need for a dedicated development team to write custom JavaScript code for dashboard updates. Now, updates and changes can be made on the fly instead of taking months to roll out.
Our high-level architecture choice embraces serverless computing and stores data in flat files, departing from the traditional data warehouse approach. Utilizing services such as AWS Glue for ETL, S3 for storage, Step Functions for orchestration, and Lambda for custom SDK API calls, we built a cost-effective and scalable architecture.
Simply put, we extract data from SQL Server using Glue, perform transformations, and write to S3 in Parquet format. Leveraging QuickSight SPICE, we cache the data, ensuring our dashboards perform seamlessly. This modernized approach has significantly enhanced our efficiency and responsiveness in delivering advanced analytics.
Successes
Democratized Dashboard Development:
One major outcome of this project was it empowered our users to have access to the data so that they can build their own dashboards as they wish for their specific needs. This was not possible with our custom JavaScript implementation.
Elevated Data Security Standards:
Using Row-Level-Security (RLS) increases data security by eliminating the need to remove personal information from data. This means users can access the data they need without worrying about exposing personal details. RLS allows for full access to relevant data without compromising privacy.
3x Faster Deploying New Insights:
What would have taken months of time, can now be brought to market within weeks. With speed to market 3x faster, SchoolTool keeps its competitive edge, and team members can apply their skillsets and time to other critical projects.
Database Retirement and Cost Savings Impact:
As a result of these changes, we can retire our old dashboard SQL Server databases, leading to a 10% cost reduction annually across all school districts. This is significant cost savings we're able to reinvest back into more advanced features within the SchoolTool product, including Generative AI.
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