In this post, we’ll cover some of the basics on Legacy Data Stacks (LDS) and Modern Data Stacks (MDS). These are the main questions you’ll be able to answer after reading:
A combination of technologies or tools, used to gather, store, and transform data. The point of data stacks is to enable teams to turn raw data into valuable data for analytics, insights and AI. In simple terms, Modern Data Stacks are improved data stacks where the main pain points of traditional data stacks have been addressed.
A Modern Data Stack is a cloud-based, flexible, and easily scalable data infrastructure tailor-made to the unique needs of each business.
The stack is composed of a collection of different modular data components that work in unison to generate clean, actionable insights by everyone: technical, non-technical, and final business users.
The purpose of this stack, which centers around an ecosystem of tools, is to support the collection, movement, storage, transformation, analysis and operationalization of data. Essentially, to make businesses data-enabled and truly data-driven.
A company’s technology stack is its foundation, driving the value of its data.
Data-driven companies need effective and rapid data flow to the correct teams to enable valuable insights and analytics.
Without infrastructure, the right tools, and processes, data is detached from its value.
Modern Data Stacks are cloud-based. This makes them scalable: data infrastructures can grow in line with your data needs without any associated costs or downtimes involved when scaling. Scalability means improved adaptability to business needs.
Before, most companies reached scalability caps due to costs, maintenance, or hardware limitations.
These limitations are not present on the cloud.
However, the cloud isn’t always an option. Sometimes compliance regulations may force a company to keep some data on-premise. One approach, in this case, is to migrate parts of the DW and keep others on-premise, also known as DW Augmentation.
Modern Data Stacks don’t force users into tooling components that “come in the box”. They allow users to swap out components without affecting the proper functioning of the rest of the stack. With a Modern Data Stack, each company handpicks the best tools for each component, considering goals and constraints.
Scaling and adapting processes or the platform itself in locally hosted legacy data services is more expensive, time-consuming, and technically complex than in modern data stacks. The technical barriers to implementing and adapting modern data stacks are lower, this also includes the barrier to integrating tools in the stack.
Moreover, the total cost of ownership of deploying infrastructures on a Cloud like Amazon Web Services (AWS) is significantly cheaper than on-premise traditional solutions. With traditional solutions, infrastructure and other resources generally need to be estimated and paid upfront as capital expenditure (CAPEX), while ongoing maintenance and monitoring need to be owned by the client. However, Cloud services allow pay-as-you-go models and serverless models that reduce fixed and operating costs.
Downtime costs when scaling or changing infrastructures are much lower. Cloud elasticity and resource availability allow for virtual environments where you can replicate a dev environment and test changes simply without impacting the original environment. With Cloud Computing and storage costs declining, this trend will continue to favor Modern Data Stack solutions in terms of costs.
Many tools that make up a Modern Data Stack are accessibility focused. For certain parts of the stack, usually associated with reporting and analytics, little to no code is needed. The technical know-how required to access data by final business users (marketing, sales, etc.) is reduced, enabling them to easily and routinely consult data to generate insights.
The possibilities for integrating Business Intelligence, marketing, or sales tools are endless.
Furthermore, modern data stacks drastically reduce time-to-market, or in this case, time-to-report. Essentially, this means increasing the value of your data which is only as valuable as the time it takes to become an insight that can be turned into a competitive advantage.
Modern Data Stacks, Machine learning and AI implementations can be quite challenging and failure prone. Companies spend significant amounts of time and money implementing these solutions.
Mutt Data can help you crystallize your data strategy through the design and implementation of technical capabilities and best practices. We study your company’s business goals to understand what has to change so we can help you accomplish it through a robust technical strategy with a clear roadmap and set of milestones. Talk to one of our sales reps at firstname.lastname@example.org or check out our sales booklet and blog.