Building the Modern Data Stack

As almost 90% of organizations are executing on a multi-cloud strategy for migrating their data and analytics workloads to the cloud, the term “modern data stack” continues to gain more traction.

A modern data stack is a suite of technologies and apps built specifically to funnel data into an organization, transform it into actionable data, build a plan for acting on that data, and then implement that plan.

The majority of modern data stacks are built on cloud-based services, composed of low- and no-code tools that enable a variety of groups within an organization to explore and use their data.

Read on to learn how to optimize your data stack.

Why Modern Data Stack Matters Today

Big data stack technology now provides almost every organization the power to harness data without the massive upfront costs. Traditionally, investing in data required significant time and resources to build, manage, and maintain the requisite IT infrastructure. Today, creating a modern data stack doesn't suffer such barriers and can be accomplished in less than a day.

When organizations modernize their data stack, employees become more productive and effective. Because they can analyze volumes of raw data and derive highly actionable insights, organizations are able to create and maximize internal efficiencies, eliminate operational bottlenecks, accelerate decision-making and drive innovation. Simply put, organizations are able to build and centralize a unified high-value data asset that is easily accessible and can be used to drive value across their business.

A Five-Stage Build Process

To build a modern data stack, you need to focus on each stage and fill it with the tools that suit your requirements, goals, and other unique needs. Choose tools that are integration-ready, as this will streamline your workflows.

1. Get a data warehouse: A data warehouse is the central hub of your stack. It is where your data resides after it's collected from different sources and where data is prepared to be delivered to other apps such as business intelligence or data operationalization tools.

2. Pick a tool for data ingestion: Ingestion tools move and normalize your data from sources to storage. They prepare the data to be stored in a clean production environment. What makes this stage challenging is the overabundance of ingestion tools in the market as well as ensuring that the most valuable data is prioritized for ingestion. The ingestion process can be tricky, as you need to know if the data you're collecting is contributing to your ROI or not. You should also ensure that there are no redundant ingestion streams.

3. Tailor a value-driven analytics process: Your data stack must have its own analytics process specific to your organization's requirements and needs. It's important that creating an analytics process is left to data analytics teams, whether in-house or outsourced, as this requires human expertise. You should collaborate with talented analysts to create a data analytics process that maximizes the value of your data. This means establishing your goals and developing a method of collecting the data that will help your organization achieve those goals.

4. Create a process for data transformation and modeling: This stage is all about finding the right metrics and aligning these metrics to your organization. Making this process more complicated is the high level of SQL knowledge required. your organization does not have people with considerable SQL expertise, you can turn to on-demand teams of data specialists to help define and create your data models.

5. Choose an ELT tool: An ETL (Extract, Transform, Load) tool is critical to your modern data stack. This solution transfers your data from your data warehouse back into your third-party business tools. What this process does is it makes your data fully operational. Today's ETL tools can do the process in minutes, resulting in faster data activation and implementation.

The Challenges of The Modern Data Stack

The modern data stack is a crucial component for today's organizations and requires enterprises to embrace a lot of changes including adopting emerging technologies or changing operational models. Poor execution, unoptimized cloud performance management, and other strategic missteps can be expensive and risky.

■ Delivering actionable data to all: Any piece of information is useless to someone if it's not actionable and doesn't give any value at all. A few years ago, the big data technology stack was exclusive to data analysts, engineers, and scientists. But with enterprises able to create their own modern data stack, people who traditionally didn't interact with data, like marketers, salespeople, and finance and operations teams are now part of the data picture. It's no longer a question of access but, rather, how can organizations make data and insights actionable to people with different skill sets, functions, and purposes. In most cases, companies address this by adding extra tools to their data stack for business intelligence, data science, and data transformation. While this works most of the time, compounding multiple tools also contribute more complexity and added costs to modern data stack.

■ Data Governance: As enterprises begin to accumulate data, it becomes increasingly important for the organization to know which teams and people have access to what type of data, how they should work with data, as well as when and where. The big data stack helps teams power up their innovations, pipelines, and transformations. It's crucial for organizations to have governance policies in place. Without policies and best practices, everyone can access and use data for their own functions and purposes, resulting in chaos. Modernizing the data stack provides enterprises the agility they need to maximize the value of their data. But it's also important for enterprises to provide frameworks and rules for access and usage.

■ Diverse Tool Ecosystem: The modern data stack trumps traditional monolithic data approaches with its ability to support and integrate multiple tools. However, the undeniable diversity of tools available in the market contribute to the complexity of building your data stack. Automation, scalability, and agility of deployment in the data stack all come into play. Finding a combination that works in your organization can be a complex and time-consuming process.

■ Poor Stack Visibility: It's crucial for IT teams and developers to have great visibility into their data stack. Observing what's going on in real time allows them to closely monitor application performance and apply the recommended configurations for optimized performance.

However, not all performance optimization tools in the market have enterprise-level visibility and provide observability beyond surface metrics. Without visibility, enterprises run the risk of overprovisioning resources for their data stack and ending up with more cloud costs than anticipated.

Conquer The Modern Data Stack

They say you can build a data stack from the ground up faster now than just a few years ago. While that may be true, working on your modern data stack is not a frictionless endeavor. The good news is that you have the opportunity to learn from industry professionals about conquering the modern data stack.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Building the Modern Data Stack

As almost 90% of organizations are executing on a multi-cloud strategy for migrating their data and analytics workloads to the cloud, the term “modern data stack” continues to gain more traction.

