The amount of data available in the world at this point in time is enormous. With companies developing various new projects and analyzing new interpretations and developments with big data, it becomes critical to understand the best ways to analyze different structures of data. One of the widely available formats of data is semi-structured information. However, a lot of data analytics needs to be performed on semi-structured data.
A data warehouse is a significant component of business intelligence. In these large central repositories of integrated data, we can report, explore, and analyze data. Some of the best data warehouse tools also help in solving a majority of problems related to semi-structured analytics. In this article, we will explore what semi-structured data is and some of the best ways to handle their analytics.
Before we directly understand semi-structured data, let us understand the other two entities—structured and unstructured data. Structured data refers to the type of data that you can process, analyze, access, and store in one fixed format. One of the primary examples of such a use case would be in an employee’s database where you store fixed information about employees.
On the other hand, unstructured data is available in an unknown form. Unstructured data usually contains large amounts of information. When data analysts try to extract this information, there are several challenges they might face due to the complexity of handling unstructured data. A fantastic example of unstructured data is the type of information that is available in a CCTV recording. Most companies find difficulties while managing this type of data.
Finally, we might draw some conclusions on what type of data is present in semi-structured analytics. It contains a mixture of both structured and unstructured data. One of the best examples of human-generated semi-structured data is XML files that contain personal details in text format along with some type of metadata and tags.
Another example of machine-generated semi-structured data is satellite imagery for defense purposes. Handling these types of data is critical for the optimal performance of a majority of companies. In the next section of this article, let us discuss some of the best ways to solve semi-structured analytics.
Best Ways to Analyze Semi-structured Analytics
Now that we have a brief understanding of semi-structured data, it is crucial to understand how we can handle it effectively for managing numerous projects successfully. In this section, we will look at some of the best tools that are highly effective in handling semi-structured analytics.
One of the best tools that are currently available for most developers is the Firebolt warehouse set of tools. It offers a massive boost in performance for solving various tasks, including handling semi-structured data with a performance speed boost of 4x –6000x and more efficient computation compared to other similar data warehouse tools. The platform is simple and easy to use because it is a true cloud-native SaaS platform, and users don’t have to worry about server and hardware issues.
Firebolt offers one of the best, unparalleled price-performance ratios. In comparison to some of the other data warehouse platforms, Firebolt’s services come extremely cheap and with great performance. Their services can go as low as $1 per hour with a boost of 400x, which is much better than other services that charge around $20 for similar services with lesser performance. The computing cost is less than $1 per hour, and the storage cost is S3 list price.
2. IBM Db2 Warehouse
Another fantastic service that is provided by IBM cloud services is the IBM Db2 Warehouse service. This warehouse tool is an amazing option for managing semi-structured data analytics. The Db2 Warehouse service runs a client-managed, preconfigured data warehouse that usually runs in a private cloud platform. Hence, most of the user’s time and resources are saved. The IBM Db2 Warehouse service is highly flexible, offering multiple support components like built-in machine learning, automated scaling, built-in analytics, and SMP and MPP processing.
You can choose to use this platform to extract fresh and fast insights for managing semi-structured data analytics. The deployment of data analytics is also quite flexible with this cloud platform service. The pricing range of this platform is variable depending on your region of interest, but you can choose between either the developer plan (install on your laptop, virtual machine, or virtual private cloud) or an enterprise plan (install on enterprise-grade servers or a virtual private cloud as part of the IBM Cloud Pak® for Data platform).
Snowflake is one of the earlier interpretations of cloud-based data warehouse platforms used for data storage and computation of data analytics, including semi-structured data. It was one of the first companies to revive data warehousing services to compute numerous operations like handling semi-structured analytics, big data, and other cloud-based operations. It is available on most service-providing platforms, such as Amazon S3, Microsoft Azure, and Google Cloud Platform.
The Snowflake platform offers multiple services, such as management of multiple workloads like data analytics, data sharing, data lakes, data engineering, and more. The pricing for this platform varies depending on the region of location and the type of service you require. The general range of pricing can be quite expensive for certain services. The performance in comparison to some of the other data warehouse tools is not that effective. However, it is still considered a great option to compute on and perform semi-structured analytics.
The majority of data available in the real world is usually semi-structured data. We have information such as presentation files, word processing documents, social media information on Facebook or Twitter, emails with specific internal structure, and so much more, which contain information stored in a semi-structured data format.
In this article, we understood the types of data that are available to most companies and researchers. We then discussed some of the best ways to handle this type of semi-structured analytics using data warehouse tools.