Modern Data Stack

What is a Modern Data Stack?

A modern data stack (MDS) is a collection of cloud-based components and tools used to collect, store, process, analyze, visualize, and share data. Made up of multiple layers and best-of-breed Software-as-a-Service (SaaS) tools from different vendors, the modern data stack covers the end-to-end data lifecycle in a modular manner.

Due to its cloud-based nature, the modern data stack is scalable, efficient and agile, and is designed to be easy to use and manage. It is able to cope with the highest demands and most complicated of data pipelines in terms of volume, variety and velocity, maximizing value from data.

What is the difference between a Modern Data Stack and a legacy data stack?

Traditional (legacy) data stacks rely on on-premise solutions and infrastructure, such as servers and storage. This makes management more complex, requiring greater IT administration resources and difficulties in scaling efficiently or quickly. Legacy data stacks therefore need significant technical support, whereas modern data stacks are easier to manage and can be used by less technical data professionals, helping to drive forward data programs within organizations.

What does a Modern Data Stack contain?

A modern data stack normally consists of these modular tools, normally provided by different vendors:

• Data sources (such as business systems, sensors)
• Extraction and transformation tools to prepare data for use
• Cloud data warehouse to store data
• Transformation tools to update and process data within the cloud data warehouse
• Data orchestration tools, making data available for analysis
• Experience, visualization and Business intelligence (BI) tools to share and analyze data

All of these SaaS tools and layers integrate together, linked through APIs to create the full modern data stack architecture.

What is a Modern Data Stack used for?

The modern data stack enables three key use cases that maximize data value:

1. Data analytics – making it easy and fast to analyze large volumes of disparate data to support better, more informed decision-making, such as through dashboards that can be kept constantly up to date
2. Process automation – increasing efficiency and productivity by creating seamless, end-to-end processes, underpinned with data, reducing administration time and resources
3. Product development – allowing the faster creation of data products and services for both internal and external audiences, opening up new opportunities and potential revenue streams

What are the benefits of a Modern Data Stack?

Overall, the modern data stack saves time, money and effort, while enabling innovation and the creation of a data-driven culture. It delivers the following benefits:

• Scalability – as components are cloud-based, the modern data stack can easily be scaled up or down to cope with changing data volumes and needs
• Speed – due to its integrated, API-driven nature and access to cloud processing providers, the modern data stack operates much faster than traditional architectures
• Efficiency – with no requirement for buying and managing on-premise servers or large upfront costs for software, companies are able to save money and operate more efficiently. Ongoing administration time and cost is also reduced through automation
• Modularity – while tools may be from different providers, they are interoperable through APIs. This delivers choice and enables best-of-breed tools to be easily added or replaced as needs change. Additionally, different types of user are able to work with data through their preferred tools – for example data analysts can use BI tools while data scientists can use more specialist tools
• Flexibility – administration is simpler, and consumption-based SaaS pricing means that the modern data stack can be easily adapted to specific needs, now and in the future
• Data culture – by enabling data to flow more seamlessly around the organization, the modern data stack helps build a data-centric culture. More people have access to data, improving insights and helping spread data democratization across the business