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From Silos to Insight: The Databricks Lakehouse Playbook for Financial Services

Your financial institution is sitting on a goldmine of data, but it's trapped. Scattered across disconnected legacy systems—core banking, trading platforms, CRMs—it's a nightmare to aggregate. The current process is a slow, manual, error-prone marathon of data wrangling, leading to delayed regulatory reports and a lack of trust in your own numbers. You're fighting data silos instead of leveraging your most valuable asset.

This playbook details the construction of a best-in-class, cloud-native lakehouse on the Databricks platform. We solve the data aggregation problem by creating a single, governed source of truth. The architecture uses AWS Glue for powerful, scalable ingestion from your complex legacy sources, landing the data in a cost-effective cloud data lake. From there, Databricks provides the unified analytics engine and orchestration, while dbt enforces rigor and reliability on your data transformations. The result is a high-performance, future-proof data foundation that turns fragmented data into a strategic asset for regulatory compliance and advanced analytics.

Expected Outcomes

  • Establish a single, reliable source of truth for all financial and operational data.
  • Automate data pipelines to dramatically reduce the time and manual effort for regulatory reporting.
  • Increase data accuracy and trust with version-controlled, tested transformations via dbt.
  • Build a scalable foundation that can grow with your data volumes and support future AI/ML initiatives.
  • Empower business users with direct access to clean, consistent, and up-to-date data for analysis.

Core Tools in This Stack

Databricks

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Databricks is a unified Data Intelligence Platform that combines data warehousing, data engineering, and data science on a single, open platform. It allows organizations to manage all their data, analytics, and AI workloads, leveraging a lakehouse architecture built on open standards to accelerate innovation.

Key Features
  • Unified Data Governance (Unity Catalog)
  • Databricks SQL
  • Data Engineering & ETL
  • Data Science & Machine Learning
  • Generative AI & LLMs
  • Delta Lake
  • Collaborative Notebooks
Ideal For

Company Size: Medium, Large

Industries: Technology & Software, Business & Professional Services, Retail & E-commerce, Health & Wellness

Pricing

Model: Pay-as-you-go, Subscription

Tier: Enterprise

Ease of Use

Medium

AWS Glue

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AWS Glue is a serverless data integration service that makes it easy to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and application development.

Key Features
  • AWS Glue Data Catalog
  • AWS Glue Studio
  • Serverless ETL Jobs
  • AWS Glue Crawlers
  • AWS Glue Data Quality
  • AWS Glue DataBrew
  • Broad Data Source Integration
Ideal For

Company Size: Micro, Small, Medium, Large

Industries: Technology & Software, Business & Professional Services, Retail & E-commerce, Health & Wellness

Pricing

Model: Pay-as-you-go, Free Tier, Consumption-based

Tier: Variable

Ease of Use

Medium

dbt (data build tool)

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dbt is a transformation workflow that enables analytics engineers to transform, test, and document data in their cloud data warehouse by writing SQL or Python select statements.

Key Features
  • SQL and Python-based Transformations
  • Automated Data Testing
  • Automatic Documentation Generation
  • Data Lineage Graph Visualization
  • Version Control & CI/CD Integration
  • Incremental Model Building
  • Package Manager for Reusable Code
  • Cloud-based IDE and Scheduler (dbt Cloud)
Ideal For

Company Size: Small, Medium, Large

Industries: Technology & Software, Business & Professional Services, Retail & E-commerce, Creative & Media, Health & Wellness, Other

Pricing

Model: Freemium, Per-Seat, Enterprise/Custom

Tier: Free

Ease of Use

Moderate

The Workflow

graph TD subgraph "Cloud-Native Lakehouse with Snowflake" direction LR N0["Databricks"] N1["AWS Glue"] N2["dbt (data build tool)"] N1 -- "Loads raw Parquet files via S3" --> N0 N2 -- "Connects to SQL Warehouse to run transformations" --> N0 N0 -- "Orchestrates and triggers extraction job" --> N1 end classDef blue fill:#3498db,stroke:#2980b9,stroke-width:2px,color:#fff; classDef green fill:#2ecc71,stroke:#27ae60,stroke-width:2px,color:#fff; classDef orange fill:#f39c12,stroke:#d35400,stroke-width:2px,color:#fff; class N0 blue; class N1 blue; class N2 blue;

Integration Logic

  • FinLegacy Connectors

    This integration follows an Extract-Load-Transform (ELT) pattern. 1) AWS Glue connects to the legacy financial data source (e.g., a DB2 database on a mainframe) via a JDBC connector. A Glue job extracts the data, converts it to an efficient file format like Parquet, and loads it into a designated 'raw' zone in an AWS S3 bucket. 2) Databricks is configured to access this S3 bucket. A Databricks job, often using Auto Loader, automatically ingests the new raw files from S3 into Delta Lake tables, providing schema evolution and data quality guarantees. 3) dbt connects to the Databricks SQL Warehouse. Developers define transformation logic in dbt models (SQL) to clean, enrich, and aggregate the raw financial data into business-ready 'marts' tables within the Databricks Lakehouse. The entire workflow can be orchestrated using Databricks Workflows or an external tool like Apache Airflow.

Dismantle Your Data Silos: Get the Playbook

Discover the blueprint to unify legacy systems, accelerate regulatory reporting, and build unshakeable trust in your financial data.