Evaluate InetSoft's Data Integration Software
InetSoft offers Web-based BI software that includes flexible data mashup capabilities that can be your data integration solution without the need for a data warehouse.
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Why InetSoft’s StyleBI?
StyleBI stands out as a comprehensive data integration software platform that not only consolidates data from disparate sources but also empowers organizations to gain actionable insights in real time. With a focus on agility, scalability, and ease of use, StyleBI is fast becoming the backbone of digital transformation strategies across industries.
Powerful Data Mashup Capabilities
One of the core strengths of StyleBI lies in its robust data mashup engine. Traditional data integration often involves rigid data pipelines and expensive ETL processes. StyleBI disrupts this model by enabling users to integrate structured and semi-structured data on the fly. Whether you're pulling from SQL databases, flat files, web APIs, or cloud applications, StyleBI lets you create blended datasets without requiring a dedicated data warehouse.
This flexibility is particularly valuable for organizations that maintain hybrid environments or need to bring together legacy ERP data with modern SaaS analytics. Business users and analysts can use drag-and-drop interfaces to link data sources, apply joins, filters, and transformations—all without writing complex code.
Real-Time Data Access
Time-sensitive decisions require timely information. StyleBI offers live data connectivity to a wide range of systems including Oracle, MySQL, SQL Server, Salesforce, SAP, MongoDB, and many more. Rather than relying on batch data refreshes, users can work with real-time dashboards that reflect the most current data available.
Whether you're monitoring inventory levels in a manufacturing plant or tracking e-commerce KPIs, StyleBI ensures that every chart, KPI card, and report tile reflects what’s happening now. This real-time access is invaluable for detecting anomalies, responding to operational disruptions, or seizing market opportunities as they arise.
Intuitive Data Modeling and Transformation
Data integration is more than just connecting sources—it’s about reshaping that data for meaningful analysis. StyleBI’s built-in Data Worksheet environment acts like a modeling sandbox where users can create calculated fields, apply business logic, and define metrics. IT professionals and data analysts alike can define reusable data blocks that serve as the foundation for enterprise dashboards.
Advanced users benefit from support for expression scripting, enabling custom functions and KPIs. For example, calculating customer churn rates, creating normalized performance indexes, or building dynamic product scoring models can all be done directly within StyleBI.
Unified Platform for Integration and Visualization
Unlike platforms that separate ETL, data warehousing, and BI into siloed products, StyleBI is a unified platform. This design streamlines the entire analytics lifecycle from data preparation to interactive dashboard creation. Users don’t need to switch between applications or manage fragile integrations—everything happens within the same interface.
This cohesion not only reduces complexity but also enhances collaboration. Business users can explore the same curated data models built by IT teams, ensuring consistency in reporting while empowering self-service exploration.
Data Security and Governance
StyleBI supports enterprise-grade data governance features. Integration with LDAP and Active Directory ensures that users see only the data they are authorized to access. Row-level security, user role management, and object-level permissions safeguard sensitive data and enforce compliance standards like GDPR and HIPAA.
Audit trails and version control also help data stewards track changes to data models, maintain data lineage, and ensure reproducibility in analytics reporting.
Scalability for Growing Enterprises
InetSoft’s microservices-based architecture ensures that StyleBI can scale horizontally as data volumes and user concurrency increase. Whether deployed on-premises or in the cloud, the platform handles high throughput scenarios with ease. Load balancing, caching, and resource isolation make it suitable for both small teams and global enterprises.
As companies grow or expand their analytics programs across departments, StyleBI’s modular architecture ensures that performance and user experience remain consistent.
Time-to-Insight and ROI
One of the most compelling reasons to choose StyleBI is the accelerated time-to-insight. Because of its visual data modeling tools and pre-built dashboard components, organizations can go from raw data to executive dashboards in days—not months. This speed translates into faster decision-making, better risk mitigation, and ultimately, greater business agility.
