As technology advances, so does the data stack. Before you go into deploying a modern data stack at your company, here are some important things to know.
The modern data stack has transformed the way businesses approach data management, allowing them to use data to make educated decisions and plan strategically. The modern stack provides enterprises with a comprehensive solution for gathering, storing, processing, and analyzing data through its integrated array of technologies and applications for data warehousing, cloud-based analytics platforms, ETL pipelines, and machine learning algorithms. Companies may obtain more detailed information, make informed choices, and eventually drive development and success in today’s hyper-competitive marketplace by harnessing the power of the modern data stack.
This article will explore what a modern data stack is, how it evolved, its benefits, and the core differences between a legacy stack and a modern stack.
What Is a Modern Data Stack?
A data stack is an array of tools and technologies used for data collection, analysis, and storage. The term “modern data stack” refers to a cloud-centric strategy for data handling that stresses flexibility and prioritizes the delivery of processed data. It consists of numerous applications that work together to deliver a comprehensive data processing solution.
Evolution of Modern Data Stack
The modern data stack has its roots in the early 2010s, with the advent of large-scale data commoditization. During this time, two significant events occurred that resulted in an evolution beyond conventional stacks.
The first was the widespread use of cloud computing, which allowed data to be viewed and processed from any location. Various cloud computing platforms provide scalable infrastructure. This implies that businesses may scale up or down their data operations depending on the requirements without investing in and operating on-premises hardware. These considerations boosted the popularity of modern data stacks significantly.
The second event that led to the advent of modern data stacks was the transformation from ETL -Extract, Transform, Load analysis procedures to ELT -Extract, Load, Transform. ELT enables enterprises to handle enormous quantities and a wide range of data types by employing distributed storage and computation frameworks at ease. Furthermore, ELT’s low cost and interoperability with current data architectures like data lakes and cloud computing make it an appealing alternative for enterprises looking to maximize the value of their data in a constantly shifting data landscape.
Legacy Data Stack vs. Modern Data Stack
The major difference between a traditional and modern data stack is related to cloud-based tools and on-premises hardware. Legacy data stacks are totally on-premises, which means that the hardware must be separately provisioned, managed, and expanded with the changing needs of the business.
Modern data stacks, on the other hand, are completely hosted in the cloud, which means that all of the primary maintenance associated with managing hardware is handled effortlessly as a service. Cloud and SaaS-based products relieve users of a significant load by allowing them to focus on business objectives rather than technology. Modern data stacks are also significantly less expensive because users only pay for usage rather than having to buy the underlying resources themselves. This is made possible by the massive economies of scale that major SaaS systems provide.
Benefits Of a Modern Data Stack
Besides substantially reducing the technological hurdle to entry, a modern data stack has other advantages. To begin, contemporary data stacks are designed with corporate clients in mind. The cloud-based architecture’s modular design minimizes vendor lock-in, eliminates data silos, and integrates everything into a single central data warehouse. Second, SaaS applications are highly scalable and cost-effective. Companies may simply be charged for consumption and manage resources up or down instantly based on demand, rather than provisioning equipment and estimating usage over the following year. By adopting off-the-shelf connectivity, data engineering and analytics teams save substantial time and are able to concentrate their time and attention on delivering business objectives.
The modern data stack is an interesting field, with a lot of new vendors entering the market on a daily basis. However, the current stack should not only serve the requirements of major tech organizations but also a broader set of people within the organization, with a greater emphasis on corporate results. It is likely that, with the deployment of the Sub-ML use case strategy, it will soon become verticalized, integrating with small and medium-sized organizations.
The most important reason to use a modern data stack is that it reduces the time it takes to arrive at vital insights. Your data stack is essential for enhancing your data strategy while making solid business decisions. This means enhanced products, a more efficient go-to-market strategy, and improved data maturity. However, it is critical not to get caught up in the digital hype and update only for the sake of upgrading. To genuinely benefit from an MDS, considerable preparation is required to ensure a positive user experience. Develop a good MDS and let your people do their tasks, and the result will be tremendous.