What Is Compute Over Data Architecture? A Simple Guide

Managing data across multiple countries is a compliance minefield. Regulations like GDPR and HIPAA place strict limits on where sensitive information can be stored and processed, turning every analytics project into a legal review. The traditional approach of centralizing data creates a massive risk, forcing you to move protected information across borders and increasing your security exposure. A compute over data architecture offers a fundamentally more secure and compliant approach. By leaving data in its original location and sending the computation to it, you can gain insights without ever moving sensitive records, making governance simpler and more robust by design.

Key Takeaways

• Flip the traditional data model: Instead of moving massive datasets to a central location, send lightweight compute jobs directly to the data's source. This fundamental shift is the key to overcoming the bottlenecks and high costs of traditional data pipelines.
• Solve critical cost and speed challenges: This approach directly reduces major expenses like cloud egress fees and platform ingest costs while eliminating the latency caused by data movement. The result is faster, more efficient analytics and a significant reduction in infrastructure spending.
• Strengthen security and simplify compliance: By processing data where it lives, you minimize data movement, which is a primary source of security risk. This makes it far easier to enforce data residency rules like GDPR and HIPAA and maintain a clear audit trail without compromising on analytics.

What Is Compute Over Data?

At its core, Compute over Data is a simple but powerful idea: instead of moving massive amounts of data to a central place for processing, you move the processing to where the data already lives. Think about how much time, money, and effort your teams spend on complex data pipelines. They extract data from various sources, load it into a centralized warehouse or lake, and then transform it for analysis. This traditional model was the standard for years, but it’s becoming a major bottleneck for modern enterprises.

As data volumes explode and sources become more distributed—spanning multiple clouds, on-premise data centers, and edge devices—the old way of doing things just doesn’t scale efficiently. Moving petabytes of data across networks is slow, expensive, and creates significant security and compliance risks. The Compute over Data architecture flips this model on its head. It allows you to run your analytics, AI/ML models, and other jobs directly on the data at its source. This approach minimizes data movement, which in turn cuts costs, reduces latency, and simplifies your entire data infrastructure. It’s about working smarter, not harder, with the data you already have.

How It Differs From Traditional Architecture

The traditional approach requires you to centralize everything. You build and maintain fragile data pipelines to pull data from applications, logs, and IoT devices into a single data warehouse. This process is not only expensive due to data transfer and storage costs, but it also introduces delays. By the time the data is ready for analysis, it might already be stale. This latency is a huge problem for use cases that require real-time insights, like fraud detection or operational monitoring. Centralizing data also creates compliance headaches, especially when dealing with data residency rules like GDPR or HIPAA.

Compute over Data offers a more direct and efficient path. By leaving the data in place, you eliminate the need for costly and complex data movement. Your distributed computing platform sends the computational task—the code or algorithm—to the data’s location, whether that’s in another cloud region or on a factory floor. The job runs locally, and only the small, lightweight result is sent back. This decentralized method is faster, more secure, and far more cost-effective, especially for organizations struggling with data gravity and regulatory constraints.

The Core Idea and Its Benefits

The main goal of Compute over Data is to make your data processing more efficient, timely, and secure. By decentralizing computation, you can analyze information right at the source, gaining insights in minutes or hours instead of days or weeks. This has a massive impact on your business. Your teams can make faster, more informed decisions, and your engineers can spend less time managing brittle data pipelines. This approach also inherently improves data privacy and governance. Since sensitive data doesn't have to be moved or duplicated, you drastically reduce the risk of exposure and make it easier to enforce access policies.

The financial benefits are just as compelling. Organizations that adopt this model often see significant reductions in their infrastructure spending. You can cut down on expensive data transfer fees, lower your cloud storage bills, and optimize your use of compute resources. In fact, it’s possible to reduce data infrastructure costs by up to 80% without sacrificing performance. This is why so many enterprises are exploring Expanso's solutions to get control over their runaway data platform costs while accelerating their AI and analytics initiatives.

Why Should Your Enterprise Adopt This Model?