Organizations that deploy StyleBI often report ROI in the form of hours saved in manual reporting, improved forecasting accuracy, reduced errors, and better alignment across departments. For IT departments, the ability to deliver analytics rapidly without heavy coding or new infrastructure investments is a game-changer.
Learn the advantages of InetSoft's small footprint BI platform.
More Articles About Data Integration Software
Query Condition
Expressions - The query condition expression grammar is based on the SQL conditions. Most of the
expressions have a construction identical to their SQL counterparts. Like SQL, all reserved words are
case-insensitive. However, all names, including variables and child nodes, are case-sensitive. We will cover
the complete list of conditional expressions and give a few examples of advanced usage. In SQL’s
conditions, the column values can be used in expressions for calculation or comparison. Similarly, the Data
Modeler condition expressions can reference values on the data tree. However, since the hierarchical data
model used by the Data Modeler supports much richer organization of data, the expressions support a few more
types of data references. The selection node and all of its child nodes can be referenced by the conditional
expression. The value of the selection node can be referenced using the reserved word, ‘this’...
Query Parameter Handling -
Report queries may require parameters. Parameters are defined in queries to retrieve user input at runtime. In
addition to the parameters defined in the queries, data sources used by queries may also require parameters to
establish connections to the source. For example, an RDBMS data source may require a user ID and password to
login to a database. Query parameters are handled in the same way as replet parameters. A list of all
variables used in queries in a template is returned as...
Powerful Data Mashup Capabilities
One of the core strengths of StyleBI lies in its robust data mashup engine. Traditional data integration often involves rigid data pipelines and expensive ETL processes. StyleBI disrupts this model by enabling users to integrate structured and semi-structured data on the fly. Whether you're pulling from SQL databases, flat files, web APIs, or cloud applications, StyleBI lets you create blended datasets without requiring a dedicated data warehouse.
This flexibility is particularly valuable for organizations that maintain hybrid environments or need to bring together legacy ERP data with modern SaaS analytics. Business users and analysts can use drag-and-drop interfaces to link data sources, apply joins, filters, and transformations—all without writing complex code.
Real-Time Data Access
Time-sensitive decisions require timely information. StyleBI offers live data connectivity to a wide range of systems including Oracle, MySQL, SQL Server, Salesforce, SAP, MongoDB, and many more. Rather than relying on batch data refreshes, users can work with real-time dashboards that reflect the most current data available.
Whether you're monitoring inventory levels in a manufacturing plant or tracking e-commerce KPIs, StyleBI ensures that every chart, KPI card, and report tile reflects what’s happening now. This real-time access is invaluable for detecting anomalies, responding to operational disruptions, or seizing market opportunities as they arise.
Intuitive Data Modeling and Transformation
Data integration is more than just connecting sources—it’s about reshaping that data for meaningful analysis. StyleBI’s built-in Data Worksheet environment acts like a modeling sandbox where users can create calculated fields, apply business logic, and define metrics. IT professionals and data analysts alike can define reusable data blocks that serve as the foundation for enterprise dashboards.
Advanced users benefit from support for expression scripting, enabling custom functions and KPIs. For example, calculating customer churn rates, creating normalized performance indexes, or building dynamic product scoring models can all be done directly within StyleBI.
Unified Platform for Integration and Visualization
Unlike platforms that separate ETL, data warehousing, and BI into siloed products, StyleBI is a unified platform. This design streamlines the entire analytics lifecycle from data preparation to interactive dashboard creation. Users don’t need to switch between applications or manage fragile integrations—everything happens within the same interface.
This cohesion not only reduces complexity but also enhances collaboration. Business users can explore the same curated data models built by IT teams, ensuring consistency in reporting while empowering self-service exploration.
Data Security and Governance
StyleBI supports enterprise-grade data governance features. Integration with LDAP and Active Directory ensures that users see only the data they are authorized to access. Row-level security, user role management, and object-level permissions safeguard sensitive data and enforce compliance standards like GDPR and HIPAA.
