Tableau vs. Sigma: The Battle Between Old BI And Real-Time Analytics

The report was supposed to answer a simple question. Instead, half the meeting is spent figuring out why numbers don’t match. Meanwhile, the leadership team waits and decisions are stalled until someone can figure out whose version is right. This is what happens when analytics runs on tools built for an era when data was small, slow, and siloed. Teams patch the gaps with exports, extracts, and workarounds, but the delays and confusion never really go away.

Tableau has long been the poster child for this problem because it was designed for a world where data lived on desktops, not cloud warehouses. To make Tableau work with cloud data, teams rely on static snapshots of data copied from the warehouse. The result is stale numbers, conflicting reports, and a growing mess of duplicated files that fuel version control nightmares. Decisions slow down when no one is sure if they’re looking at the latest numbers or when teams make choices based on outdated data without even realizing it.

A very different model has started replacing this. Sigma runs entirely in the cloud, querying live data straight from the warehouse. No extracts, exports, or outdated dashboards. Just a direct line to what’s current. That shift is changing how companies think about collaboration, agility, and accountability in every part of the business.

In this blog post, you’ll see how Tableau’s extract-based architecture stacks up against Sigma’s warehouse-native approach. More importantly, you’ll see why the difference between live data and copied data has become one of the most important questions facing data teams right now.

Note: This blog post is based on our Tableau vs. Sigma eBook. Download it for free.

The legacy burden: Why Tableau still relies on extracts

Tableau wasn’t built for the world most businesses operate in now. It originated from an era when data was stored on-premises, tightly controlled, and relatively small in volume compared to modern cloud warehouses. The platform’s architecture reflects that. Even as Tableau has shifted toward the cloud, its foundation remains unchanged. Instead of connecting directly to cloud data warehouses in real-time, Tableau relies on extracts as a workaround. An extract is exactly what it sounds like: a copy of your data, pulled out of the warehouse and saved somewhere else so Tableau can visualize it faster. This was an innovative solution back when bandwidth was limited and live queries were expensive or slow, but that trade-off hasn’t aged well.

Every time someone generates an extract, they’re creating a static snapshot of the data at that point in time. From that moment on, it’s disconnected from the source. If the data changes, even minutes later, the extract doesn’t know. Business decisions start drifting away from reality without anyone noticing until the numbers don’t line up. It doesn’t stop at freshness. Extract-based workflows multiply operational headaches. Teams often create multiple versions of the same dataset because different reports or dashboards need different slices of data.

Pretty soon, there’s no single source of truth. Storage costs creep up, and governance becomes harder to enforce because data is living in too many places, outside the secure perimeter of the warehouse. Performance becomes another problem in disguise. Tableau users often pre-aggregate, sample, or filter datasets before creating extracts to make dashboards load faster. That might solve the speed issue temporarily, but it limits what users can ask of their data. Instead of exploring any questions they have, users are boxed into the slice of data the dashboard was built for. Anything outside of that scope means requesting a new extract, a new dashboard, or a manual workaround.

These limitations are side effects of an architecture designed before cloud warehouses existed. Even with cloud-hosted versions of Tableau, the extract model still sits at the center, and it’s showing its age.

Limitations of extract-based workflows

The hidden costs of extract-based workflows are rarely reflected in a budget line item. They manifest in operational delays, decision paralysis, and the constant, low-grade chaos that teams learn to accept as normal.

Start with speed. Building dashboards on extracts is a workflow bottleneck. When an extract needs to be created, refreshed, or reconfigured, it interrupts the flow of questions and answers. What should be a five-minute exploration turns into an hours-long back-and-forth between business users and the data team. If the extract was sampled or filtered incorrectly, the process starts over.

Then there’s the issue of version control. Once a dataset is extracted, it’s no longer tethered to the warehouse. One person runs a snapshot for their report, and another team pulls their own version. Over time, small differences creep in. A filter applied here, a column dropped there, a refresh missed. The result is teams spending meetings arguing about numbers instead of acting on them.

Storage and compute costs also quietly escalate. Every extract represents an additional copy of data that resides outside the warehouse, consuming storage and compute resources in the BI tool or on servers. Multiply that by hundreds of dashboards, and the overhead becomes significant both financially and operationally. Data engineers spend more time managing extract pipelines than improving data models or delivering new insights.

Governance takes another hit. When data lives outside the warehouse, it escapes the security, lineage, and access controls designed to protect it. An extract saved to someone’s desktop or embedded in a dashboard inherits none of the warehouse’s protections. This opens the door to compliance risks, accidental exposure, and untraceable data changes.

These problems are baked into the extract-based mode,l and the more an organization grows, the heavier this burden becomes. It doesn’t scale; it fragments.

Why live data is a competitive advantage

Business doesn’t wait. Deadlines, customer demands, and shifting markets don’t pause while teams refresh dashboards or troubleshoot mismatched reports. The companies that move fastest are the ones with immediate, unquestionable access to the truth. Speed for speed’s sake isn’t the point. Working with live warehouse data is about removing friction between questions and answers. When every question leads to another extract request or a new dashboard build, momentum collapses. Analysts become bottlenecks, and business leaders get stuck making decisions based on partial or outdated snapshots, often without realizing it.

Live data collapses that lag between curiosity and action. When a metric spikes unexpectedly, teams can drill in immediately. Finance updates forecasts mid-quarter based on current actuals, the supply chain adjusts to inventory shifts as they occur, and marketing keeps a close eye on campaign performance and spend in real-time, making adjustments before minor issues become significant problems.

