Data Mashup and Dashboards with a Pre-configured AWS Instance

As an option, InetSoft offers a pre-configured AWS instance with data intelligence tools to give you a supercharged data transformation and data mashup platform for interactive dashboarding, visual analytics, and production reporting.

Simply launch the instance and connect to your Amazon RDS, Redshift, MySQL, EMR and other data sources. Then, mashup and transform data on the fly, and build interactive, personalizable AWS dashboards, and visual analyses right inside a single web app.

   Explore all your big and small data without burdening IT or requiring a data warehouse.

   Speed up analytics across disparate sources with intelligent caching.

   Build executive KPI monitoring dashboards and exploratory data visualizations in minutes with a web-based drag-and-drop designer.

   Give business users intuitive, personalized point-and-click access to the data they need to monitor and the ability to answer ad hoc questions on their own.

Garnet Mining Company Switches from Redash to InetSoft for AWS-Based Visual Reporting

The garnet mining industry is not typically associated with advanced data practices. Yet as global markets tighten and operational efficiency becomes critical, even highly specialized extractive industries like garnet mining are turning to sophisticated analytics platforms. One such miner, with operations across multiple sites, faced significant challenges using Redash for dashboards and reporting on its AWS-based data sources. After careful evaluation, the company migrated to InetSoft, a move that reshaped its reporting capabilities and improved decision-making across the organization.

The Garnet Mining Industry’s Data Landscape

Industrial garnet production is used mainly for abrasives in waterjet cutting, sandblasting, and water filtration. Because demand is spread across industrial buyers in construction, manufacturing, and environmental services, garnet miners must balance variable demand against extraction and logistics costs. This requires constant analysis of mine output, quality grading, equipment performance, supply chain bottlenecks, and customer demand forecasts.

The company in focus operated multiple extraction sites, each generating detailed logs on production volume, mineral grades, and maintenance data. It also tracked shipping schedules, fuel usage, and sales orders. All this data was stored in AWS-based sources, including Amazon RDS for transactional data, Amazon Redshift for analytical queries, and Amazon S3 for log files. While the company initially adopted Redash for querying and simple visualization, limitations quickly became apparent as data demands grew.

Limitations of Redash in the Garnet Mining Context

Redash was appealing at first for its simplicity and cost structure. Engineers could write SQL queries directly against AWS databases and quickly build lightweight dashboards. However, the platform was not designed to scale with complex operational needs. The garnet miner encountered several roadblocks:

• SQL Dependency: Every visualization required custom SQL queries. Production managers without SQL skills struggled to generate reports without assistance from the IT team.
• Limited Data Transformation: Redash had minimal capabilities for blending or reshaping data. Complex joins across Redshift, RDS, and S3 logs became unwieldy, forcing IT staff to pre-process data manually.
• Performance Bottlenecks: As queries became more complex, dashboard performance slowed. Waiting for results became common, reducing user adoption.
• Static Visuals: The dashboards lacked interactivity. Managers wanted drill-down capabilities into specific mines, equipment sets, or time periods, but Redash offered only basic charting.
• Scaling Costs: Although open-source at its core, the hosted version introduced costs as more users came online, while self-hosting required heavy IT oversight.

In short, Redash was sufficient for analysts who knew SQL, but it was not accessible or efficient for operational managers, executives, or field staff who needed timely and interactive reporting.

Why InetSoft Was Chosen

The garnet miner’s IT and operations leadership evaluated several alternatives, including AWS QuickSight, Tableau, and Qlik. Ultimately, they selected InetSoft StyleBI for its ability to integrate seamlessly with AWS sources, provide robust data mashup capabilities, and deliver highly interactive dashboards with minimal technical overhead.

