Market Intelligence with Custom Data Platform for Public Financial Data

Product Overview:

Background- Financial services are in need of a centralized, more comprehensive, secure, fast and scalable database. They have to process huge volumes of data in lesser time and apply analytics to golden copy data to find intelligent insights. Common challenges that financial institutions decision-makers face:

• Lack of a unified data platform. Different business units have different/proprietary data silos.
• Customer data/public data is split among multiple business units and data platforms; thus, no centralized repository is available making it difficult to apply analytics.
• Inability to derive actionable insights from multiple data sources.
• Lack of a highly available, resilient and horizontally scalable platform for data quality.
• Lack of APIs to expose analytics to other applications, to easily search required data.
• Slow time-to-market of new features.
• Launching new products fast, especially involving private market assets, with the complexities of set up, valuations and reporting
• Challenges for regulators to support public policies are significant, and jurisdictional differences create a highly complex compliance environment
• Need for rapid solutions to handle increasing complexity cause by external demands for instance being able to comply with a new regulation or introduce a new financial instrument

Features-

• Enables immediate access to all data

• Accelerates delivery of applications for business units

• Facilitates productionizing advanced analytics

• Offers fully automated setup of data lake stack with AWS CDK

• Supports ETL code with various languages such as Python and Java

• Deploys ETL code as Lambda using serverless architecture

• Allows for CI/CD of infrastructure and ETL code

• Monitors ETL pipeline status using DynamoDB and CloudWatch

• Enables creation of multiple transformations with AWS Step Functions

• Reduces total cost of ownership (TCO)

Benefits –

• Elasticity and scalability: Easily handle growing data volumes and changing business needs without compromising performance

• Flexible application: Supports a range of use cases and lets you adapt quickly to changing requirements

• Centralized governance: Helps you maintain control and consistency across your data landscape while enabling data access for different stakeholders

• Democratized data: Empowers your teams to access and use data without being limited by traditional data silos or restrictions

• Reduced operational costs: Streamlined, automated data management approach optimizes resources and eliminates inefficiencies.

Common use cases –

• Market analysis: Gain insights into market trends and investment opportunities with real-time and historical financial data.

• Risk management: Identify and mitigate potential risks with comprehensive risk analysis tools, monitoring credit, market, and liquidity risk.

• Portfolio management: Optimize portfolios and maximize returns with accurate and up-to-date information on asset performance.

• Compliance management: Ensure compliance with regulations and reporting requirements by automating compliance tasks and reducing the risk of errors.

• Competitive intelligence: Gain a competitive advantage by staying informed about industry trends, company performance, and other relevant information on competitors.

Deliverables -

1. Find the right S3 based data lake architecture pattern based on requirements
2. One Click Solution where the Tech Stack is fully automated with AWS CDK
3. A framework that allows the Data Engineers to write ETL code using any language and deploy them as Lambda Functions within the App
4. Providing CI/CD pipelines for Infrastructure and ETL code using AWS Code Pipeline

Add-ons:

• Visualize data with stunning visualizations using AWS Quick sight, PowerBI, and Streamlit.

• Easily integrate APIs with AWS API Gateway for seamless communication with other applications.

• Seamlessly integrate with popular platforms such as Snowflake and DataBricks for enhanced functionality.

• Effortlessly integrate with analytics and machine learning platforms.

• Fully managed service for hassle-free usage.

Three different options: (Different architecture options based on the requirement)

• Low code no code option with AWS Glue (simple transformations and not huge volumes of data)

• Write transformation logic in language of choice with AWS Lambda and Step Functions (complex transformation and not huge volumes of data)

• Support huge volumes of data with Amazon EMR (complex transformations and huge volume of data)