There’s also a trust factor that’s easy to underestimate until you’ve experienced it. When every department works from the same source, disputes over “whose dashboard is right” start to disappear. Teams stop second-guessing whether a number is accurate and start focusing on what it means. This improves decision-making and collaboration. Meetings shift from debating data to acting on it.

Sigma’s architecture enables this operational alignment without requiring teams to overhaul their workflows. Business users explore data in a familiar spreadsheet-style interface. Analysts, engineers, and stakeholders all interact with the same live tables in the warehouse. There’s no risk of someone analyzing a stale file they downloaded last week or tweaking numbers in an offline spreadsheet that no one else can see.

For businesses competing in fast-moving markets, this shift is the difference between reacting a day late and being the first to move.

What does live data mean in Sigma?

Most BI platforms weren’t designed to work directly with cloud data warehouses. Sigma is the exception. Instead of pulling copies or slices of data into a separate layer, Sigma runs every query directly against the data warehouse. Nothing gets pulled out or saved in a separate data extract. What you see is always what exists in the warehouse at that moment.

This approach eliminates a long list of issues that have quietly become normalized in extract-based workflows. There are no stale datasets waiting to be manually refreshed. There’s no confusion over which version of a dashboard is correct. When a metric changes in the warehouse, it changes everywhere instantly. Teams no longer waste time validating whether reports are accurate because the numbers are always current and consistent.

Sigma’s interface plays a massive role in making this accessible. Business teams aren’t forced to learn complex BI tooling or SQL to interact with warehouse data. The spreadsheet-like interface serves as a familiar bridge, allowing users to explore, pivot, filter, and analyze data without stepping outside their comfort zone. Yet, behind every cell, chart, or filter lies a live query that hits the warehouse directly.

There’s no hidden step where data gets exported or transformed into an extract behind the scenes. Sigma supports write-back capabilities. That means teams can input changes, such as forecast adjustments, scenario planning data, or manual overrides, and write them directly back to governed tables in the warehouse. This is integrated, operational analytics connected directly to the data infrastructure that the business already trusts.

The result is an entirely different relationship with data. Teams can ask and answer questions on demand. They can follow a train of thought deeper without waiting for someone to build a new dashboard. Finance can model scenarios mid-meeting, operations can check warehouse inventory in the moment it’s needed, and marketing can track campaign pacing against spend without waiting for tomorrow’s refresh. Data stops being something locked behind a request queue and becomes part of how teams work every hour of the day.

Use cases where live data makes all the difference

The gap between live data and extract-based reporting becomes apparent when examining how teams actually utilize analytics in their day-to-day operations. On paper, an overnight refresh might seem good enough. In practice, it’s not.

Take revenue tracking. Most leadership teams need an accurate, up-to-the-hour view of sales performance. With extract-based dashboards, that number reflects the data from yesterday, or the last time someone remembered to refresh it. Revenue targets are missed not because the team didn’t work hard, but because the decision-makers didn’t see the warning signs until it was too late.

Inventory and supply chain reporting suffer the same fate. When teams depend on snapshots, stockouts, overages, or bottlenecks become visible only after they’ve already caused disruption. By the time the dashboard catches up, the opportunity to adjust has passed. In industries like retail, manufacturing, or distribution, this isn’t an inconvenience. It’s a direct hit to margins.

Marketing teams feel it too. Campaign performance changes hour by hour. Ad spend needs to be paced against conversions, click-through rates, and pipeline contribution in near real time. Waiting until tomorrow’s report means overspending on ineffective ads or missing the chance to double down on what’s working. Marketers end up relying on disconnected spreadsheets or ad platform dashboards that show only part of the picture. The BI tool becomes less useful because it can’t keep up.

Then there are executive dashboards that the board looks at. These serve as the pulse of the business. When the data behind those dashboards is stale or inconsistent, leadership confidence suffers, and decisions are made based on inaccurate assumptions. Live data turns those dashboards into actual operating tools instead of static performance summaries.

These are the backbone of how modern businesses operate. The companies that have access to live, warehouse-connected data are simply faster, more accurate, and better aligned. Their teams aren’t wasting time reconciling reports or chasing down the “right” number. They’re acting in the moment the business demands it.

The future of BI is real-time and warehouse-native

The way companies approach business intelligence is shifting. The old model of copying data into extracts, pushing dashboards to business users, and hoping it’s accurate enough, is wearing thin. The warehouse has become the operational backbone for nearly every data-driven company. It holds historical records and operational data that flows by the minute. Yet too many BI tools still expect that data to be pulled out, shaped into extracts, and served up in static dashboards. This isn’t a technology limitation anymore; it’s an architectural mismatch.

Modern BI is about working directly with the warehouse as the source of truth. Sigma was built with that premise at its core. It works alongside the warehouse, and every query, every chart, every pivot runs against live data exactly as it exists in the warehouse. When the data changes, the analysis changes with it. Teams move faster, decisions are made with confidence, and governance improves because data remains secure, governed, and fully auditable within the warehouse. Collaboration gets easier when there’s no version control battle over which dashboard is right.

The companies that succeed are those that adapt to this shift. They’re operating with the best information available at the moment it matters. Sigma reflects this shift. It’s designed for organizations that understand the warehouse is no longer just a place to store data; it’s where work gets done.

The choice for data leaders is between two models of thinking. One is tied to the constraints of the past. The other aligned with the speed, flexibility, and accuracy that the business demands.