Audit trails and version control also help data stewards track changes to data models, maintain data lineage, and ensure reproducibility in analytics reporting.
Scalability for Growing Enterprises
InetSoft’s microservices-based architecture ensures that StyleBI can scale horizontally as data volumes and user concurrency increase. Whether deployed on-premises or in the cloud, the platform handles high throughput scenarios with ease. Load balancing, caching, and resource isolation make it suitable for both small teams and global enterprises.
As companies grow or expand their analytics programs across departments, StyleBI’s modular architecture ensures that performance and user experience remain consistent.
Time-to-Insight and ROI
One of the most compelling reasons to choose StyleBI is the accelerated time-to-insight. Because of its visual data modeling tools and pre-built dashboard components, organizations can go from raw data to executive dashboards in days—not months. This speed translates into faster decision-making, better risk mitigation, and ultimately, greater business agility.
Organizations that deploy StyleBI often report ROI in the form of hours saved in manual reporting, improved forecasting accuracy, reduced errors, and better alignment across departments. For IT departments, the ability to deliver analytics rapidly without heavy coding or new infrastructure investments is a game-changer.
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Learn the advantages of InetSoft's small footprint BI platform. |
More Articles About Data Integration Software
Query Condition Expressions - The query condition expression grammar is based on the SQL conditions. Most of the expressions have a construction identical to their SQL counterparts. Like SQL, all reserved words are case-insensitive. However, all names, including variables and child nodes, are case-sensitive. We will cover the complete list of conditional expressions and give a few examples of advanced usage. In SQL’s conditions, the column values can be used in expressions for calculation or comparison. Similarly, the Data Modeler condition expressions can reference values on the data tree. However, since the hierarchical data model used by the Data Modeler supports much richer organization of data, the expressions support a few more types of data references. The selection node and all of its child nodes can be referenced by the conditional expression. The value of the selection node can be referenced using the reserved word, ‘this’...
Query Parameter Handling - Report queries may require parameters. Parameters are defined in queries to retrieve user input at runtime. In addition to the parameters defined in the queries, data sources used by queries may also require parameters to establish connections to the source. For example, an RDBMS data source may require a user ID and password to login to a database. Query parameters are handled in the same way as replet parameters. A list of all variables used in queries in a template is returned as...
Query Performance Considerations - For performance reasons, you should avoid using too many large queries in one report. In addition, you should only run a query from script in cases where you cannot directly bind the query. In general, it is more efficient to use the 'Data Binding' dialog box to bind the query to an element, which allows the query to automatically run as part of report generation. There are two complimentary methods for controlling and improving the performance of a report. • Size limit: You can limit the number of rows that the query returns. • Time limit: You can limit the amount of time a query may execute. For example, if you only use the first few rows of a query, you should set a tight limit on the query size. You can make these settings at the query level or data binding level. See Advanced Toolbar Buttons in the Data Modeler to limit at query level, and Precautions and Safeguards in the Report Designer to limit at binding level...
Query Traps - A Query Trap refers to a construct in a data model that can generate undesired query results. This can confuse end users or even give incorrect results. Loops refer to the multiple possible paths from entity A to B in a data model. Loops can exist in different forms: a self join or multiple join path. The Data Modeler in InetSoft's business intelligence software automatically highlights these loops in red. An employee table containing a “manager id” column that points to itself to indicate an employee’s manager is a self join loop. This self join must be resolved to allow end users to find employees and their respective managers. Self join loops are most easily resolved by creating a table alias. The multiple table loop has the potential to create results that are too restrictive. An order table (A) joins to the “ship to” table (B) and the “bill to” table (C), which in turn joins to a “company” table (D). If end users select data from all four tables, only orders that have the same company for “bill to” and “ship to” will be returned. Multi-table loops can be addressed using aliasing or auto-aliasing. Additionally, these loops can be addressed using a weak join which will not be included in the join path of the resultant query...
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