Modern data stack: What challenges does it face?
Introduction
The modern data stack is widely popular among data - driven enterprises. This is not surprising, as the stack itself is powered by cloud - native tools designed to support artificial intelligence (AI), machine learning, and advanced analytics. The stack promises scalability, modularity, and speed.
Due to the huge amount of data generated globally, a stack is needed to manage the data. Statista predicts that by 2028, the global data generation will exceed 394 ZB, which further highlights the need for an advanced stack with high operational thresholds.
Everything seems to be in good order, but this is only in theory. As enterprises adopt this data stack, the situation changes. Teams often need to use multiple pipelines and platforms. Although the original intention was to simplify the process, the result is the creation of new "silos", such as increased complexity and fragmentation.
This is because teams within the same organization use a variety of tools to achieve different data functions. Although each tool has overlapping functions, the interoperability is far lower than expected.
What are the results?
- Redundant data pipelines, isolated workflows, and increased integration overhead have a significant impact on costs.
- Maintenance and integration require continuous resources and efforts.
- The costs of infrastructure and tools are constantly rising.
- The steep learning curve and specialized skills make it difficult to recruit new talents or democratize data usage.
The modern data stack is designed to facilitate faster insight generation. However, due to some obvious trade - offs, it may become a bottleneck. For organizations that want to scale their data and AI, a clear understanding of the challenges of the data stack is crucial so that it can become a partner rather than an obstacle.
Challenges of the modern data stack
The data stack has been evolving continuously. However, as mentioned above, some significant challenges prevent it from fully realizing its potential.
1. Tool fragmentation
Tool fragmentation is one of the most urgent challenges in today's modern data stack. A typical data stack consists of tools for data collection, transformation, storage, orchestration, BI, machine learning, and reverse ETL, etc., each with its own functions. However, this approach creates a bloated ecosystem of multiple tools, and the integration between these tools does not even reach the expected level.
The lack of interoperability between tools increases the overall complexity. Teams spend a lot of time integrating these tools correctly instead of solving actual business pain points.
The redundant workflows brought by tools with overlapping functions cause many troubles in decision - making among teams. Eventually, it becomes extremely difficult to manage configuration consistency, lineage, and access rights.
2. Operational complexity
Fragmentation leads to increased operational complexity. Why? Each tool requires a specific set of monitoring, expertise, and configuration. This pushes the burden on the data team to the limit, as they must maintain the infrastructure, handle emergencies, adjust performance, and ensure the normal operation of the entire data stack.
One of the most significant problems caused by this complexity is its impact on overhead, which increases sharply. More tools create more pipelines to debug, increase the integrations to monitor, and result in more tasks being delegated among different teams. The modular architecture becomes a mess, filled with too many responsibilities, slowing down the progress and putting everything at risk.
3. Data quality and trust gap
Enhancing data quality is an important goal of any data stack. However, inconsistent standard verification, ambiguity of data ownership, and pipeline failures lead to the loss of data trust. Due to the lack of testing and observability, teams are always slow to respond to quality problems and only pay attention to them when these problems affect decision - making in an unwise way.
The traditional data quality lifecycle. Aspects such as quality monitoring and data contracts are still in their infancy and have not been tightly integrated with the workflow. What are the results? Users question the timeliness, integrity, and accuracy of the data. Without absolute trust as a support, the consequences will be redundant work, project suspension, and dependence on manual spreadsheets. The value of the entire technology stack will be reduced.
4. Metadata debt
Metadata management is one of the most under - developed areas in the modern data stack. As new tools enter the data ecosystem, metadata is often the first to be affected, becoming outdated or fragmented.
Put simply, metadata is the context around data, or the meaning and relevance behind the data. It tells the story of the data. What does this data mean? Where does it come from? How often does it arrive? Where is it located? Who is using it? What is its purpose? How often is it used? And so on...
In short, without metadata, data has no value and will fall into chaos. Not surprisingly, most organizations have a large amount of useless data because it is disconnected from the core semantic model. In common terms, this is called "dark data". Dark data does not refer to storage costs, but to the cost of wasted money due to the failure to fully utilize rich and valuable data.
The three rules of metadata:
Partial metadata releases partial value of the data.
Metadata streams that do not communicate with each other do not generate new and valuable metadata.
Metadata is most meaningful when it is extracted from the entire journey rather than from limited boundaries or components.
Therefore, the metadata collection process itself affects the potential of metadata. It is not enough to simply collect metadata; collecting it correctly is of utmost importance.
The following is a comparative overview of the two collection methods.
Assembly system or metadata on the modern data stack
Metadata is partially injected by different externally integrated components. There is not enough space for continuous interaction between these different components, so rich metadata cannot be generated from a dense network.
This situation leads to the generation of metadata debt, which is one of the biggest challenges faced by the modern data stack. The cost is poorly defined data, lack of context, and poor discoverability, as data analysts need to spend a lot of time locating and verifying data. In addition, due to the lack of necessary visibility of existing assets, engineers have to bypass the pipelines.
Unified system
The unified architecture consists of loosely coupled and tightly integrated components that interoperate/network closely with each other and generate and capture dense metadata in the process. This metadata circulates back to the components on a unified plane.
