What Is Data Flattening?
Data flattening transforms complex, multi-dimensional, or hierarchical data structures into simpler, two-dimensional tabular formats. Put more simply, it takes stacked or grouped data and “lays it out flat” in a spreadsheet-like format that’s easier to work with. The plain text files are then stored in file systems rather than in databases or data warehouses.
Most commonly used in databases and data integration, data flattening is employed for:
· Tool and algorithm compatibility. Many data analysis tools, traditional machine learning (ML) algorithms, and spreadsheet software use tabular data. Each row represents an independent observation, and each column represents a specific feature or variable.
· Simplified analysis. Compared to complex nested structures, flat data is easier to sort, aggregate, filter, and query. For instance, when applied to semi-structured data files like JSON or XML, flattening makes it easier to extract values, join datasets, and run queries.
· Improved model performance in ML. Flattened data promotes better model performance and accuracy, as algorithms more easily capture the relationships between variables when they’re in a flat, digestible format.
· Efficiency. Flattening can make data more efficient to process and store, especially if the original structure is highly complex.
· Feature engineering. Flattening is often used alongside feature engineering, turning raw data into meaningful new features.
Because some information is usually lost during the flattening process, it’s standard procedure to store the raw files in secure locations so they can be processed and loaded again.
Why Flattening Data Matters in Modern Data Workflows
The proliferation of complex data structures and a growing reliance on data-driven decision-making has elevated flattening in modern data workflows. It helps make data easier to access, analyze, and integrate across systems.
Compatibility with Analytical and Machine Learning Tools
Most traditional analytical tools like Excel and SQL databases, as well as many ML algorithms, are designed to work with tabular data. They expect data to be structured into clear rows and columns; they cannot directly consume complex, nested formats like JSON or XML. Flattening enables integration by transforming complex data into the expected tabular format.
Handling Diverse and Semi-Structured Data
Today’s data environments contain massive volumes of semi-structured data, including JSON logs, social media inputs, API responses, and IoT sensor feeds, which often have nested arrays, objects, and inconsistent schemas. Flattening turns disorganized, variable data into structured formats that can be analyzed and understood. Without it, working with such data would be more time-consuming and introduce a greater risk of misinterpretation or inefficiency.
Improved Data Processing Efficiency and Performance
Flat data is easier to work with throughout a data pipeline. Getting useful insights from traditional databases, especially those split into many related tables, often means stitching data together with JOINs, which can slow things down. Flattening avoids slowdowns by combining the data into one big, easy-to-query table. In ML and deep learning, flattening also helps turn complex data, such as images or time series, into a simple list of numbers, which computers can process faster.
Enhanced Data Discoverability and Usability
Flat data is simply easier to read. With everything laid out in a simple table, analysts, data scientists, and other users can quickly understand a dataset’s structure and context. It also enables self-service analytics, where even non-technical users can explore and manipulate data using tools like Tableau and Power BI (or even a spreadsheet) without having to call in the data team every time they need something.
Scalability and Big Data
Big data frameworks excel at efficiently handling tabular data in distributed environments. Flattening ensures compatibility with these platforms, making it easier to scale data processing across massive datasets. Flattened data can also save storage space, particularly when stored in columnar formats that provide excellent compression and rapid query speeds for analytics.
Data flattening is a technical step and a strategic necessity in modern data workflows. It bridges the gap between diverse, complex raw data and the tabular format required by the powerful tools and algorithms that drive insights, predictions, and automated decision-making in today’s data-driven world.
Common Techniques to Flatten Out Complex Data Structures
Flattening complex data structures makes data easier to read, use, and feed into tools or models that expect a simpler layout.
One common technique turns data with nested elements, such as a JSON file with customers, orders, and products, into a flat table where each row shows a single combination, and each detail has its own column.
Another converts multi-dimensional arrays, like images, into one long vector so they can be used by certain types of neural networks that expect a flat input.
In relational databases, flattening frequently involves combining several linked tables into one larger, denormalized table for easier querying or reporting. JOIN operations are often used to combine data from several tables into one.
Use Cases for Data Flattening in Analytics and Integration
Examples of when flattening data is called for:
Use Case #1: Healthcare Analytics from JSON Medical Records
A health tech company collects patient intake data from digital forms that store responses in nested JSON format. Each form includes general information about the patient, a list of prior diagnoses, prior and current medications, and appointment history. The JSON structure must be flattened before feeding this data into a relational database for reporting and predictive analytics. Once it is, analysts can more efficiently track treatment trends, medication adherence, and patient outcomes across facilities.
Use Case #2: Image-Based Risk Detection in Insurance
An insurance company uses 2D and 3D satellite images and drone footage to assess property damage after a natural disaster. The images are flattened into 1D vectors before they’re fed into a neural network model for automated damage detection, enabling faster and more accurate claims processing and resource allocation.
Use Case #3: Financial Reporting in Business Intelligence
A retail bank maintains a highly normalized transactional database that logs everything from individual purchases to customer account changes. The information is flattened and denormalized into a centralized data mart to support executive dashboards and branch-level performance tracking. Keeping the database flat at this stage improves performance by reducing query complexity and enabling faster, more efficient reporting through business intelligence tools. Leadership gets real-time insights into revenue, customer behavior, and operational efficiency.
Challenges and Limitations of Flattening Data
Flattening data is often necessary for analysis and tool compatibility, but it also introduces several challenges.
Data redundancy is one such issue, especially with one-to-many relationships. For instance, flattening customer records with multiple orders means repeating the customer’s name and address over and over, increasing storage use and cost. This redundancy can also lead to data inconsistency if updates aren’t properly managed, adding complexity and risk of errors.
Another drawback is the loss of original structure and context. Because flattening removes nested relationships, it can be harder to understand how data points are connected. This is especially problematic for analyses relying on hierarchical data, like tracking event sequences or understanding parent-child relationships. Reconstructing the original structure from a flattened format is difficult without raw data or metadata, limiting analytical flexibility.
While data flattening improves query read performance, it can hurt write performance. Insert, update, and delete operations slow down due to duplicated data fields. The act of flattening itself can also be computationally demanding, particularly with large or deeply nested datasets like JSON or XML. Schema evolution adds another layer of difficulty. If the original nested structure changes, the flattening logic must be updated and the flattened table rebuilt, making pipelines fragile and harder to maintain.
Column exposure can also occur, with flattening introducing dozens or hundreds of columns, many of them sparsely populated, due to optional fields or arrays. This can make tables wide and inefficient to query. Flattening arrays can lead to row duplication, as each array item becomes its own row, further amplifying storage and consistency issues.
To address these limitations, many organizations take a hybrid approach to data flattening, storing raw semi-structured data in data lakes and maintaining flattened versions in data warehouses. Others turn to NoSQL databases that natively handle nested data or use specialized tools like Spark’s “explode” or Python’s json_normalize. Some flatten data only partially, focusing on high-value fields. At the same time, careful schema planning helps reduce redundancy and support long-term usability.
While flattening is a powerful tool, it’s not a universal solution. Data professionals must weigh its benefits against its potential drawbacks and choose the most appropriate data modeling and processing strategies for their specific use cases.