InetSoft was particularly appealing because it offered:

• Direct AWS Integration: Built-in connectors to Amazon Redshift, RDS, and S3 enabled a smooth transition without heavy re-engineering.
• Data Mashup Layer: Instead of relying purely on SQL, InetSoft allowed data blending across heterogeneous sources, letting business users combine operational logs with sales forecasts in a drag-and-drop interface.
• Serverless Architecture: Deployed on AWS infrastructure, InetSoft’s serverless deployment minimized infrastructure management and scaled dynamically with usage.
• Interactive Visualizations: Dashboards supported filtering, drill-downs, and custom KPIs, enabling managers to explore operational data without IT intervention.
• Cost Efficiency: Compared to Tableau and Qlik’s licensing models, InetSoft offered lower total cost of ownership, especially given its lightweight deployment model.

Implementation Process on AWS

The migration began with a proof of concept. The IT team set up InetSoft in their AWS environment using an EC2-hosted deployment initially, then later moved to a more serverless configuration. Within weeks, dashboards were connected to Redshift and RDS data, replicating the most-used Redash reports.

Unlike the static SQL-driven Redash dashboards, InetSoft dashboards allowed end-users to slice data dynamically. For example, an operations manager could filter mine output by site, grade, or equipment type without writing queries. This lowered the burden on IT and encouraged adoption across departments.

Integration with S3 was another breakthrough. Previously, production logs stored in S3 buckets required preprocessing before visualization. InetSoft’s data mashup layer enabled direct connection, transformation, and blending with sales data in Redshift, allowing real-time visibility into production-to-sales pipelines.

Operational Benefits Realized

The switch from Redash to InetSoft delivered tangible results within the first six months. Several key improvements stood out:

• Faster Decision-Making: Executives now had dashboards that refreshed automatically with near-real-time data from AWS. Decision cycles shortened from weeks to days.
• Broader Adoption: Non-technical staff could access and manipulate dashboards without SQL knowledge. User adoption tripled compared to the Redash era.
• Reduced IT Burden: The IT team no longer spent hours building and maintaining queries. They could focus on governance and optimization instead of manual report generation.
• Performance Gains: InetSoft’s optimized queries and caching improved dashboard responsiveness, even with complex multi-source data mashups.
• Scalability: With AWS-based deployment, the company scaled access to more field sites without re-architecting the system or ballooning infrastructure costs.

Cost and Resource Comparison

From a financial perspective, the transition also delivered measurable savings:

• Licensing: Redash’s enterprise version was relatively inexpensive but lacked functionality. Alternatives like Tableau would have cost significantly more. InetSoft struck a balance by offering enterprise-grade functionality at a fraction of those costs.
• Infrastructure: By running InetSoft in a serverless configuration on AWS, the company avoided the overhead of managing dedicated servers, reducing operational expenses by an estimated 25%.
• Support and Maintenance: InetSoft’s centralized administration tools reduced IT workload. This freed up 1–2 full-time equivalents previously dedicated to report maintenance, saving both time and salary costs.

End-User Satisfaction and Cultural Shift

Perhaps the most important impact was cultural. With Redash, only a handful of technical analysts felt empowered to explore data. With InetSoft, field supervisors, plant managers, and executives alike began to engage directly with dashboards. The ability to filter production metrics on the fly, compare equipment efficiency across sites, or monitor shipments in near real time created a culture of accountability and transparency.

In staff surveys conducted after six months of using InetSoft, over 80% of respondents said the dashboards made their jobs easier and improved their ability to act quickly. Several managers highlighted the newfound independence from IT staff, describing it as “liberating.”

Strategic Advantages for the Future

The switch was not only about solving today’s problems but also about preparing for the future. As the garnet miner explores predictive analytics for maintenance and demand forecasting, InetSoft’s ability to integrate machine learning outputs from AWS SageMaker positions the company well. Redash could never have handled such advanced requirements without heavy customization.

Moreover, InetSoft’s embedded analytics capabilities mean the miner can eventually extend dashboards outward to logistics partners and key customers, strengthening collaboration across the value chain. This future-proofing ensures the company stays competitive as digital transformation spreads even into niche industries.