Structured data and unstructured data are two broad categories of collectible data. Structured data is data that fits neatly into data tables and includes discrete data types such as numbers, short text, and dates. Unstructured data doesn’t fit neatly into a data table because its size or nature: for example, audio and video files and large text documents. Sometimes, numerical or textual data can be unstructured because modeling it as a table is inefficient. For example, sensor data is a constant stream of numerical values, but creating a table with two columns—timestamp and sensor value—would be inefficient and impractical. Both structured data and unstructured data are essential in modern analytics.","sortDate":"2023-12-15","headlineUrl":"https://aws.amazon.com/compare/the-difference-between-structured-data-and-unstructured-data/?trk=faq_card","id":"faq-hub#whats-the-difference-between-structured-data-and-unstructured-data","category":"Analytics","primaryCTA":"https://portal.aws.amazon.com/gp/aws/developer/registration/index.html?pg=compare_header","headline":"What’s the Difference Between Structured Data and Unstructured Data?"},"metadata":{"tags":[{"id":"GLOBAL#tech-category#analytics","name":"Analytics","namespaceId":"GLOBAL#tech-category","description":"Analytics","metadata":{}},{"id":"faq-hub#faq-type#compare","name":"compare","namespaceId":"faq-hub#faq-type","description":"
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Structured data and unstructured data are two broad categories of collectible data. Structured data is data that fits neatly into data tables and includes discrete data types such as numbers, short text, and dates. Unstructured data doesn’t fit neatly into a data table because its size or nature: for example, audio and video files and large text documents. Sometimes, numerical or textual data can be unstructured because modeling it as a table is inefficient. For example, sensor data is a constant stream of numerical values, but creating a table with two columns—timestamp and sensor value—would be inefficient and impractical. Both structured data and unstructured data are essential in modern analytics. \n Read about structured data","id":"seo-faq-pairs#whats-the-difference-between-structured-data-and-unstructured-data","customSort":"1"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#structured-vs-unstructured-data","name":"structured-vs-unstructured-data","namespaceId":"seo-faq-pairs#faq-collections","description":" structured-vs-unstructured-data","metadata":{}}]}},{"fields":{"faqQuestion":"Key differences: structured data vs. unstructured data","faqAnswer":" You can model structured data as a table with rows and columns. Each column has an attribute (such as time, location, and name), and each row is a single record with associated data values for each attribute. Unstructured data doesn’t follow any predetermined rules. \n The following are more differences between structured data and unstructured data. \n Structured data must always comply with a strict format, known as a predefined data model or schema. Unstructured data doesn’t fit a schema. The prescribed format of unstructured data might be as simple as requiring all meeting recordings to be in MP3 format, or that all system events must be collected in a certain store. \n Read about data modeling \n Both structured data and unstructured data can reside in various types of data stores. The choice of correct storage type depends on the inherent qualities and attributes of the data, the reason for collecting the data, and the types of analysis required. \n Examples of structured data stores include relational databases, spatial databases, and OLAP cubes. Large collections of structured data stores are called data warehouses. Examples of unstructured data stores include file systems, digital asset management (DAM) systems, content management systems (CMS), and version control systems. Large collections of unstructured data stores are called data lakes. \n Some data stores that you typically use for structured data can also store unstructured data and the other way around. \n Read about data stores \n Read about relational databases \n Read about data lakes \n Typically, it’s easier to organize, clean, search, and analyze structured data. When data is strictly formatted, you can use programming logic to search for and locate specific data entries, as well as create, delete, or edit entries. Automating data management and analysis of structured data is more efficient. \n Unstructured data doesn’t have predefined attributes, so it’s more difficult to search and organize. Typically, unstructured data requires complex algorithms to preprocess, manipulate, and analyze.","id":"seo-faq-pairs#key-differences-structured-data-vs-unstructured-data","customSort":"2"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#structured-vs-unstructured-data","name":"structured-vs-unstructured-data","namespaceId":"seo-faq-pairs#faq-collections","description":" structured-vs-unstructured-data","metadata":{}}]}},{"fields":{"faqQuestion":"Technologies: structured data vs. unstructured