Powering Financial Services with Databricks Lakehouse: Focus on Data Modeling, Feature Store Tables, and Robust Data Governance

Introduction

In today’s financial services landscape, ensuring adherence to robust data governance, implementing privacy regulations, and efficiently managing large volumes of data is crucial.

Data has emerged as the cornerstone of strategic decision-making across the Financial industry sector. As organizations harness the potential of data to gain insights, optimize operations, and foster innovation, the significance of effective data management techniques cannot be overstated. Among these techniques, two crucial aspects stand out: data modeling using star schema and the creation of feature store tables. These components play pivotal roles in structuring and utilizing data for analytical and machine-learning purposes, respectively.

In this blog post, we’ll explore how Databricks Lakehouse presents a unified platform that facilitates the construction of powerful data models, formulation of feature store tables, and the implementation of comprehensive data governance.

The Data Revolution: Navigating Complex Terrain

Data, often referred to as the “new oil” of the digital age, has transformed the way businesses operate, governments formulate policies, and individuals make choices. From customer behavior analysis to personalized marketing strategies, the applications of data-driven insights are virtually limitless. However, this transformative potential comes with its own set of challenges.

As data streams in from various sources, the sheer volume and diversity of information can become overwhelming. Without a well-defined structure, data can quickly devolve into an unwieldy mess, rendering valuable insights hidden and decision-making processes convoluted. This is where data modeling steps in.

Star Schema: Illuminating the Path to Insights

Types of Data Models

• Conceptual Data Model: A bird’s-eye view of organizational data, mapping entities, their relationships, and information flow.
• Logical Data Model: Details data organization without venturing into database specifics, emphasizing structure, relationships, constraints, and semantics.
• Physical Data Model: Zeroes in on database specifics, illuminating how data will be stored, including tables, columns, and indexes.
• Star Schema: A favorite for data warehousing, this model centralizes data using a fact table surrounded by dimension tables, resembling a star shape. It’s tailored for quick querying in analytical systems.
• Snowflake Schema: An advancement of the star schema, it normalizes dimension tables to minimize redundancy, albeit at the cost of increased complexity.

In the intricate realm of data analysis, where raw information from disparate sources converges, the star schema stands as a guiding light. This schema, a type of database architecture, offers a structured framework that transforms complex datasets into intelligible patterns and relationships. At its core, the star schema revolves around two essential components: fact tables and dimension tables.

Fact Tables: Measuring the Universe of Data

Fact tables act as the central repositories of quantitative data. These tables capture the measurable events, transactions, or activities that interest the organization. For instance, in banking & payments, a bank account balance. At the end of each day, the balances of every customer account in the bank are stored in this account balance table.

Dimension Tables: Adding Context and Depth

While fact tables hold the quantitative essence, dimension tables bring depth and context to the data. These tables contain descriptive attributes that provide additional information about the data stored in the fact tables. Continuing with the banking & payments dimension tables could include information about the type of transactions like deposits, withdrawals, etc. in the transactions table

Advantages of the Star Schema:

• Simplified Queries: The star schema simplifies querying by reducing the number of joins required, leading to faster response times and improved user experience.
• Enhanced Performance: Since dimension tables are small and can be indexed, queries involving these tables are faster compared to more complex normalized schemas.
• User-Friendly: The star schema’s intuitive design makes it user-friendly, even for non-technical users, enabling easier access to insights.
• Aggregation: Aggregating data for reporting and analysis becomes straightforward due to the clear separation of facts and dimensions.

Feature Store Tables: Empowering Machine Learning with Organized Insights

As data-driven decision-making extends into the realm of machine learning, the role of feature store tables becomes paramount. In the context of machine learning, a “feature” refers to an attribute or property of data that serves as an input to a model. The quality and relevance of features significantly influence the performance of machine learning algorithms.

The Feature Engineering Challenge

Machine learning models thrive on data attributes or features that encapsulate relevant information for prediction and classification tasks. However, preparing these features for model training is often a time-consuming and iterative process. Raw data seldom aligns perfectly with model requirements, necessitating transformations, aggregations, and derivations to create meaningful features. Feature Stores are foundational in the domain of machine learning, acting as intermediaries between raw data and model-ready insights.

Types of Feature Stores

• Offline Feature Stores: Cater to extensive historical data and are designed for training ML models.
• Online Feature Stores: Aimed at real-time predictions in applications.

The Role of Feature Store Tables

A feature store provides a structured repository for curated and preprocessed features that are ready for machine learning model consumption. These tables centralize the management of features, allowing data scientists and analysts to collaboratively build, share, and reuse features across various projects.

Advantages of Feature Store Tables

• Consistency: Feature store tables ensure that the same set of features is used across different models, improving consistency and reducing errors.
• Efficiency: Data scientists can focus on modeling and experimentation rather than recreating features, leading to faster model development. Features are pre-compute, the Model training pipeline runs faster and avoids duplicative work — recreating features — across teams
• Versioning: Feature store tables offer version control, enabling teams to track changes to features over time.
• Scalability: As the volume of features grows, a feature store can efficiently manage and serve these features to multiple models.

The Importance of a Unified Platform

What conflicts arise when we manage both Data Models and Feature Store tables? When do we use one or the other?

