Why the modern data stack is broken (and how to fix it)

In 2015-2016, the modern data stack (MDS) emerged as a significant concept within the data community. Although it was never formalized as an industry, software category, or recognized by major analyst firms, the MDS represented a collection of products and services that redesigned data management and analytics workflows to leverage the cloud and interact via SQL and other common languages like Python. 

Classic examples include Looker, built for Redshift to exploit its performance and scalability, and Stitch, which simplified data loading. Tools like Matillion performed the full data lifecycle in cloud data warehouses, transforming ETL into ELT, making data processes more cost-effective, lower friction, and more accessible. The MDS helped convey the advantages of these cloud-native tools, differentiating them from outdated ones.

The MDS solved many problems in the pre-cloud and big data eras. Before the cloud, data management and analytics were organized and unified across database and analytic vendors, but they weren't scalable for large volumes of data coming into the enterprises. The advent of the cloud, with services provided by AWS, Azure, and Google, offered scalable, flexible, and cost-efficient analytics and data management platforms. However, this new environment required new tools, which the MDS provided. Several vendors decomposed the data integration and management process into individual categories, such as loading, transforming, and orchestration layers.

Despite its initial success, the MDS posed challenges for enterprises. Implementing the MDS required purchasing and integrating multiple tools to complete their data management platform, resulting in a complex and fragmented system. Today, most tools are equipped to work natively in the cloud or have adapted, making the term "modern data stack" increasingly irrelevant. 

Welcome to the post-modern data stack era

We now need to transition to the post-modern data stack (PMDS), where the focus shifts to consolidating tools and simplifying data integration and management. We need to look back at pre-cloud days and learn some of the lessons from how platforms worked seamlessly together with less disparate tools.

The primary issue with the MDS today is the need for enterprises to stitch together multiple tools, which can be cumbersome and inefficient. Most enterprises prefer to consolidate their data integration and management tools into one, two, or three comprehensive solutions to form their platform. They want to focus on the data itself and the insights it can provide, rather than managing a complex software ecosystem. 

At Matillion, we anticipated this shift and developed a unified data integration platform that supports all enterprise workloads, whether BI or AI. Our platform offers a viable, scalable, and cost-effective solution that fundamentally leverages cloud architectures. By embracing a full pushdown architecture, we ensure compatibility with SQL, AI, large language models (LLMs), Python, and more. This approach simplifies data integration and management, aligning with the needs of modern enterprises.

The transition to a PMDS is essential as we enter the era of AI and advanced analytics. Enterprises require streamlined, integrated solutions that reduce complexity and enhance efficiency. The MDS concept, while groundbreaking in its time, is no longer sufficient to meet these evolving needs. A PMDS addresses the shortcomings of the MDS by providing consolidated, comprehensive tools that enable enterprises to focus on deriving insights from their data rather than managing disparate software components.

Takeaways 

The modern data stack played a crucial role in the evolution of data management and analytics. However, as the industry progresses, the need for a more integrated and simplified approach has become apparent. The post-modern data stack offers a path forward, addressing the challenges of the MDS and providing a foundation for the future of data-driven enterprises. By adopting unified platforms like Matillion's, organizations can navigate the complexities of the cloud era and unlock the full potential of their data.

Download Matillion’s Guide to Data Debt to learn more about why data debt grows over time and what you can do about it.