Adopting a Compute Over Data model isn't just a technical upgrade; it's a strategic shift that addresses some of the most persistent challenges in enterprise data management. If your teams are wrestling with slow analytics, unpredictable cloud bills, and infrastructure that can't keep up with new data sources, this approach offers a practical path forward. Instead of continuing to invest in a centralized model that creates bottlenecks and inflates costs, you can process data intelligently, right where it's created. This leads to faster insights, significant cost savings, and a more flexible architecture that can grow with your business needs, from the data center to the edge. It's about working smarter, not harder, with your data. By flipping the traditional script of 'bring data to the compute,' you empower your teams to act on information immediately, maintain stricter governance over sensitive data, and build a more resilient data ecosystem. This model is designed for the realities of modern data—distributed, diverse, and growing exponentially—and provides a foundation for innovation rather than a constant source of infrastructure headaches.

Gain Speed and Reduce Latency

In a traditional setup, data has to travel from its source to a central processing hub. This journey takes time and creates latency, delaying critical business insights. For time-sensitive operations like fraud detection or real-time analytics, these delays are unacceptable. A Compute Over Data architecture eliminates this bottleneck by bringing the processing power directly to the data. With lightweight, distributed job orchestration, you can run machine learning jobs and queries across an entire network of devices. This means you get answers in minutes or seconds, not hours or days, because you’re no longer waiting to move petabytes of data before you can even begin your analysis.

Cut Costs by Moving Less Data

Moving massive volumes of data is expensive. You pay for network egress fees, redundant storage copies, and the powerful infrastructure required to handle it all. These costs can quickly spiral out of control, especially with rising data volumes from sources like logs and IoT sensors. By processing data at its source, you fundamentally change the cost equation. You only move the results—the valuable insights—not the raw data itself. This approach can help enterprises save 40–80% on data infrastructure costs without compromising performance or security, turning a major cost center into a source of efficiency.

Achieve Greater Scalability and Flexibility

Centralized data architectures eventually hit a scaling wall. As your organization expands globally and deploys more edge devices, funneling everything back to a central core becomes impractical and fragile. A distributed model is inherently more resilient and scalable. It allows you to manage the massive data loads modern enterprises depend on by processing data locally, whether it's in another country to meet data residency rules or on a factory floor for immediate analysis. This gives you the flexibility to build a truly hybrid infrastructure that spans on-prem, multi-cloud, and edge environments, adapting easily as your data processing needs evolve.

How Does Compute Over Data Actually Work?

At its core, the compute over data model is a practical shift in how we approach data processing. It combines a few key principles to create a more efficient, secure, and cost-effective architecture. By sending lightweight compute jobs to your data’s location, you get insights faster without the cost and complexity of moving massive datasets. Let’s break down the mechanics behind this approach.

Understanding Distributed Processing

Think of distributed processing like managing a large project. Instead of giving the entire thing to one person, you break it into smaller tasks for a team. In a data context, this means breaking up a large computational job and sending the pieces to different machines across a network. These machines—in a data center, a public cloud, or at the edge—process their portion of the data locally. This approach is fundamental to handling massive data loads, turning scattered IoT and cloud workloads into real business value.

The Principle of Data Locality

The core idea here is simple: it’s easier to move the work than it is to move the data. This is the principle of data locality. Traditional architectures require you to pull huge volumes of data into a central platform for processing—a slow, expensive, and risky process. Compute over data flips this model. It packages the processing logic into a lightweight container and sends it directly to where the data already resides. By decentralizing computation, you can analyze data more effectively. This reduces network traffic, lowers latency, and makes it easier to maintain security and governance because sensitive data never leaves its secure environment.

Integrating Edge and Cloud Environments

Today’s enterprises operate in a hybrid world, with data spread across multiple clouds, on-premise servers, and the edge. A compute over data architecture is designed for this reality, creating a unified fabric to run analytics jobs on data regardless of its location. This model provides a more resilient and cost-effective way to manage your infrastructure. You can process streaming data from IoT devices at the source or run queries on a distributed data warehouse without first consolidating everything. This flexibility helps you build future-proof data pipelines that adapt to new business needs.

