Data Engineering

Data
Engineering

At the foundation of AI advancement lie advanced data platform technologies such as knowledge graphs, vector databases, and CAP architectures. These technologies closely interact with LLMs to enhance model performance and increase the utilization of real-time data. In addition, as multiple database technologies coexist, flexibility is required to avoid dependence on a single technology and to select the most appropriate solution based on the specific context.

In the AI era
Data Engineering

At the foundation of AI advancements are cutting-edge data platform technologies such as knowledge graphs, vector databases, and cap architecture. These technologies work closely with LLM to enhance its performance and enhance real-time data utilization.

Moreover, as various database technologies coexist, flexibility is required to select the optimal technology for each situation without being confined to a specific technology.

Challenge

Challenges
in data collection and preprocessing.

Data quality and regulatory
compliance issues.

Difficulty in securing sufficient data quality and volume
Risk of biased outcomes caused by data bias
The need to comply with security and regulatory requirements, including personal data protection
Legal and reputational risks arising from non-compliance with regulations such as GDPR and CCPA

Data format conversion and storage optimization issues.

The need to convert data into formats suitable for model training and to store it efficiently
Requirements to transform unstructured data into structured tensor formats and to convert large-scale tabular data into columnar formats
Complexity in format conversion and schema unification, with risks of data loss or inconsistency
The need to optimize storage architectures to enable high-speed reading of large-scale training datasets
Additional infrastructure investment and tuning are required for optimization, demanding specialized expertise and costs

Complexity of data cleansing and labeling processes.

Significant time and cost required to clean corrupted values, duplicates, and outliers in raw data
Challenges in essential preprocessing steps, such as handling missing values, standardizing data formats, and removing duplicates
Bottlenecks caused by manual labeling by specialized personnel when preparing data for supervised learning models
Data annotation with complex criteria can consume up to 80% of an AI project’s timeline
Model limitations arising from low-quality or biased labels
Difficulty in generating augmented data while preserving the characteristics of the original data

Compute performance
and cost issues.

Massive computational resources are required when preprocessing large-scale datasets
High computational intensity of preprocessing workloads and the limitations of single-server processing
Increased costs due to the adoption of distributed processing engines and parallel computing resources
Difficulty in maintaining a balance between compute performance and cost

Service

Optimization services
DIA NEXUS focuses on.

Data collection
and cleansing strategy

Proposing approaches to clean structured and unstructured data using automated tools by applying data cleansing rules and quality management processes, and to regularly conduct data quality audits to ensure completeness, consistency, and reliability.

Data architecture
design

Proposing approaches to reduce redundant computation and ensure consistency, mitigate I/O bottlenecks during training, and efficiently handle large-scale workloads by leveraging distributed processing and parallelization when needed.

Continuous
optimization.

Continuously enhancing AI system performance and maximizing business value by executing ongoing infrastructure scaling in response to data growth, adopting improved algorithms, and refining pipeline stages.

Data Engineering