April 21, 2022
Data is the lifeblood of any digital company. The bigger your data repository, the more opportunities you have to drive growth and innovation. Data has an integral part in any business, be it marketing, sales, operations, you name it. Unstructured and structured data are two different types of data stored on a database system. The data is stored, retrieved, and used in different ways.
In our article, we’re going to cover:
Structured data is formatted in a certain way and follows specific guidelines. Structured data also adheres to predefined rules for formatting and labeling information. Usually, we store structured data in the relational database (RDBMS) table columns with a fixed structure. The following is an example of structured data.
You define a customer table with fields like First Name, Last Name, phone numbers, social security numbers. The columns have predefined data types and their length. We cannot store a string in the numeric column. Once you define a table schema, you cannot change it while inserting or updating the data. You need to modify the table schema in case of any additional column or data type modification. If you require additional fields or information, modify the schema and work on the modified data structure.
The following image is an example of structured data stored in rows and columns in a table.
Unstructured data does not contain a predefined schema structure or does not belong to a data model. Therefore, we cannot store them in relational databases. We can use non-relational databases such as MongoDB, Couchbase, Apache Cassandra, Redis, DocumentDB for storing unstructured data. The unstructured data might have internal structural elements, but it does not store information in a predefined schema table format. It allows dynamic data generation and storage. We can use non-relational databases such as MongoDB, Couchbase, Apache Cassandra, Redis, DocumentDB for storing unstructured data.
As per the recent report, 80% to 90% of enterprise data is unstructured. Therefore, it emphasizes the importance and criticality of working with unstructured data. Let's understand a few examples of unstructured data usage:
Structured data is highly specific in comparison to unstructured data. Structured data is stored in a predefined schema or format, whereas unstructured data is a conglomeration of many different types of information.
Structured data has a fixed schema and is referred to as organized data. The information can usually easily be searched for and processed in a database. However, if any information does not comply with the schema requirements, it fails to store in a database.
The unstructured data offers flexibility and scalability without defining a fixed schema before working with any document. It allows storing data in various formats. However, it is slightly challenging to work in comparison with Structured data.
The following table summarizes the difference between structured and unstructured data.
This section explores the advantages and disadvantages of structured and unstructured data.
We can have one more data type, i.e., Semi-Structured data. The Semi-structured data does not conform to a specific data model. However, it has structural properties for quick data analysis. It can be considered as a combined version of Structured and Unstructured Data.
The following image shows semi-structured data that contains student records in JSON format.
The data conversion process is time-consuming and requires experience resources. It might involve the following phases.
The data conversion might use the machine learning models with the Python, R services, or third-party tools such as Azure Data factory, log parser tools, Cogito Semantic Technology, Zoho Analytics, SAS Viya, TextMiner, RapidMiner.
Among the tools that deal with structured data, we can highlight Skyvia. It is a cloud-based platform and an excellent ETL tool that has advanced transformation functionality, unlike usual ELT approaches, offering only data copying.
Skyvia is a single solution for both ETL and Reverse ETL tasks, which can significantly reduce the developers' efforts. With Skyvia, you can replicate data into DWH to further analyze it through Power BI (analytics reports, visualization, etc). In addition to this, you can use the Reverse ETL functionality, which returns the required actionable data back to the operational system.
Skyvia Replication and Skyvia Import can solve many cloud data integration tasks with structured data. You probably also heard about data pipelines. The difference between data pipelines and ETL is pretty well described in the article by Edwin Sanchez – What is a data pipeline?
Besides that, Skyvia also offers such an advanced data trasformation tool as Data Flow, which can be extremely helpful when complex, multistage data transformation and integration scenarios foreseen.
Among the tools that deal with unstructured data, we can highlight Apache Camel, Integrate.io, etc.
Data is at the heart of our businesses in today's digital world, whether a business professional or a consumer. Data is collected at every moment, and it forms the basis of our many decisions. In the future, data may take on a more significant role in our lives, but it will likely be used in new ways. Each organization includes structured, unstructured and semi-structured data. You might interchange data formats for data import, export or consume them in a standard format. I hope this blog is helpful and exciting!