What Are the Key Components of This Architecture?

A compute over data architecture isn’t a single, off-the-shelf product. Instead, it’s a strategic approach that brings together several key technologies to let you process data where it lives. Think of it as a blueprint for your data infrastructure, built on three core pillars: processing engines, a distributed storage layer, and the network that connects them. Each component plays a critical role in creating a system that is fast, cost-effective, and secure. By understanding how these pieces fit together, you can design a modern data stack that handles the demands of distributed data without forcing you to move everything into one central location. This model gives you the flexibility to analyze information across cloud, on-premise, and edge environments seamlessly.

Processing Engines and Frameworks

The processing engine is the heart of the compute over data model—it’s the "compute" part of the equation. These are the software frameworks that run your analytics queries, data transformations, and machine learning models. The key difference here is that these engines are designed to be distributed. Instead of pulling data into a central server, the architecture sends the computation out to wherever the data resides. This approach of separating compute and storage allows you to process data where it lives, which is a game-changer for reducing latency and strengthening data privacy. Your teams can get insights faster without exposing sensitive information by moving it across networks. This is the core principle behind platforms like Bacalhau, which orchestrate these distributed jobs efficiently.

What to Consider for Your Storage Layer

In a compute over data architecture, your storage layer is not a single, monolithic system. Your data is likely spread out across different locations and formats—and that’s perfectly fine. This model embraces that reality. A good data architecture turns raw data into a valuable resource, and that includes everything from massive, unstructured files in data lakes to the organized tables in your data warehouses. The goal is to leave the data in place, whether it’s in an S3 bucket, an on-premise database, or on an edge device. This flexibility allows you to build a distributed data warehouse that can query information across all these sources without a complex and costly ETL (Extract, Transform, Load) process first.

Meeting Network and Connectivity Needs

The network is the connective tissue that makes this entire architecture work. But instead of being a bottleneck for massive data transfers, it becomes a lightweight communication channel. Since you’re sending small computational jobs to the data, you drastically reduce the amount of information that needs to travel across the network. Expanso’s platform is built to bring computation to distributed data, which enables processing where data lives without vendor lock-in or expensive data transfers. This is especially critical for managing costs associated with cloud egress fees and for use cases involving remote or edge locations with limited bandwidth. Secure and reliable connectivity ensures that jobs can be dispatched and results can be returned efficiently, no matter where your data is.

Where Can You Apply Compute Over Data?

The real value of any architecture is how it performs in the real world. Compute Over Data isn’t just a theoretical model; it’s a practical approach that solves some of the most persistent challenges enterprises face today. By shifting computation to where your data already exists, you can unlock new capabilities and efficiencies across your entire organization. This model is incredibly versatile, offering tangible benefits whether you're trying to get faster insights from business analytics, train complex AI models, manage a fleet of IoT devices, or simply get your log processing costs under control.

The core idea is to stop treating data movement as a prerequisite for analysis. Instead of pulling everything into a centralized system—a process that’s often slow, expensive, and creates compliance headaches—you send the work to the data. This simple change has a ripple effect, improving latency, reducing network strain, and lowering your cloud and platform bills. It allows you to build more resilient, secure, and scalable data pipelines that can handle the demands of modern business. From the data center to the public cloud and out to the farthest edge, this approach provides a unified way to process information, making it one of the most effective solutions for building a future-proof data strategy.

Real-Time Analytics and Monitoring

Traditional data warehouses are powerful for business intelligence and reporting, but they often operate on data that’s hours or even days old. For use cases that require immediate action, like fraud detection or operational monitoring, that delay isn’t acceptable. Compute Over Data closes this gap by enabling analysis directly at the data source. Instead of waiting for a lengthy ETL process to load data into a central repository, you can run queries on fresh data as it’s generated. This gives you a real-time view of your operations, allowing you to power live dashboards and alerting systems that help you spot issues and opportunities the moment they happen. It’s a more agile way to handle your distributed data warehouse needs.

