What is HDC ?
InTouch HDC (InTouch Histdata Converter) is a specialized tool designed to accurately convert massive InTouch Histdata raw files into structured formats and import them directly into SQL Server databases. It eliminates the need for complex programming and manual data preparation, providing the essential data for secondary development simply and efficiently.
Operating entirely independently of AVEVA InTouch, the converter can run in the background without interrupting your SCADA operations. It not only simplifies the workflow but also ensures data integrity, maintaining consistency and completeness across all your datasets to establish a solid foundation for advanced analytical applications.
Features
Multi-Plan Management
Supports running multiple plans simultaneously and automatically generates data tables to handle different sources, time ranges, and data densities.
No System Burden
Performs conversions in the background without affecting on-site operations, and can write exported data to remote SQL Server or PostgreSQL to reduce local host load.
User-Friendly UI
Features an intuitive interface to easily select target databases, data density, and specific points to convert without extensive scripting.
Seamless Compatibility
Works flawlessly with both single and multiple InTouch environments, including mixed versions.
How HDC works
Here is how InTouch HDC streamlines the data-converting process for you :
Configure via UI
Use the intuitive user interface to quickly select the specific data points you want to convert, the target database, and the required data density.
Set Up Plans
Check the LogData to initiate the export process and easily configure scheduled background executions for continuous operation.
Automated Conversion
The system accurately converts the raw InTouch Histdata files into structured formats while automatically generating the necessary data tables.
Direct Database Import
The structured data is seamlessly imported into your SQL Server or PostgreSQL databases.
Benefits
Massive Data Conversion
Ensures continuous and reliable conversion regardless of diverse sources, different time ranges, or massive datasets.
Easy Setup
Enables fully automated conversion of historical data through a simple UI, eliminating manual processes and complex scripting.
Multi-Tasking Capability
Allows multiple plans to run simultaneously across single or multiple InTouch environments and mixed versions.
Strengthen the Data Foundation
Builds a solid and reliable data infrastructure for advanced applications like Big Data Analytics, Predictive Maintenance, and Business Intelligence.
Uninterrupted Operations
Runs entirely in the background, ensuring that your on-site SCADA operations and local hosts remain completely unaffected.
System Requirements
To ensure optimal performance and system stability, we recommend that your server environment meets the following specifications :
Server Specifications
| CPU | RAM | Storage | Network | Operating System | Database |
|---|---|---|---|---|---|
|
Quad-core or higher, Intel or AMD x86 processor, 2.6 GHz or faster. Recommended: 3rd Gen Intel® Xeon® Scalable (Ice Lake / Cooper Lake) or 10th Gen Intel® Core™ (Comet Lake) or later. |
16GB DDR4 or higher | 1TB SSD or larger | 1Gbps Wired Ethernet Adapter | Microsoft Windows Server 2022 Standard x64 or later | Microsoft SQL Server 2022 Standard x64 or later |
※ Note: Actual hardware requirements may vary depending on the data volume, system load, and the number of concurrent users.
Customer story
Seamlessly Bridging Historical Data and Business Intelligence: Automating Massive Data Conversion with InTouch HDC.
The customer is a large manufacturing plant that has long relied on the AVEVA InTouch SCADA system, accumulating vast amounts of historical data. To move toward smart manufacturing, the company planned to deploy applications such as Big Data analytics, process optimization, predictive maintenance, and business intelligence.
However, the historical data was scattered across different environments and mixed versions. There was an urgent need to accurately convert raw InTouch Histdata files into structured formats directly readable by relational databases like SQL Server, providing a solid data foundation for further development and analysis.
During the data conversion process, the engineering team faced several severe challenges :
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Heavy reliance on complex programming and manual labor
Traditional conversion required extensive manual processes, such as scripting and manual data preparation.
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Difficulties in handling massive data
It was hard to maintain data consistency and completeness when dealing with diverse sources, different time ranges, and massive datasets.
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Potential interruption to on-site operations
Directly processing large-scale data conversions locally could increase the load on local hosts and interrupt ongoing SCADA operations.
To overcome these data conversion bottlenecks, the plant implemented InTouch HDC to automate the data export process effortlessly :
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Zero-barrier automated conversion
Through a user-friendly UI, engineers easily selected the specific points to convert, the target database, and data density, completely eliminating complex programming and manual work.
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Simultaneous multi-plan management
The system supported running multiple plans simultaneously and automatically generated data tables, easily handling different sources, time ranges, and mixed InTouch versions.
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Background execution and remote import
InTouch HDC operated independently in the background; the exported structured data was written directly to remote SQL Server or PostgreSQL databases.
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Strengthened the data foundation
Successfully and reliably converted massive historical data into SQL databases, providing instant access for advanced applications like Big Data Analytics and Predictive Maintenance.
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No system burden
Background conversions and remote database writing effectively reduced the load on local hosts, ensuring that on-site SCADA operations remained completely uninterrupted.
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Ensured data integrity and completeness
Completely eliminated repetitive manual data conversion and cleanup, ensuring data consistency across all datasets, which allowed the team to focus on high-value analysis and decision-making.
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