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How to Extract Data Data extraction, as the name suggests, is the process of extracting or retrieving of data from multiple sources. Companies that store large amounts of data use data extraction techniques to condense information. The extracted data is then sent for further processing. The data is stored in a repository. It is processed through various algorithms. Hence, the original data goes through a complete transformation. Some of the steps in this transformative process include: Aggregating the data. For instance, the sum total of all customer service response received by the company This aggregated data is stored in a repository Adding metadata with vital information such as geo-locations or time stamps Combining the refined data with other datasets in the final data store. These steps are jointly known as ETL - Extraction, Transformation, and Loading. Extraction is the initial step that sets off this entire process. How is data extracted? To understand the process of data extraction, we must first assess the two main classifications of data. Structured data Structured data comes in the form of pre-defined data models. It is organized. All the key elements of the text data are distinct. The extraction process takes place within the source system. Unstructured Data Unstructured data is information that doesn’t come with pre-set data models. The information is unorganized. Typically, unstructured data sets contain a lot of text, dates, numbers, etc. – all mixed up with each other. Extracting Structured Data There are two main ways of extracting structured data - Full extraction – The required data set is entirely extracted from the source. There are no track changes. Although this process is straightforward, the system has to deal with a lot of load at one time. Incremental Extraction – Alterations in the source data are tracked and maintained throughout the process of incremental extraction. This process is meticulous and very complicated. Data engineers have been able to create data repositories where there are built-in functionalities to track data changes. Hence the system load in such processes is not as severe. Extracting Unstructured Data The main task of data engineers dealing with unstructured data is to prepare the data for easy and efficient extraction. Usually these experts store the data in a ‘data lake’ as they plan out their extraction process. All unnecessary information is removed from the data set. Some of these unnecessary pieces of information include – whitespace, out of place symbols, replica results, etc. On top of that, engineers also have to make sure that there are no missing values in the final output. Physical extraction Experts may choose to physically extract data in case the source material has no strict restrictions against this process. Physical extraction is carried out using two mechanisms – the data is extracted either online, directly from the source system or from an offline dataset. Routine extraction processes involve the creation of such offline structures that contain all the original source objects. The data already has an accessible structure in the form of either archive logs or redo logs or was created in prior extraction routines. There’s no need to consider whether or not distributed transactions are processing original or prepared source objects. In online extraction processes, there is no direct access to the source system. Intermediate systems that store the data in a pre-defined manner need to be established. Logical extraction Logical Extraction is a ‘narrow’ technique of data extraction. In this process the software adopts a selective approach and is able to retrieve only particular data types. The software is programmed to feature certain data markers. For instance, logical extraction tools will only extract the customer names from a data set. This data is copied into a separate dataset. Logical Extraction is used to minimize the data load and discerningly pick out certain important portions of the data. Automated extraction Automated Extraction processes address common challenges of processing unstructured data. It is impossible to manually predict the location or nature of the required data elements. Like human minds, Automated Extraction programs search through countless amounts of various data sources to detect and extract the information that is required by the user. Several top companies have heavily invested in the field of automating their organization's data extraction process. These processes are fast, precise, and are not limited to any form of constraint. Neither do these processes require the preservation of specific templates or other pre-configuration settings. All kinds of data can be reviewed in these processes. Data extraction techniques The extraction technique a company chooses will depend on its source system, performance levels and commercial requirements. Designing and establishing an organization-specific extraction process is the most important task for data engineers. The source system may be an extremely complicated system and require engineers to manually extract the data a lot of times just to stay consistent with the data in-flow into the data warehouse. The commonly used data extraction techniques are – Logical Extraction – This technique consists of two approaches - full extraction or Incremental Extraction. Physical Extraction - This technique consists of two approaches - Online and Offline extraction. Data extraction use cases Companies need Data Extraction to correctly format and prepare their data before loading it into a data storage system. Data extraction helps businesses by - Establishing a historical context for the data collected by a business. Helps Business Intelligence teams make better data-based commercial decisions The data collected by companies receives much-needed context The aggregated data can help small businesses generate higher revenues. They can also save a lot of money that they would have otherwise spent on hiring industry-specific data experts. It also helps businesses create a common data repository that can be accessed and used by all members of the organization. Helps boost organizational productivity because no further attention to data management tasks is needed once a good system has been put in place. Conclusion In the past, data management was a technique that could only be applied to small volumes of data. Modern data scientists are rapidly eliminating all the limitations of data transportations from the source systems to data warehouses via even more effective tools and processes! Rosoka Software meets data extraction needs by offering a full range of NLP products designed with customers in mind. Customers can choose from our self-service product to extract key insights on-the-fly with no long term commitments; integrate our extraction engine directly into their production pipeline; or employ our turnkey solution to take advantage of Rosoka's full extraction abilities with built-in load distribution - all in a matter of minutes. Additionally, Rosoka fulfills the need for data scientists to tailor their extraction results with an easy-to-use desktop client. Contact us today to learn how Rosoka can help with data extraction.

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