Powering Machine Learning and AI

Machine learning models thrive on data, but getting that data into a usable state is often the biggest hurdle. Your training datasets might be scattered across different cloud providers, on-premises servers, and geographic regions, making centralization a logistical nightmare. Compute Over Data simplifies this by letting you train models on distributed data without moving it. This approach not only accelerates the ML lifecycle but also enhances data privacy by keeping sensitive information within its required security or governance boundary. By decentralizing computation, you can run more experiments, iterate faster, and deploy more effective edge machine learning models that deliver timely and efficient results.

Solving for IoT and the Edge

The explosion of IoT devices has created a tidal wave of data generated far from the central cloud. From factory floors to remote infrastructure, sending every byte of sensor and telemetry data back for processing is impractical and expensive. It overwhelms networks and introduces latency that makes real-time control impossible. The Compute Over Data model is a natural fit for the edge. By processing data directly on or near the devices, you can perform immediate analysis, filtering, and aggregation. This allows you to respond instantly to events, like identifying a potential equipment failure, while only sending valuable summaries back to the cloud. This turns your IoT data into a real business asset for things like distributed fleet management.

Streamlining Log Processing and Observability

Log data is essential for security and performance monitoring, but its volume can easily lead to runaway costs. Many enterprises spend millions ingesting, indexing, and storing logs in centralized platforms like Splunk or Datadog, much of which is low-value or redundant. Compute Over Data offers a smarter approach to log processing. You can pre-process, filter, and enrich logs at the source, drastically reducing the amount of data you need to send downstream. This allows you to cut ingest and storage costs by 50-70% without losing critical visibility. You can run queries on raw data where it lives, making your observability pipelines more efficient, resilient, and affordable.

How Does This Approach Reduce Your Costs?

When you look at your cloud and data platform bills, it's easy to see how costs can spiral. Most of that expense comes from a single, traditional practice: moving massive volumes of raw data from where it’s created to a centralized location for processing. Compute Over Data flips this model on its head. Instead of bringing the data to the code, you bring the code to the data. This simple shift has a profound impact on your bottom line.

By processing data directly at its source—whether that’s in another cloud, an on-premise data center, or at the edge—you eliminate the most expensive and inefficient steps in your data pipeline. You’re no longer paying hefty fees to transfer terabytes of data just to filter or analyze it. This approach is why Expanso's platform helps enterprises save 40–80% on their data infrastructure costs. Let’s break down exactly where those savings come from. The reasons to choose Expanso often start with cost reduction but quickly expand to include greater speed and security.

Stop Paying for Expensive Data Transfers

One of the biggest hidden costs in any distributed environment is data egress—the fee cloud providers charge to move data out of their network. When your architecture requires you to centralize data for processing, you are constantly paying these fees. This gets even more complicated and expensive for global companies that need to move data across different regions or countries.

A Compute Over Data model allows you to process information right where it lives. This means you can analyze, filter, and transform large datasets at the source and only move the small, high-value results. You’re no longer shipping raw logs or entire databases across the internet. Instead, you’re transferring just the insights, which dramatically cuts down on transfer volumes and their associated costs. This is especially powerful for building a distributed data warehouse without the massive expense of data consolidation.

Optimize Storage and Compute Resources

Think about how much you spend on your central data platforms like Snowflake, Splunk, or Datadog. A significant portion of that cost is for ingesting and storing raw, often redundant, data. Much of this data may not even be useful for analysis, but you pay to move and store it anyway. By processing data upstream, you can clean, de-duplicate, and enrich it before it ever reaches your expensive centralized systems.

This pre-processing step means the data you send is leaner, cleaner, and ready for analysis. Your central platforms receive only the valuable information, which directly lowers your ingestion and storage bills. It also reduces the computational load on these systems, making queries faster and more efficient. This is a core principle behind effective log processing, where you can filter out the noise at the source and only forward critical events to your SIEM.

Lower Your Network Bandwidth Demands

Moving massive amounts of raw data doesn’t just cost money in egress fees; it also puts a huge strain on your network. For organizations with thousands of edge devices or distributed applications, sending all that telemetry and log data back to a central hub can saturate network links, leading to bottlenecks and pipeline fragility. This can slow down critical operations and delay time-to-insight.

