From Spreadsheets to Data Lakes: Understanding Where Your Data Lives

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Most teams start managing data the same way: a few spreadsheets, a shared drive, a couple of people who “own” the numbers. It works for a while, then version conflicts, broken formulas, and slow reports start to creep in. At the same time, data volumes keep growing. Industry forecasts put annual global data creation well into the tens of zettabytes and climbing, with most of it generated in just the last few years.

At that point, terms like database, data warehouse, data lake, and more recently “lakehouse” enter the conversation. They sound similar, yet they solve different problems and carry different costs. Treating them as interchangeable is like treating a notebook, a filing cabinet, and a distribution center as the same thing because they all store paper.

In this post, we’ll explain the typical roles of spreadsheets, databases, warehouses, and lakes/lakehouses as your data grows, and how they fit together so you invest in the right structure at the right time.

Spreadsheets are excellent for early experiments and small workflows. They are flexible, fast to set up, and almost everyone knows how to use them. They are ideal when a single person or a very small team is exploring an idea, building a quick model, or testing a metric.

The risk shows up when a spreadsheet quietly turns into the system of record for important data. Studies of real-world business spreadsheets, especially in finance and accounting, routinely find a high percentage with material errors in formulas or data entry. Once decisions rely on a single workbook, the odds start to stack against accuracy.

Spreadsheets also lack most of the safeguards that proper data stores provide:

• • No enforced schema or data types beyond simple cell formats
  • No referential integrity between related tables
  • Limited validation and almost no protection against accidental overwrites
  • Weak audit trails and permissions when files are copied or emailed around

They struggle when:

• • Many people need to work with the same data
  • You need audit trails and clear, role-based permissions
  • Data volumes grow into millions of rows
  • Several systems must stay in sync reliably

Most successful data systems begin life in a spreadsheet. The key is recognizing when that stage has ended and a system with real structure, constraints, and governance is needed.

Operational databases handle the day-to-day work of software. Signing up in an app, placing an order, updating a profile, issuing a refund—each of these reads from and writes to a database.

Relational databases such as PostgreSQL and MariaDB store data in structured tables with defined columns, types, and constraints. They typically use normalization, keys, and indexes to keep data consistent and fast to access. Non-relational stores, often called NoSQL, such as MongoDB, handle more flexible document-shaped data. Specialized systems, like graph databases and vector databases, support relationship-heavy data or AI workloads.

The common thread is that operational databases:

• • Enforce data integrity through constraints and transactional (ACID) guarantees
  • Are tuned for large numbers of small, predictable reads and writes
  • Serve as the source of truth for specific applications or services

From a business point of view, databases have become basic infrastructure. Analysts estimate the database management system market at well over one hundred billion dollars annually and still growing. That level of investment reflects a simple fact: nearly every digital process writes to a database somewhere.

Operational databases focus on transactions. They are primarily tuned for a steady stream of small, low-latency operations, not arbitrary heavy, long-running queries across years of history. Modern systems can offload heavier analytics to read replicas or hybrid OLTP/OLAP (“HTAP”) features, but using the same production database for unrestricted analytics and critical transactions will eventually create contention and performance risk.

A data warehouse is a central store for structured data that exists to support analysis rather than live transactions. It answers questions such as:

• • Performance over months and years
  • Trends across products, locations, or channels
  • Behavior of key customer segments

Where operational databases store data in the shape needed for applications, warehouses reshape it for analysis. A common pattern is to organize data into:

• • Fact tables for measurable events (orders, logins, page views, tickets)
  • Dimension tables for entities and attributes (customers, products, locations, time)

This “star schema” or related modeling approach makes it easier to define consistent metrics and slice them by many attributes without re-deriving logic in every report.

Pipelines move data from operational systems into the warehouse. In older designs, this was often ETL—extract, transform, then load. In many cloud setups today it is closer to ELT—extract and load raw data into the warehouse first, then transform it there using SQL-based tools. Either way, the result is a set of curated tables that are optimized for large, complex queries.

