This month, our company released the new Trinity version 3.7, featuring added support for Big Data.
As business services increasingly migrate to the web and mobile platforms, the accumulation of unstructured data is no longer limited to internet-based companies. Enterprises across all industries are rapidly generating vast amounts of unstructured data. Traditional database architectures are no longer sufficient to handle the scale and complexity of storing and analyzing these datasets. As a result, the Big Data wave is sweeping across the business intelligence sector, making it a prominent topic of focus.
In response to this trend, our Head of R&D, Mr. Chen-Tse Hsieh, noted:
“Big Data is not merely about the volume of data—it is fundamentally defined by three characteristics, commonly referred to as the three Vs:
In response to this trend, our Head of R&D, Mr. Chen-Tse Hsieh, noted:
“Big Data is not merely about the volume of data—it is fundamentally defined by three characteristics, commonly referred to as the three Vs:
- Volume – Data sizes often reach into the terabytes and beyond.
- Velocity – From batch to real-time and streaming data. For example, online ad systems must determine responses within 40 milliseconds, while credit scoring systems need to process ratings in under 1 millisecond.
- Variety – Data comes in structured, semi-structured, and unstructured formats, often in complex combinations.
Our Trinity platform, positioned as the ideal solution for data management, now embraces these challenges in its latest release—Version 3.7—by introducing robust Big Data integration and processing capabilities. This update empowers enterprises to meet growing demands around data diversity, volume, and processing performance, and helps them unlock the true value of Big Data.”
Currently, Hadoop stands as the most mature Big Data technology. However, it presents certain technical complexities in both application development and system maintenance. Our CTO, Mr. Cheng-Yu Wu, explained:
“While Hadoop’s greatest strength lies in its parallel processing capabilities, data must first be loaded into the Hadoop environment for any computation to occur. The real challenge lies in enabling seamless data exchange and processing between traditional analytical databases and the Hadoop ecosystem.
Through Trinity’s modular architecture, we developed the Big Data Adaptor, which bridges Hadoop’s Big Data processing power with traditional ETL workflows. Users can now integrate Hadoop-based applications directly into their existing ETL systems—without needing advanced Hadoop expertise. This approach protects prior system investments while enabling organizations to harness Hadoop’s powerful processing capabilities.”
In addition to serving as a data exchange bridge between ETL and Hadoop, the Trinity Hadoop Data Adaptor module also provides the following key features:
Currently, Hadoop stands as the most mature Big Data technology. However, it presents certain technical complexities in both application development and system maintenance. Our CTO, Mr. Cheng-Yu Wu, explained:
“While Hadoop’s greatest strength lies in its parallel processing capabilities, data must first be loaded into the Hadoop environment for any computation to occur. The real challenge lies in enabling seamless data exchange and processing between traditional analytical databases and the Hadoop ecosystem.
Through Trinity’s modular architecture, we developed the Big Data Adaptor, which bridges Hadoop’s Big Data processing power with traditional ETL workflows. Users can now integrate Hadoop-based applications directly into their existing ETL systems—without needing advanced Hadoop expertise. This approach protects prior system investments while enabling organizations to harness Hadoop’s powerful processing capabilities.”
In addition to serving as a data exchange bridge between ETL and Hadoop, the Trinity Hadoop Data Adaptor module also provides the following key features:
- Simplified HDFS Data Access
Offers dedicated HDFS Reader and Writer components, enabling direct read/write operations on Hadoop Distributed File System (HDFS).
- HBase Database Integration
Includes HBase Reader and Writer components to facilitate direct access to HBase databases.
- Hive and Pig Execution Support
Supports the execution of Hive and Pig queries through specialized components, allowing users to leverage these Hadoop-native languages within the Trinity environment.