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Michal Wosk
Michal Wosk Author

Last updated on February 23, 2024

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Many organizations and companies are rapidly moving from managing only structured data sets to managing both  structured and unstructured data. This is due to the growth in the number of sources and data types, which are rooted in the new variety of use cases that this data is used for. From advanced computer vision algorithms for autonomous vehicles to real time analysis of sensor data or medical data, to advanced analysis of financial and customer data. Image data, streaming data, sensor data, video data and more – are becoming increasingly common. Therefore they require proper handling, together with the structured tables of analytics data.

From structured to unstructured data – automotive industry example

The rise of the Internet of Things (IoT) has led to an explosion of unstructured data in the automotive industry. Cars manufactures equip cars today with a wide range of sensors and devices that collect data on everything from engine performance to driving habits. This data is often unstructured, meaning it is not organized in a specific way and can include images, videos, and text.

Car companies are increasingly relying on this unstructured data to gain insights into customer behavior and preferences, improve product design and performance, and enhance the overall customer experience. For example, developing algorithms for connected and self-driving vehicles requires a combination of sensor data, image, video, graph data and more. All of these are unstructured data by nature and cannot be stored and accessed in a structured data storage.

What is structured data?

Structured data refers to data that is organized into a specific format or structure, making it easy to store, access, and analyze. This type of data is typically stored in a database or data warehouse and has a clear and defined schema or set of rules for how the data is organized.

It is fairly easy to transform structured data into numerical data. Then it can be relatively easily used to train and evaluate machine learning models.

For example, structured data includes:

  1. Tabular data: data that is organized in rows and columns, such as a spreadsheet or a database table.
  2. Relational data: This is data organized in tables with relationships between them. Relational data is used in transactional systems, such as e-commerce websites, banking systems, and customer relationship management (CRM) systems.
  3. Time series data: This is data that is collected over time and organized in chronological order. Time series data is used in forecasting and trend analysis, such as predicting stock prices or weather patterns.
  4. Graph data: Data that is represented in a graph or network structure, with nodes and edges connecting different data points. Graph data is used in social networks, recommendation engines, and fraud detection.
  5. Spatial data: This is data related to geographic locations, such as addresses, GPS coordinates, and maps. Spatial data is used in logistics, urban planning, and real estate.

Structured data will most likely be stored in relational databases, in a table format. Other common storage formats for structured data include: XML, JSON and CSV file formats, as well as NoSQL databases, which can store structured data in a variety of formats, including key-value stores, document stores, and graph databases.

When is structured data most typically used?

Structured data is typically used to store data that can be organized into a well-defined schema or format. This includes data that has a clear and consistent structure, with defined attributes or fields that are shared across all records. Here are some examples: 

    1. Customer information which includes data such as name, address, email, phone number, and other contact details. 
    2. Product information which includes product name, SKU, price, description, and other attributes. Financial data such as transactions, account balances, and other financial records.
    3. Inventory data such as product stock levels, warehouse locations, and other inventory details. 
    4. Log data such as server logs, application logs, and other system logs. 
    5. Sensor data – data from sensors and other IoT devices, such as temperature, humidity, pressure, and other environmental data.




What is unstructured data?

Unstructured data refers to any data that does not have a predefined data model or organizational structure. It does not conform to a specific data model or schema, and it typically includes data that is not easily searchable or analyzed using traditional data processing techniques. 

Examples of unstructured data include:

    1. Textual data such as emails, social media posts, and documents in various formats like PDF, Word, and HTML.
    2. Multimedia data such as images, audio, and video files.
    3. Sensor data such as data from IoT devices, GPS, LiDAR and RFID.
    4. Streaming data such as data from social media feeds, online news sources, and web logs.
    5. Graph data such as data from social networks, recommendation systems, and web page links.





Unstructured data can be more challenging to manage and analyze compared to structured data, which follows a specific format and schema. However, unstructured data can also provide nearly unlimited opportunities for companies to develop advanced insights and algorithms.

When will you most likely use unstructured data?