Cloud data warehouses like Snowflake, Google BigQuery, and Amazon Redshift now anchor many analytics stacks. They typically use columnar storage, parallel processing, and, in many cases, a separation of storage and compute so teams can scale query power independently from raw data volume. Industry reports place the global cloud data warehouse market in the multi‑billion dollar range, with double‑digit compound growth.

Organizations that move reporting into a well-modeled warehouse often see faster report cycles and more consistent metrics. Instead of many teams running their own slightly different spreadsheets, everyone works from a shared, documented data model.

Structurally, the warehouse separates operational workloads from analytical workloads. Production systems stay fast and predictable. Analysts and data teams work against a copy of the data that is designed for their style of questions and for high-volume scanning and aggregation.

A classic data lake is a storage layer, usually on object storage, that holds raw data in many formats. CSV exports, JSON, logs, sensor feeds, images, and more can all land in the same large store. The idea looks attractive: collect everything now, keep it in raw form, and open the door to advanced analytics and machine learning later.

In practice, many early data lake initiatives stall. Surveys and case studies often report high failure rates for large data lake programs. Common themes included unclear ownership, weak governance, poor documentation, and uncertainty about exactly what lives in the lake. Without structure and oversight, “data swamp” is an accurate description.

A minimum level of governance is required once data moves beyond simple spreadsheets. At its simplest, this means defined ownership for each dataset, documented schemas, and basic quality checks such as type validation and duplicate detection. Warehouses and lakehouse systems rely on a catalog that records what each table contains, who maintains it, and how often it is updated. Without this lightweight structure, analytical environments degrade quickly regardless of underlying technology.

Modern platforms respond to this by layering more structure on top of the lake. This is often called a lakehouse approach:

• • Raw data lands in object storage, usually in efficient columnar formats
  • A table layer adds schemas, transactions, and time travel on top of those files
  • A catalog tracks which tables exist, their owners, and how they should be used

The result is closer to a warehouse in behavior, but keeps the flexibility and low cost of storing many data types in a single underlying system.

Lakes and lakehouses tend to make sense when:

• • You have high data volumes in many formats (structured, semi-structured, and unstructured)
  • You need to retain detailed history cheaply for machine learning or data science
  • You are prepared to invest in governance, cataloging, and data quality checks

Very large enterprises with mature data engineering teams were early adopters, often combining lakes, warehouses, and strong governance processes. Increasingly, mid-sized organizations are using cloud lakehouse platforms for similar reasons. For many teams, though, a clean operational database and a well-run warehouse will provide more practical value than a vast, loosely managed data store.

A useful way to think about these tools is as a progression of responsibilities, not a strict ladder where everyone must end at a lake.

• • Spreadsheets support prototypes, experiments, and very small workflows
  • Operational databases become the backbone once several people or systems share the same data and you need real integrity guarantees
  • Data warehouses add value when teams need consistent reporting and long-term trend analysis across many systems
  • Data lakes and lakehouses make sense when you have large, mixed data sets, strong governance, and clear use cases that require access to raw or semi-structured data at scale

Many teams achieve better outcomes by strengthening databases and warehouses before considering a lake or lakehouse. Solid foundations—good schemas, clean pipelines, clear ownership, and tested metrics—beat ambitious but loosely defined platforms.

It is also normal for all of these to coexist:

• • Applications write to operational databases
  • Pipelines move curated subsets into a warehouse model for BI and reporting
  • Raw and enriched data land in a lake or lakehouse for data science, ML, and long-term storage

The goal is not a single “home” for all data, but clear roles and reliable movement between them.

Where data lives shapes how fast teams can work, how confident people feel in the numbers, and how safely sensitive information is handled. Spreadsheets, databases, warehouses, and lakes or lakehouses each solve different problems and are most effective when they play defined roles rather than competing for the same one.

Spreadsheets help people experiment and move quickly. Operational databases keep daily systems running and data consistent. Warehouses convert history into insight through curated, well-modeled tables. Lakes and lakehouses, in the places they fit well, extend what is possible with very large and varied data.

The most effective strategy usually starts with getting the basics right. A reliable operational database and a thoughtfully designed warehouse unlock more value than an oversized platform that no one fully understands. From there, adding a governed lake or lakehouse, where it is justified, becomes part of a long-term evolution rather than a one-time project.