By cutting 50-70% of data volume upstream, you dramatically reduce the burden on your network infrastructure. As our FAQs explain, this reduction in traffic frees up bandwidth, improves overall network performance, and makes your data pipelines more resilient. Instead of investing in more expensive network capacity to handle ever-growing data volumes, you can use your existing infrastructure more efficiently, saving money while improving reliability.

How to Handle Governance and Compliance

At first glance, a distributed architecture might seem like it complicates governance. With data spread across different locations, clouds, and even countries, how can you possibly keep track of it all? The reality is, a Compute Over Data model actually simplifies and strengthens your compliance posture. By processing data where it lives, you drastically reduce data movement, which is often the biggest source of security risks and regulatory headaches. Instead of pulling all your sensitive data into one place and hoping your perimeter security holds, you leave the data in its secure, compliant environment. This approach gives you a powerful framework for security and governance that’s built for the modern, distributed enterprise. It allows you to enforce rules and maintain control right at the source, ensuring that compliance isn't an afterthought but a core part of your data strategy.

Establish Clear Data Ownership

Effective data governance starts with knowing who is responsible for what. In many organizations, this is a major challenge because once data is moved into a central lake or warehouse, the lines of ownership can get blurry. A Compute Over Data architecture helps maintain clarity. When data stays within its original system or geographic location, ownership remains with the team that created and manages it. This structure reduces risk exposure and makes data more usable across the organization because the rules for access and use are clear. A well-defined governance program with unambiguous ownership roles is the foundation for building reliable analytics and AI that can drive real innovation.

Meet Regulatory Requirements (HIPAA, GDPR)

For any business operating in sectors like healthcare or finance, or across international borders, data residency is non-negotiable. Regulations like HIPAA and GDPR place strict rules on where sensitive data can be stored and processed. This is where Compute Over Data becomes a true game-changer. Instead of risking non-compliance by moving protected health information or personal data across borders for processing, you can send the computation to the data. This approach ensures you can run analytics on sensitive datasets while fully adhering to data sovereignty laws. It’s a critical capability for protecting privacy while still enabling the secure information sharing needed for a distributed data warehouse or other global operations.

Manage Data Lineage and Audit Trails

When a regulator comes knocking, you need to be able to show exactly what happened to your data, who accessed it, and how it was used. Manually tracking this in a complex environment is nearly impossible. A Compute Over Data platform can provide automated data lineage, creating a clear and immutable audit trail for every job. Because the platform orchestrates the work, it can log which computation ran, what specific data it accessed (without copying it), and where the results were sent. This overcomes a huge governance hurdle, making it much easier to prove compliance, increase data security, and ensure the integrity of your analytics from end to end.

Implement Robust Security and Privacy Controls

A core principle of modern security is to minimize your attack surface. Every time you move data, you create a new potential point of failure. By leaving data at rest in its secure source system, you inherently reduce risk. Compute Over Data architectures allow you to apply robust security and privacy controls—like data masking, filtering, or redaction—directly at the source. The computation only ever sees the data it’s explicitly allowed to see, ensuring sensitive information is never exposed. This method provides a far more granular and effective way to manage security, enabling better decision-making and improved patient or customer outcomes through data-driven solutions you can trust.

What Are the Common Implementation Challenges?

Adopting a Compute Over Data architecture is a significant shift, and like any major change, it comes with its own set of hurdles. While the benefits of processing data at its source are clear—lower costs, faster insights, and better compliance—getting there requires thoughtful planning and execution. The biggest challenges usually fall into three main categories: managing the technical learning curve, integrating with your existing infrastructure, and getting your teams on board with a new way of working. Facing these challenges head-on is the key to a smooth and successful transition. By understanding what to expect, you can build a strategy that addresses potential roadblocks before they slow you down, ensuring your organization can fully realize the value of this modern approach.