There is a variety of use cases that require a broad use of unstructured data for algorithms and analytics development. Some examples include:

    1. Customer sentiment analysis: Unstructured data such as social media posts, product reviews, and customer feedback can be analyzed using natural language processing (NLP) techniques to understand customer sentiment and identify patterns and trends.
    2. Fraud detection: Unstructured data such as transaction records, email communications, and web logs can be analyzed using machine learning algorithms to identify patterns and anomalies that may indicate fraud or other suspicious activity.
    3. Content recommendation: Unstructured data such as user preferences, browsing history, and social media activity can be analyzed using machine learning algorithms to provide personalized content recommendations to users.
    4. Image and video analysis: Unstructured data such as images and videos can be analyzed using computer vision techniques to identify objects, people, and other features within them.
    5. Medical research: Unstructured data such as medical records, research papers, and clinical trial data can be analyzed using NLP and machine learning algorithms to identify patterns and trends that may lead to new insights and discoveries.





What are the challenges in managing unstructured and structured data concurrently?

Combining structured and unstructured data can be a challenge for organizations due to the inherent differences between the two types of data. Structured data is organized into a predefined format, such as a database table or XML file, and can be easily searched, sorted and analyzed. On the other hand, unstructured data, such as video, sensor data, emails, documents and multimedia content, lacks a predefined structure and is often difficult to search, organize and analyze.

The most common challenges in managing structured and unstructured data are the following:

Appropriate master data

Master data is crucial to establish a common language for objects, such as customers, materials, and products – within the organization. Without it, generating meaningful insights is impossible. Combining structured and unstructured data is challenging because unstructured data lacks structure and is hard to categorize and fit into a table or an excel sheet. Therefore creating master data that will reflect the entire data set requires accurate and consistent business goals appropriate to building and maintaining the master data.

Metadata extraction

By definition, unstructured data does not have a data structure. Therefore, unstructured data is hard to combine with structured data that sit in a relational database. Using metadata you can “wrap” some of the unstructured data files properties and enable integration of the structured and unstructured data. The fields and values that are consistent with the structured data can help link these disparate data sources. Extracting the right metadata accurately, storing it and managing it poses another challenge for the data engineers that are building the data sets.

Data transformations

In many cases, the metadata that wraps the unstructured data is not sufficient. For example, in video files, it is the content of the file that is required for further analysis. Therefore, data engineers need to use tools that ingest and transform the unstructured data to enable classification and extraction of properties of the data. These tools need to be reliable and accurate, in order to create the right transformations.

BI tools adaptation

Traditional BI tools are designed for structured data, and require unstructured data to be adapted in order to support it. For BI tools to support unstructured data, the right master data and metadata must be extracted from the unstructured data. Then it needs to be accurately managed and maintained.

The opportunity in combining structured and unstructured data

Combining structured and unstructured data has significant benefits. Companies possess a wealth of valuable information, but often encounter challenges due to inconsistent master data or the format of their data storage. Therefore, in order to drive innovation, it is necessary for companies across industries to create composite views. They can achieve this by establishing appropriate master data, applying it to all content, extracting metadata from unstructured data, and developing an analysis toolset that enables business users to access these insights throughout the organization.

When done right, the possibilities for innovations that will be driven by the data are endless: From autonomous driving, to real time financial analytics, to faster more innovative development of medical devices and treatments.

How can lakeFS help in managing structured and unstructured data

lakeFS is a data version control system for data lakes that can help better manage both structured and unstructured data. It provides a unified platform for managing the different types of data stored in a data lake. It also enables users to work with these data types using familiar Git-like commands and workflows.

Here are some specific ways in which lakeFS can help better manage structured and unstructured data:

Data versioning

lakeFS allows users to version their data, making it easier to track changes, collaborate on data projects, and ensure data integrity. This is particularly useful for unstructured data, which may change frequently and requires careful versioning to maintain consistency and accuracy.

Data lineage

lakeFS provides a comprehensive view of the lineage of different datasets, including their sources, transformations, and outputs. This can be particularly valuable for managing structured data, which often involves complex ETL (extract, transform, load) processes that can be difficult to track and manage.

Metadata management

lakeFS provides pre merge hooks for data, which are essentially tests that are triggered upon a predefined event. One such test that can be easily applied is a schema validator for metadata. For example, a webhook that reads new Parquet and ORC files to ensure they do or don’t contain a certain column or field. Another such test is format validator. This is a webhook that checks new files to ensure they are of a set of allowed data formats.

Data governance

lakeFS provides a centralized platform for managing data governance policies, such as access control, auditing, and compliance. This can help ensure that data is properly secured, tracked, and managed, regardless of its format or location.

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