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What are the various techniques for data extraction?
Data is quickly becoming the digital economy's lifeblood, and as more organizations transition to online operations, the value of data is rapidly increasing. To be helpful, data must be collected and translated into a format that can be analyzed.
Data collection is the initial step in using data for corporate growth through analytics and Business Intelligence applications.
What Is Data Extraction?
Data extraction is the process of analyzing and crawling through data sources (such as databases) to recover vital information in a specific pattern. Data is processed further, including metadata and other data integration; this is another step in the data workflow.
Unstructured data sources and various data formats account for most data extraction. Tables, indexes, and analytics can all be used to store unstructured data.
Data in a warehouse can come from various places, and a data warehouse must use three different approaches to use it. Extraction, Transformation, and Loading are the terms for these procedures (ETL).
Data extraction entails retrieving information from disorganized data sources. The data extracts are subsequently imported into the relational Database's staging area. The source system is queried for data via application programming interfaces, and extraction logic is applied. Due to this process, the data is now ready to go through the transformation phase of the ETL process.
Why do we need Database Extraction?
Database extraction drives the overall ETL process by identifying the most relevant information for achieving your business objectives. Customer information, for example, can be extracted from unstructured, semi-structured, or structured data sources.
Assume your company's profits are declining due to client attrition. Every month, you keep a record that shows a list of all existing customers and their churn status. To study the churn rate drift, you'll need to extract and aggregate the data with churn status. This data can aid you in determining whether you can retain your customers and developing necessary measures (such as improving your customer service) to lower the turnover rate.
What Kinds of Data Extraction Are There?
Organizations extract two types of data in the broadest sense −
Unstructured data
Unstructured data isn't saved in a database in a standardized or structured format. Unstructured data generated by humans and machines are plentiful. Audio, email, geospatial, sensor, and surveillance data are all common examples, and they commonly come from the Internet of Things (IoT). Companies must first execute data preparation and cleaning operations such as removing duplicate results, deleting extra symbols, and establishing how to handle missing values before extracting unstructured data.
Structured data
Structured data is stored and managed in a transactional system in a standardized manner. Rows in a SQL database table represent structured data. Businesses usually extract the information from a source system when working with structured data.
Companies can extract a wide range of organized and unstructured data to suit their business objectives. However, the sorts of data retrieved usually fall into one of three categories:
Operational Information − Many organizations harvest data relating to everyday actions and procedures to understand outcomes better and increase operational efficiency.
Customer Information − Companies frequently gather consumer names, contact information, purchase histories, and other data for marketing and advertising purposes.
Financial Information − Companies can track performance and execute strategic planning by extracting sales numbers, purchasing expenses, and competitive prices.
Data extraction techniques
From a logical and physical standpoint, the projected amount of data to be extracted and the stage in the ETL process (initial load or data maintenance) may also influence how to extract. Essentially, you must decide how to conceptually and physically extract data.
Methods of Logical Extraction
Logic extraction can be divided into two types −
Full Extraction
The data is fully pulled from the source system. There's no need to keep track of data source changes because this Extraction reflects all of the information saved on the source system after the last successful Extraction.
The source data will be delivered in its current state, with no further logical information (such as timestamps) required on the source site. An export file of a specific table or a remote SQL query scanning the entire source table is two examples of full extractions.
Incremental Extraction
Only data that has changed since a particular occurrence in the past will be extracted at a given time. This event could be the end of the extraction process or a more complex business event such as the last day of a fiscal period's bookings. To detect this delta change, there must be a way to identify all the changed information since this precise time event.
This information can be provided by the source data itself, such as an application column indicating the last-changed timestamp, or by a changing table in which a separate mechanism keeps track of the modifications in addition to the originating transactions. Using the latter option, in most situations, entails adding extraction logic to the source system.
As part of the extraction process, many data warehouses do not apply any change-capture algorithms. Instead, full tables from source systems are extracted to the data warehouse or staging area, and these tables are compared to a previous source system extract to detect the changed data. Although this strategy may have little influence on the source systems, it strains the data warehouse procedures, especially if substantial data volumes are.
Methods of Physical Extraction
Physically extracting the data can be done in two ways, depending on the chosen logical extraction method and the source site's capabilities and limits. The data can be extracted online from the source system or offline from a database. An offline structure like this could already exist or be created by an extraction routine.
Physical Extraction can be done in the following ways −
Online Extraction
The information is taken directly from the source system. The extraction procedure can link directly to the source system to access the source tables or connect to an intermediate system to store the data in a predefined format (for example, snapshot logs or change tables). It's worth noting that the intermediary system doesn't have to be physically distinct from the source system.
It would be best to evaluate whether the distributed transactions use source objects or prepared source objects when using online extractions.
Offline Extraction
The data is staged intentionally outside the source system rather than extracted straight from it. The data was either created by an extraction method or already had a structure (redo logs, archive logs, or transportable tablespaces).
The following structures should be considered −
Flat files are files that have a predefined, generic format. For further processing, further information about the source item is required.
Oracle-specific format for dump files The containing items' information is included.
Logs for redoing and archiving
A separate, supplemental dump file contains the information.
Tablespaces that can be moved
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