Data-driven decision-making can help businesses upturn their productivity by almost 6%. However, barely 0.5% of the world’s data is analyzed and utilized. As a result, companies all across the globe are losing massive prospects just because they can’t access the required data at the right time.
That’s where database extraction can come handy. It helps you retrieve data and make the most of it for reaching well-informed business decisions.
In this blog post, we’ll explain what database extraction is and how it can help streamline your business intelligence and reporting. Plus, we’ll also go through some key challenges and commonly used techniques to extract information from the database.
What is Database Extraction?
Database extraction is a process of retrieving data from disparate databases. In most cases, companies extract data to process it further or to transfer it to another repository for storage, such as a data warehouse or a data lake.
For example, you may perform some calculations on the data (like aggregating sales information) and store the outcome in your data warehouse. This procedure is called ETL (Extract, Transform, and Load). And, extraction is the first important stage in ETL.
Benefits of Database Extraction
Database extraction helps recognize which information is most valuable for accomplishing your business objectives, driving the overall ETL process. You can extract useful info concealed within unstructured, semi-structured or structured data sources, like customer information.
Suppose your business is undergoing decay in profits owing to customer churn. You maintain a record that displays the list of all existing consumers and consumer churn status for every month. To investigate the drift in the churn rate, you’ll have to extract the rows with churn status and aggregate them. This info will help you determine whether or not you can retain your consumers and plan essential strategies (like refining your customer support services) to reduce the turnover rate.
Database Extraction Challenges
The following are the key challenges of database extraction:
Data stored in relational databases is in a structured format, but NoSQL databases store unstructured data. Extracting data from the latter sources is challenging as it lacks basic structure. To make sense of this data, you have to format and standardize it.
Bringing incompatible data together is another huge challenge. For instance, suppose source A is a relational database with structured data (like phone numbers or ZIP codes), while source B has unstructured data enclosed in text files or word-based messages.
Now, if you are extracting data for further processing, you’ll have to execute ETL so that data from both sources become compatible. Only then you’ll be able to analyze and generate value out of it.
Quality and Security of Data
Preserving the quality and security of data is also challenging. Raw data from several sources often have data quality problems like duplicate values, redundancy, or contradictory data. So, you have to cleanse and transform this data after extraction to make it accurate and reliable.
Your data may also include sensitive info like personal information of customers or any other private information. You’ll have to carefully extract this sensitive info and transfer it securely, such as by encrypting the data in transit.
Database Extraction Techniques
The database extraction technique you select depends on the source system, in addition to your business requirements in the target repository environment. The two most commonly used database extraction methods are:
The data is extracted completely from the source in its current condition by loading the source table in the target location as it is. Full extraction involves data transfer in high volume, and that increases the load on the source system.
Only the data that has updated or changed since a discrete past event is extracted. This past event could be the last successful extraction or updated data. However, this technique has one big challenge. You need at least some info to recognize all the changed data since this specific past event to recognize the delta change. The source data itself can provide this info (like an application column that reveals the last-changed timestamp), or there could be a change in the table that offers this info by using an appropriate supplementary mechanism to track the variations besides the initiating transactions.
There’s a substantial volume of data that’s exchanged by businesses every single day. This makes manual database extraction a difficult job. Using a database extraction software can automate the extraction process and make critical business data accessible on time. Consequently, leading your company to make accurate decisions and simplify your operations.
Using Astera Centerprise, you can accomplish this in minutes that entire database extraction teams usually do in days and/or hours. The solution automates the extraction of valuable data from structured, semi-structured or unstructured sources with features such as workflow orchestration, email/FTP/folder integration, in-built job scheduler, automatic name and address parsing, and auto-creation of database extraction patterns. Besides, the easy-to-use interface streamlines the database extraction process, letting business users construct extraction logic in an entirely code-free way.