5. Lack of clear ownership
The whole premise of the modern data stack is to improve flexibility through tools. However, this has led to a lot of confusion in clearly defining the ownership of the data team.
Different tools for data collection, transformation, orchestration, and other related functions lead to the dispersion of responsibilities among different teams and roles. In the context of the end - to - end data lifecycle, there is a lack of accountability for each function. The fragmented architecture creates a lot of confusion, weakens accountability, and slows down the problem - solving speed.
Effective data governance is also affected, as the enforcement of policies and data standards often crosses team boundaries. Correct data ownership requires more than just assigning names to data sets or dashboards to truly become an enabler.
6. Gaps in compliance, security, and access control
As the amount of data increases, the related risks also increase. A report from Cyber security Insiders points out that 91% of cybersecurity professionals believe that their systems are not ready to deal with zero - day vulnerabilities or newly discovered vulnerabilities. This indicates that the existing compliance practices are lagging behind in the progressive data stack.
Yes, the tools in use have their own access controls, but without a hybrid governance framework, vulnerabilities will soon appear. Problems such as inconsistent role access, weak audit links, non - compliance with standards such as the Personal Information Protection Law, and insufficient encryption will gradually accumulate and weaken the processes and pipelines over time.
7. Silos and shadow flows
Ironically, the data stack used to unify data eventually recreates the "silos" that it was originally designed to eliminate. This is because different teams have their own tools, pipelines, and processes, which leads to redundant workflows and inconsistent data access.
When data governance is weak, it leads to shadow workflows, where unauthorized data sets, undefined pipelines, and isolated dashboards are daily challenges beyond the defined governance control, causing compliance risks, duplicate logic, and inconsistent reporting.
The impact of modern data stack challenges on return on investment
The modern data stack seems to be a winning opportunity, as it prioritizes scalability, agility, and data democratization. However, once an organization starts to adopt a variety of tools, each with limited functions, the overall complexity calls the return on investment into question.
Although speed and agility are the key points of concern, too many incoherent tools lead to disjointed integrations, new silos, and a sharp increase in operational overhead.
The biggest challenge here is that it is not only the data team that is affected, but the entire organization. Users face delays in obtaining the correct insights, the trust in data is diluted, and data governance becomes a reactive rather than a proactive approach. Indeed, although each tool brings some benefits, the costs of monitoring, orchestration, and compliance are constantly rising.
The stack becomes "modern", but efficiency and return on investment are affected. Since teams need to spend a lot of time integrating fragmented pipelines instead of working hard to ensure positive strategic results, the time to obtain actionable insights also increases. To obtain the correct value, organizations need to synchronize their data strategies with the principles of product thinking. This is crucial for creating the correct business impact.
The future of the modern data stack: A data - first approach
As organizations deal with the complexity of the modern data stack, a version emerges where data takes precedence over the influence of various tools and architectures. This is the "data - first" stack approach, where the entire data ecosystem is built around the data lifecycle, accessibility, and data value, rather than simply unifying data through different technologies.
The Data Developer Platform (DDP) is a self - service infrastructure standard and a key element of this transformation. It serves as a framework to enable teams to efficiently create, manage, and scale data products. The DDP is deeply rooted in the self - service principle, allowing each domain team to have ownership without specific infrastructure knowledge. The self - service feature transforms the modern data stack from a fragmented collection of tools into a well - functioning machine.
The Data Developer Platform standard for building a unified infrastructure.
Essential elements of the data - first stack
There are many important factors at play in the data - first stack:
- The DDP can incorporate operational simplicity as a built - in function, providing centralized monitoring, policy implementation, and lineage tracking throughout the data lifecycle.
- With the modular Lego - like components of the DDP, the technology stack becomes a set of loosely coupled and tightly integrated components instead of hard - coded tool integrations, making ingestion, transformation, access control, and storage seamless across the entire organization.
- The data - first approach ensures that governance is deeply embedded in each layer, from access control to metadata, to ensure compliance, security, and trust.
- Combined with the Data - Driven Delivery (DDP) function, the data - first approach can bring significant results in just a few weeks (instead of months). The principles of the data mesh include decentralized ownership and centralized standards for seamless delivery.
Solutions, not conclusions
The "modern" in the modern data stack is not just an adjective, but a highlight. It leans towards a self - service platform that helps enterprises quickly provide data solutions and becomes a necessity for the data mesh approach.
With this data stack, enterprises can fully realize the potential of all their services and tools through standardized integration, access, resource optimization, and other low - priority complexities. All of this is achieved through the Data Developer Platform (DDP).
It allows development teams to easily build and deploy applications through a set of tools and services, thereby better managing and analyzing data. The unified function of the DDP is one of its greatest advantages, providing a single management point for complete management.
The message is clear: The challenges faced by the modern data stack are huge, but the thought process rooted in the data - first concept is crucial for solving these challenges.
Conclusion
The year 2025 is full of new opportunities: AI in all industries will become more specialized, autonomous systems will be more deeply integrated, and the demand for real - time, privacy - focused solutions will surge. This year, we should not only focus on smarter AI, but also on AI that can act, adapt, and create tangible value in all fields.
In 2025, the field of data engineering is bound to see some exciting updates. New technological updates appear almost every day. Mergers, acquisitions, and funding in this field indicate a brighter future.