Web Based Query Builder

Kelp Processing and Extraction Firm Switching from DbVisualizer to StyleBI for Web-Based Query Building

A mid-sized kelp processing and extraction company, specializing in producing nutraceuticals, cosmetics, and biofertilizers, faced growing challenges with their data analytics infrastructure. For years, the firm relied on DbVisualizer for querying relational databases and generating reports. While DbVisualizer offered a robust desktop-based environment for SQL query writing and database exploration, its limitations became increasingly apparent as the company’s operational and analytical needs expanded. The management team recognized that the organization needed a more flexible, web-based solution that could empower both technical and non-technical users while scaling to meet future data requirements. After an extensive evaluation, the firm selected InetSoft’s open source StyleBI for its web-based query builder and self-service analytics capabilities.

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Challenges with DbVisualizer

DbVisualizer had served the company well in its early stages, providing a reliable interface to query SQL databases, perform joins, and generate simple reports. However, the solution was primarily desktop-based, which created several challenges for a growing company:

• Limited accessibility: Analysts had to install DbVisualizer locally, making it difficult for remote staff or cross-department teams to access the platform without VPNs or desktop clients.
• Steep learning curve for non-technical users: While SQL experts thrived with DbVisualizer, business users and operations managers often struggled to write queries or manipulate data independently.
• Fragmented reporting: Each analyst maintained local query files and exported results to spreadsheets, creating inconsistencies and version control issues.
• Integration limitations: DbVisualizer was designed for database exploration, not for embedding analytics into internal web portals or dashboards. Sharing insights across departments required manual exports and presentations.
• Scalability constraints: The growing volume of kelp harvest data, extraction process metrics, and production logs demanded faster query performance and centralized access to datasets, which DbVisualizer could not fully provide.

These challenges prompted the firm to explore alternatives that would allow broader adoption, more agile reporting, and a more collaborative analytics environment. After considering several business intelligence tools, the company identified StyleBI as a suitable replacement, particularly because of its web-based query builder and open-source model.

Why StyleBI Was Selected

StyleBI offers a combination of features that align closely with the company’s evolving analytics requirements. Several factors influenced the decision:

• Web-based accessibility: Unlike DbVisualizer, StyleBI runs entirely in a web browser, providing universal access to users without requiring local installations. Analysts, production managers, and supply chain personnel can access the query builder from any device, facilitating remote work and cross-functional collaboration.
• Self-service query building: The intuitive drag-and-drop interface enables non-technical users to create complex queries and reports without SQL expertise. This empowers business teams to explore kelp harvest data, extraction yields, and production metrics independently.
• Data mashup capability: StyleBI supports combining multiple data sources in real-time. For the kelp processing firm, this means analysts can integrate harvest logs, lab test results, inventory records, and sales data into unified queries and dashboards without relying solely on IT.
• Open-source model: The company values transparency and customization. StyleBI’s open-source framework allows internal IT teams to modify features, integrate custom connectors, and extend functionality according to evolving business requirements.
• Scalability and performance:StyleBI’s architecture can handle large datasets efficiently. As the kelp processing firm collects more operational and extraction data, the platform can scale horizontally to support additional users and higher query loads.

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Implementation Process

The transition from DbVisualizer to StyleBI was approached methodically to minimize disruption. The implementation followed several key phases:

1. Data Inventory and Assessment

The first step involved a comprehensive inventory of all data sources currently accessed through DbVisualizer. This included SQL databases containing kelp harvest records, lab analysis results, supply chain logs, production metrics, and sales information. Each dataset was assessed for quality, consistency, and integration requirements. The IT team identified opportunities to streamline schemas and consolidate redundant tables to optimize queries in StyleBI.

2. Platform Installation and Configuration

StyleBI was deployed on the firm’s internal cloud infrastructure, providing secure access to authorized personnel. The IT team configured database connections, set up user authentication, and established role-based access control to ensure sensitive operational and financial data remained protected. Custom templates and reusable query components were defined to speed up future report creation and promote consistency across departments.

3. Training and Knowledge Transfer

Transitioning to StyleBI required educating both technical and non-technical users. IT conducted training sessions for SQL analysts, showing how to leverage the query builder to replicate existing DbVisualizer queries. Business users attended workshops focused on drag-and-drop query construction, filtering, aggregation, and visualization. Hands-on exercises helped users build confidence in generating reports independently, reducing reliance on IT.

“Flexible product with great training and support. The product has been very useful for quickly creating dashboards and data views. Support and training has always been available to us and quick to respond.

- George R, Information Technology Specialist at Sonepar USA

4. Data Migration and Validation

Existing reports and queries from DbVisualizer were migrated to StyleBI. Analysts verified the accuracy of outputs against historical results to ensure consistency. The validation process included checking joins, calculations, filters, and aggregates, with adjustments made to accommodate StyleBI’s data block mashup logic.

5. Dashboard Creation and Collaboration

With queries in place, the firm leveraged StyleBI’s dashboarding capabilities to create real-time operational dashboards for production, extraction efficiency, and supply chain monitoring. These dashboards were shared with department heads, enabling faster decision-making and more proactive operational management. StyleBI’s web-based environment facilitated collaborative review, allowing managers and analysts to annotate dashboards, suggest improvements, and track changes over time.

Benefits Realized

The transition to StyleBI delivered immediate and long-term benefits for the kelp processing firm:

• Increased productivity: Business users could generate reports independently, reducing the workload on SQL analysts and IT personnel.
• Faster decision-making: Real-time dashboards and query results enabled managers to monitor extraction efficiency, inventory levels, and sales trends without delays.
• Enhanced collaboration: Web-based access allowed teams across harvest, lab, production, and sales departments to collaborate effectively and share insights in a single platform.
• Cost savings: By leveraging an open-source solution, the company avoided recurring per-user licensing fees associated with commercial BI platforms.
• Scalable architecture: The platform could handle increasing data volumes from expanding harvest operations and new product lines without significant performance degradation.
• Long-term flexibility: The open-source framework enabled custom integration with emerging IoT sensors, laboratory automation systems, and ERP modules, future-proofing the analytics infrastructure.