Exploring Big Data's Potential: Insights from Graph Learning Realms

In today's digital age, data has become a valuable resource for companies working with Big Data. However, the underutilization of data in large companies is a common challenge, primarily due to fragmented data views, broken integrations, lack of trust and accessibility, and strategic/operational misalignment.

The Challenges of Data Underutilization

Key reasons for this underutilization include fragmented customer/operational data views and broken integrations, data silos across departments, lack of trust and accessibility, high costs and complexity in training and maintaining AI or advanced models, and governance and ownership ambiguities. These challenges prevent unified, actionable insights in real-time campaigns, making data platforms underperform.

Automating Data Documentation and Improving Utilization

To automate documentation of new data and improve utilization, companies can implement automated metadata capture tools and data cataloging systems, establish a single, trusted environment or data platform, embed unified data into daily operational workflows, use AI-powered tools to continuously profile, document, and monitor data changes and quality, and develop and enforce clear ownership, governance, and access policies.

The Role of Graph Learning

In a recent internship, a Big Data/Graph Learning solution was implemented to document data. The goal was to create a graph to structure data and predict business data based on features. After obtaining the data, the graph was created, considering a batch of 2000 rows, with business data in the center and physical data off-centered.

The GraphSAGE architecture was employed for this task, a powerful tool for various applications in graph-based machine learning. GraphSAGE employs aggregation functions to combine neighboring nodes' embeddings with specific weights, allowing the architecture to generate embeddings for unseen nodes using their features and neighborhood.

For the multi-class classification task, the cross-entropy loss function and L2 regularization with a strength of 0.0005 were chosen. This regularization technique helps prevent overfitting and promotes model generalization.

The Importance of Data Acquisition

Data Acquisition is one of the outlined topics, focusing on sourcing essential data for graph creation. Physical data characteristics (domain, name, data type), lineage (the relationships between physical data), and a mapping of physical data related to business data are needed for data acquisition.

Within HDFS (Hadoop Distributed File System), structured data can be found, and Hive columns are referenced. The mapping is where we associate our business data with our physical data, providing the algorithm with verified information to classify new incoming data.

Streamlining the Process

To streamline the process further, an Artifactory was used to store the machine learning code securely and organize it for easy access by team members. Accessing data takes a long time due to necessary rights verification and content checks, but with these measures in place, the process becomes more efficient.

The Future of Big Data

T-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique used for visualizing and exploring high-dimensional data by preserving the pairwise similarities between data points in a lower-dimensional space. Feature Hasher is another technique used in machine learning to convert high-dimensional categorical data into a lower-dimensional numerical representation.

These tools, along with GraphSAGE, Airflow for managing and scheduling complex data workflows, Mirantis for providing a robust, scalable, and reliable cloud solution for the project's infrastructure, and Jenkins for automating the building, testing, and deployment of the project, are key technologies used in the development and deployment of Big Data solutions.

By addressing the challenges of data underutilization and implementing these technologies, large companies can transform their vast, often fragmented data into actionable, trusted assets, enabling better real-time use and monetization.