data","faqAnswer":" The type of technologies used with both structured data and unstructured data depend on the data storage type used. Typically, structured data stores offer in-database analytics, and unstructured data stores don’t. This is because structured data complies with known and repeatable rules for manipulation thanks to its format, and the format of unstructured data is more diverse and complex. \n There are various technologies used to analyze both types of data. Queries of the data using structured query language (SQL) is the fundamental basis of structured data analysis. You can apply other techniques and tools, such as data visualization and modeling, programmatic manipulation, and machine learning (ML). \n For unstructured data, analysis typically involves more complex programmatic manipulation and ML. You can access these analytics through various programming language libraries and specifically designed tools that use artificial intelligence (AI). Typically, unstructured data requires preprocessing so that it fits in a specific format. \n Read about SQL \n Read about data visualization \n Read about machine learning \n Read about artificial intelligence","id":"seo-faq-pairs#technologies-structured-data-vs-unstructured-data","customSort":"3"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#structured-vs-unstructured-data","name":"structured-vs-unstructured-data","namespaceId":"seo-faq-pairs#faq-collections","description":" structured-vs-unstructured-data","metadata":{}}]}},{"fields":{"faqQuestion":"Challenges: structured data vs. unstructured data","faqAnswer":" The challenges of using structured data are usually minimal compared to those of unstructured data. This is because computers, data structures, and programming languages can more easily understand structured data. Conversely, to understand and manage unstructured data, computer systems must first break it down into understandable data. \n In any complex organization or group, structured data becomes difficult to manage when the number of relations in a relational database grows significantly. With so many links between databases and data points, developing queries for the data can become quite complex. Other challenges include: \n Unstructured data typically poses two big challenges: \n Although you can do some analysis by using techniques such as keyword search and pattern matching, ML is often associated with unstructured data, such as image recognition and sentiment analysis. \n Other challenges can include: \n structured-vs-unstructured-data","metadata":{}}]}},{"fields":{"faqQuestion":"When to use: structured data vs. unstructured data","faqAnswer":" Both structured data and unstructured data are collected and used extensively across industries, organizations, and applications. The digital world runs on both forms of data, which is then analyzed and used in surfacing answers, decision-making processes, predictions, reflections, generative applications, and more. Although structured data is typically used for quantitative data and unstructured data is used for qualitative data, this isn’t always the case. \n Structured data is particularly useful when you’re dealing with discrete, numeric data. Examples of this type of data include financial operations, sales and marketing figures, and scientific modeling. You can also use structured data in any case where records with multiple, short-entry text, numeric, and enumerated fields are required, such as HR records, inventory listings, and housing data. \n Unstructured data is used when a record is required and the data won’t fit into a structured data format. Examples include video monitoring, company documents, and social media posts. You can also use unstructured data where it isn’t efficient to store the data in a structured format, such as Internet of Things (IoT) sensor data, computer system logs, and chat transcripts. \n Read about IoT \n Semi-structured data sits between structured data and unstructured data. For example, a store of videos might have associated structured data tags for each file, such as date, location, and topic. Metadata on multimedia files means that these are, by nature, semi-structured data. The blend of structured data and unstructured data types is what makes the data semi-structured. The use of semi-structured data instead of raw unstructured data can make analysis of the underlying unstructured data faster and easier.","id":"seo-faq-pairs#when-to-use-structured-data-vs-unstructured-data","customSort":"5"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#structured-vs-unstructured-data","name":"structured-vs-unstructured-data","namespaceId":"seo-faq-pairs#faq-collections","description":" structured-vs-unstructured-data","metadata":{}}]}},{"fields":{"faqQuestion":"Summary of differences: structured data vs. unstructured data","faqAnswer":"Data format \n
Data storage \n
Data analysis \n
Structured data \n
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Unstructured data \n
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Structured data \n
Unstructured data \n
Semi-structured data \n