A modern data stack is a suite of technologies and apps built specifically to funnel data into an organization, transform it into actionable data, build a plan for acting on that data, and then implement that plan.

The majority of modern data stacks are built on cloud-based services, composed of low- and no-code tools that enable a variety of groups within an organization to explore and use their data.

Read on to learn how to optimize your data stack.

Why Modern Data Stack Matters Today

Big data stack technology now provides almost every organization the power to harness data without the massive upfront costs. Traditionally, investing in data required significant time and resources to build, manage, and maintain the requisite IT infrastructure. Today, creating a modern data stack doesn't suffer such barriers and can be accomplished in less than a day.

When organizations modernize their data stack, employees become more productive and effective. Because they can analyze volumes of raw data and derive highly actionable insights, organizations are able to create and maximize internal efficiencies, eliminate operational bottlenecks, accelerate decision-making and drive innovation. Simply put, organizations are able to build and centralize a unified high-value data asset that is easily accessible and can be used to drive value across their business.

A Five-Stage Build Process

To build a modern data stack, you need to focus on each stage and fill it with the tools that suit your requirements, goals, and other unique needs. Choose tools that are integration-ready, as this will streamline your workflows.

1. Get a data warehouse: A data warehouse is the central hub of your stack. It is where your data resides after it's collected from different sources and where data is prepared to be delivered to other apps such as business intelligence or data operationalization tools.

2. Pick a tool for data ingestion: Ingestion tools move and normalize your data from sources to storage. They prepare the data to be stored in a clean production environment. What makes this stage challenging is the overabundance of ingestion tools in the market as well as ensuring that the most valuable data is prioritized for ingestion. The ingestion process can be tricky, as you need to know if the data you're collecting is contributing to your ROI or not. You should also ensure that there are no redundant ingestion streams.

3. Tailor a value-driven analytics process: Your data stack must have its own analytics process specific to your organization's requirements and needs. It's important that creating an analytics process is left to data analytics teams, whether in-house or outsourced, as this requires human expertise. You should collaborate with talented analysts to create a data analytics process that maximizes the value of your data. This means establishing your goals and developing a method of collecting the data that will help your organization achieve those goals.

4. Create a process for data transformation and modeling: This stage is all about finding the right metrics and aligning these metrics to your organization. Making this process more complicated is the high level of SQL knowledge required. your organization does not have people with considerable SQL expertise, you can turn to on-demand teams of data specialists to help define and create your data models.

5. Choose an ELT tool: An ETL (Extract, Transform, Load) tool is critical to your modern data stack. This solution transfers your data from your data warehouse back into your third-party business tools. What this process does is it makes your data fully operational. Today's ETL tools can do the process in minutes, resulting in faster data activation and implementation.

The Challenges of The Modern Data Stack

The modern data stack is a crucial component for today's organizations and requires enterprises to embrace a lot of changes including adopting emerging technologies or changing operational models. Poor execution, unoptimized cloud performance management, and other strategic missteps can be expensive and risky.

■ Delivering actionable data to all: Any piece of information is useless to someone if it's not actionable and doesn't give any value at all. A few years ago, the big data technology stack was exclusive to data analysts, engineers, and scientists. But with enterprises able to create their own modern data stack, people who traditionally didn't interact with data, like marketers, salespeople, and finance and operations teams are now part of the data picture. It's no longer a question of access but, rather, how can organizations make data and insights actionable to people with different skill sets, functions, and purposes. In most cases, companies address this by adding extra tools to their data stack for business intelligence, data science, and data transformation. While this works most of the time, compounding multiple tools also contribute more complexity and added costs to modern data stack.

■ Data Governance: As enterprises begin to accumulate data, it becomes increasingly important for the organization to know which teams and people have access to what type of data, how they should work with data, as well as when and where. The big data stack helps teams power up their innovations, pipelines, and transformations. It's crucial for organizations to have governance policies in place. Without policies and best practices, everyone can access and use data for their own functions and purposes, resulting in chaos. Modernizing the data stack provides enterprises the agility they need to maximize the value of their data. But it's also important for enterprises to provide frameworks and rules for access and usage.

■ Diverse Tool Ecosystem: The modern data stack trumps traditional monolithic data approaches with its ability to support and integrate multiple tools. However, the undeniable diversity of tools available in the market contribute to the complexity of building your data stack. Automation, scalability, and agility of deployment in the data stack all come into play. Finding a combination that works in your organization can be a complex and time-consuming process.

■ Poor Stack Visibility: It's crucial for IT teams and developers to have great visibility into their data stack. Observing what's going on in real time allows them to closely monitor application performance and apply the recommended configurations for optimized performance.

However, not all performance optimization tools in the market have enterprise-level visibility and provide observability beyond surface metrics. Without visibility, enterprises run the risk of overprovisioning resources for their data stack and ending up with more cloud costs than anticipated.

Conquer The Modern Data Stack

They say you can build a data stack from the ground up faster now than just a few years ago. While that may be true, working on your modern data stack is not a frictionless endeavor. The good news is that you have the opportunity to learn from industry professionals about conquering the modern data stack.

The Latest

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...