In the realm of data management, the challenges of maintaining data models and feature store tables in separate systems have become increasingly apparent. This separation can introduce complexities and inefficiencies that hinder the full potential of data-driven decision-making. Conversely, the advantages of a unified platform that seamlessly integrates both tasks present a compelling case for organizations seeking a streamlined and holistic approach to data management.

Challenges of Separate Systems

Data Discrepancies and Inconsistencies:

When data models and feature store tables are managed independently, discrepancies can arise due to variations in data definitions, transformations, or even source data updates. This can lead to divergent insights and decisions based on contradictory information.

Duplication of Efforts: Managing data models and feature store tables in isolation often results in duplicated efforts. Data teams may need to recreate similar transformations or features separately, consuming valuable time and resources that could be better spent on higher-value tasks.

Complex Integration: Integrating data models with feature store tables from disparate systems can be complex and error-prone. The need for data synchronization between these systems introduces potential points of failure and data loss.

Collaboration Barriers: Separate systems impede collaboration among data teams. Data scientists, analysts, and engineers may work within different environments, inhibiting knowledge-sharing and cross-functional innovation.

Benefits of a Unified Platform

Data Consistency and Accuracy: A unified platform ensures that data models and feature store tables are based on the same underlying data definitions and transformations. This consistency enhances the accuracy and reliability of insights derived from both tasks

Efficiency and Resource Optimization: By consolidating data modeling and feature engineering within a single platform, organizations reduce the duplication of efforts. This results in streamlined workflows and optimized resource allocation.

Seamless Integration: A unified platform facilitates smooth integration between data models and feature store tables. Changes made to data models can be seamlessly reflected in feature engineering and vice versa, ensuring data alignment across tasks.

Enhanced Collaboration: Collaborative synergy among teams is fostered when data scientists, analysts, and engineers operate within the same ecosystem. This promotes efficient knowledge-sharing, accelerates decision-making, and sparks innovation

Additionally, a unified platform provides:

1. Unified Governance and Security: Managing data in a unified platform allows for the consistent application of governance and security measures across data models and features. This is particularly crucial in industries with stringent regulatory requirements.
2. Agile Adaptation: A unified platform allows organizations to adapt to changes more swiftly in rapidly evolving environments. Modifications in data models or feature engineering can be rapidly implemented without the challenges of integrating separate systems.

Databricks Lakehouse Architecture: Empowering Financial Services with Unified Data Management — A Comprehensive Scenario

Consider a global investment firm navigating the complexities of international markets. The firm needs to create sophisticated data models that predict market trends and optimize portfolio management. Simultaneously, it aims to standardize feature engineering for machine learning models, collaborating with regulatory bodies for compliance checks. Databricks Lakehouse addresses these requirements seamlessly:

• Delta Lake: Ensures that data changes across both data models and feature store tables are accurately recorded and propagated, maintaining consistency.
• Photon: Accelerates real-time queries, providing instantaneous insights into market trends and portfolio performance.
• Feature store: Empowers data scientists to create intricate data models that consider a multitude of variables, leading to informed investment decisions.
• Unity Catalog: Ensures that metadata is accurately managed, adhering to regulatory standards and providing transparent data lineage.
• Delta Sharing: Enables collaboration with regulatory bodies, sharing feature store data for compliance assessments while maintaining data security.
• Machine Learning: Streamline the process of creating predictive and forecasting models to predict market trends and optimize portfolio management.
• Gen AI: Provides human-guided fine-tuning of models, harnessing the collective expertise of financial professionals and data scientists.
• Vector Database: Supports lightning-fast queries, enabling real-time execution of trading decisions based on up-to-the-moment insights.

In the financial services realm, where data accuracy, regulatory compliance, and informed decisions define success, the integrated capabilities of Databricks Lakehouse empower organizations to seamlessly manage data models and feature store tables, paving the way for unmatched agility, precision, and innovation.

Databricks announced AI for Governance and Governance for AI during Data AI Summit 2023! This emulates the aspect that AI and Governance need to co-exist together, especially in the financial services realm, where data accuracy, regulatory compliance, and informed decisions define success. The integrated capabilities of Databricks Lakehouse empower organizations to seamlessly manage data models and feature store tables, paving the way for unmatched agility, precision, and innovation.

Conclusion

Data Models and feature store tables, while serving different purposes, share a common goal: transforming raw data into actionable insights

The star schema (data model) provides a structural framework that simplifies data analysis, enabling organizations to uncover patterns and relationships that drive informed decision-making. On the other hand, feature store tables expedite the feature engineering process for machine learning, propelling model development and accuracy.

By embracing the power of data modeling techniques and feature store tables, organizations can unleash the full potential of their data, positioning themselves at the forefront of innovation and competitiveness.

The powerful features of Databricks Lakehouse — Unity Catalog, DLT, Delta Lake, MLflow, and Spark improve efficiency by automating and streamlining data modeling and feature store table creation processes. It fosters collaboration by providing a shared, consistent view of data to all users, enhancing performance by ensuring data freshness and high-quality features, and accelerating speed by enabling real-time data ingestion and transformation.

The Databricks Lakehouse offers a unified, efficient, and reliable platform for constructing data models and building feature store tables for the financial services industry. It centralizes these processes, ensuring robust data governance and privacy standards. In doing so, it aids in the development of accurate predictive models, enhancing risk assessment and informed decision-making. The data-driven era in financial services is increasingly dependent on platforms like Databricks Lakehouse.

Thank you Franco Patano for your collaboration in brainstorming the content for this blog post.