Addressing Technical Complexity and Skill Gaps

Let's be direct: distributed systems are more complex than their centralized counterparts. Moving from a model where all data flows to one place to a model where compute jobs run across many locations requires a new set of skills. Your team will need to be comfortable with concepts like distributed processing, containerization, and data locality. As organizations recognize the need for more specialized tools, they also find that their teams need to adapt. This isn't just about learning a new platform; it's about embracing a different architectural philosophy. You can prepare your team by investing in training and choosing a platform with excellent documentation and support. The goal is to equip your engineers with the knowledge they need to manage and troubleshoot a distributed environment effectively.

Solving Infrastructure Integration Hurdles

Your enterprise already has a complex web of technology, from cloud data lakes and on-premise servers to SIEMs and analytics platforms. A Compute Over Data solution can't exist in a vacuum; it has to integrate seamlessly with your current stack. The challenge lies in connecting these disconnected data sources without creating brittle, custom pipelines that are a nightmare to maintain. This is where concepts like data fabric and data mesh become relevant, as they aim to create a unified layer for data access. When evaluating platforms, look for open architecture solutions that work with the tools you already use. A platform designed for interoperability can connect to your existing infrastructure, preventing vendor lock-in and making the transition far less disruptive.

How to Overcome Adoption Barriers

The final hurdle is often more about people than technology. A successful data modernization strategy requires buy-in from stakeholders across the organization, including data engineers, security teams, and business leaders. To get everyone on board, you need to demonstrate value quickly. Instead of attempting a massive, all-at-once overhaul, start with a specific, high-impact use case. For example, you could focus on a log processing pipeline that’s driving up your SIEM costs. By showing a clear and immediate return on investment—like a 50% reduction in data transfer fees—you can build momentum and get the champions you need to scale the new architecture across other parts of the business.

How to Choose the Right Platform

Selecting a compute over data platform isn't just about picking new technology; it's a strategic decision that will shape your data infrastructure for years. In a traditional model, you’re constantly moving massive datasets to a central location for processing. This approach leads to soaring cloud egress fees, brittle data pipelines that require constant maintenance, and significant compliance risks when sensitive data crosses borders. The right platform flips this model on its head, bringing computation to the data to dramatically reduce costs and accelerate your analytics projects. The wrong one, however, can introduce unnecessary complexity and risk.

To make a confident choice, you need a clear evaluation framework. The key is to find a solution that aligns with your specific business goals, integrates with your existing environment, and meets your strict security and compliance standards. Start by defining what success looks like for your organization. Are you aiming to slash data transfer costs, process sensitive data in a specific jurisdiction, or enable real-time analytics at the edge? From there, you can dig into the specifics of security features and how well a platform plays with the tools your teams already use. Let’s walk through the three core areas you need to assess.

Define Your Enterprise Evaluation Criteria

Before you even look at a demo, you need to get clear on your internal needs. Every organization has unique data sources, workflows, and business objectives. As data engineering experts at Airbyte put it, you must "understand what your project needs and what limits you have." This clarity helps you map out where your data originates, where it needs to go, and what transformations are required along the way.

Start by creating a checklist. What are your primary goals? Is it cost reduction for log processing, faster model training for AI, or managing a distributed fleet of IoT devices? Define your technical requirements, such as supported data formats, processing speeds, and scalability needs. Getting this down on paper first ensures you evaluate every potential platform against a consistent, relevant set of standards that matter to your business.

Prioritize Security and Compliance Features

For any enterprise, especially those in regulated industries like finance or healthcare, security and compliance are non-negotiable. A compute over data architecture handles sensitive information across distributed environments, so governance can't be an afterthought. Your chosen platform must provide robust, built-in controls to protect data and meet regulatory demands. As IBM notes for industries like healthcare, "ensuring compliance with regulations like HIPAA and GDPR is...crucial for both protecting patient privacy and facilitating secure information-sharing."

Look for a platform with strong security and governance capabilities. Does it allow you to enforce data residency rules, ensuring data is processed within specific geographic boundaries? Can it manage data lineage, providing a clear audit trail for compliance checks? Features like data masking, access controls, and end-to-end encryption are essential for building a secure and trustworthy data pipeline.

