Nasdaq's Transformation: Leveraging Active Metadata for Enhanced Data Strategy
Technology Category
- Analytics & Modeling - Data-as-a-Service
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Product Research & Development
Use Cases
- Inventory Management
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
- System Integration
About The Customer
Nasdaq is a global technology company serving the capital markets and other industries with diverse offerings that include trading, clearing, exchange technology, regulatory, securities listing, information, and public and private company services. It is the world's second-largest exchange, with $18 trillion in market capitalization. Nasdaq has been a data-driven company for over five decades and is becoming a leader in data technology. Despite being in the cautious and often slow-moving financial sector, Nasdaq began their cloud journey with AWS as far back as 2012 and has been systematically moving towards the cloud ever since.
The Challenge
Nasdaq, the world's second-largest exchange, has been a data-driven company for over five decades. Despite having a decade of experience operating in AWS and moving the bulk of its critical workloads to the cloud, Nasdaq faced significant challenges. The trading system data was complex in size and structure, with as many as 140 billion events processed per day in the U.S. alone. The data was optimized for operational performance, not for analytics, making it difficult to manage. Additionally, Nasdaq's process for preparing and presenting data was outdated, with their legacy ETL tools unable to keep up with the scaling types of data and demand. The rigidity of these tools did not align with Nasdaq's ambitions. The data team was overwhelmed with maintaining the technical landscape and struggled to support their business partners effectively. This led to the emergence of parallel teams, each with a unique approach to creating data solutions, causing inefficiencies and confusion.
The Solution
To address these challenges, Nasdaq implemented a modern data stack and restructured their team. They adopted dbt to accelerate their team's ability to build data models and continued to operate on AWS, using Redshift as their primary data store. They also used a variety of AWS services such as S3 for their data lake, Glue for data integration, and QuickSight for business intelligence. To improve data observability, they adopted Monte Carlo. The final piece of the puzzle was Atlan, which was adopted as Nasdaq's window to their modernizing data stack and as a vessel for a maturing data governance practice. Nasdaq's data team was restructured into more specific roles, including the Platform Team and the Economic Research team. They also developed a more mature engagement and onboarding model as users began to exploit new capabilities and data. Finally, they turned to active metadata management as a path for education, collaboration, and discovery, choosing Atlan for this purpose.
Operational Impact
Quantitative Benefit
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