Assess Integration and Vendor Support

A new platform should simplify your stack, not complicate it. The best compute over data solutions are designed to integrate seamlessly with your existing infrastructure, including data warehouses like Snowflake, observability tools like Datadog, and streaming platforms like Kafka. The goal is to avoid vendor lock-in and the high costs associated with ripping and replacing your current systems. A platform with an open architecture gives you the flexibility to adapt as your needs change.

When evaluating options, ask how the platform connects to your data sources and destinations. Does it offer pre-built connectors or a flexible API? Expanso, for example, is designed to bring computation to distributed data, allowing you to process information where it lives without expensive and slow data transfers. This approach not only saves money but also works with your existing tools, making adoption smoother and faster for your engineering teams.

Your First Steps with Compute Over Data

Adopting a new architecture can feel like a huge undertaking, but breaking it down into manageable steps makes the process straightforward. It all starts with understanding where you are now and where you want to go. By focusing on clear business outcomes, you can build a practical plan that delivers value quickly without disrupting your entire operation.

Step 1: Assess Your Needs and Plan Your Approach

Before you change anything, you need a clear picture of your current setup. Think of your data architecture as the blueprint for how your company collects, stores, and uses information. Is it helping you or holding you back? The goal is to turn raw data into a valuable resource, so start by asking what the business truly needs. What are your biggest pain points? Are you struggling with high data transfer costs, slow analytics, or compliance headaches in specific regions?

Understanding your project needs and limitations will help you map out where data is coming from, where it needs to go, and how it should be processed. This initial assessment is the foundation for a successful strategy.

Step 2: Build Your Roadmap and Migration Strategy

Once you know your goals, you can design a roadmap to get there. This plan should align directly with your business objectives, whether that’s reducing latency for real-time insights or cutting costs on your Splunk ingest bill. Instead of a massive, all-at-once overhaul, identify a single, high-impact use case to start with. For many, streamlining log processing or enabling edge machine learning provides a quick win.

A focused approach lets you demonstrate value early and build momentum. After all, companies that implement comprehensive data modernization strategies report up to 25% faster decision-making and 40% improved operational efficiency. Your roadmap is the bridge between your current challenges and future success.

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Frequently Asked Questions

Can I apply this model to my existing systems, or is it only for new projects? This is a great question, and the answer is that this approach is designed to work with the infrastructure you already have. It’s not a "rip and replace" solution. Instead, think of it as an optimization layer that makes your current tools—like Splunk, Snowflake, or Datadog—more efficient. You can use it to pre-process data before it ever reaches those platforms, which helps you cut down on ingest costs and pipeline strain without having to rebuild your entire stack from scratch.

How is Compute over Data different from edge computing? It's easy to see the overlap, but the two concepts aren't the same. Edge computing is a specific application of the broader Compute over Data strategy. The core idea of processing data at its source applies to many scenarios, not just IoT devices. You can use it to run jobs on data that lives in another cloud region or in a secure on-premise data center. So, while edge computing is a perfect use case, Compute over Data is the overarching architectural principle that makes it possible.

Doesn't sending code to where the data lives introduce new security risks? This is a critical point, and modern platforms are built with this concern in mind. The computational jobs are packaged in secure, isolated containers that strictly limit what they can do. These jobs are only given access to the specific data they need to perform their task and nothing more. In many ways, this is more secure than the traditional model, which requires you to move large, sensitive datasets across networks, creating more opportunities for exposure.

What kind of performance improvement can we realistically expect? The performance gains depend on your specific use case. The biggest factor is the time you save by not having to move massive amounts of data before you can even start your analysis. For analytics queries on distributed data, this can mean getting results in minutes instead of waiting hours for a data transfer to complete. For machine learning, it can significantly shorten model training cycles. The goal isn't just raw speed but also making your entire data workflow more efficient and responsive.

What is the most practical first step my team can take to get started? The best way to begin is to start small and focus on a clear pain point. Look for a single data pipeline that is notoriously expensive, slow, or unreliable. A common starting point for many organizations is their log processing workflow, where ingest and storage costs are often out of control. By targeting one specific area, you can demonstrate a quick win, measure the impact, and build the internal support you need to apply the model to other parts of your business.