Data extraction is the first stage in a data integration and interoperability data pipeline. Its chief concern is to pull the correct data from multiple sources into a staging area where it is transformed and prepared for loading into a data warehouse for downstream consumption. This process is more commonly known as the Extract, Transform, and Load (ETL) sequence, and is the general pattern for most data integration strategies. Other patterns, like Extract, Load, and Transform, variate the general ETL pattern and are used for specific use cases.
Definition of Data Extraction
Data extraction is the process of extracting relevant information from a variety of data sources, such as databases, spreadsheets, text documents, images, and web pages. The extracted data can then be used for a variety of purposes such as analytics, reporting, machine learning, and data integration.
Data extraction can be done manually, using tools like SQL or Excel, or it can be automated using specialized software or scripts. Depending on the complexity of the data source, different techniques and tools may be used to extract the data. For example, extracting data from a structured database table can be done using SQL queries, while extracting data from an unstructured text document may require natural language processing (NLP) techniques.
Data extraction is a critical step in the process of data warehousing, business intelligence, and analytics. Extracting data from various sources and consolidating them into a single location makes it easier to analyze and gain insights from the data.
Extract, Transform, and Load
ETL stands for Extract, Transform, and Load, and refers to the basic pattern for processing and integrating data together from multiple sources. This pattern is used in physical as well as virtual executions, and in batch processing and real-time processing. In general, ETL data flows is a term that can be interchanged with data pipeline, however, data pipelines entail more.
A data pipeline, in comparison to ETL, is the exact arrangement of components that link data sources with data targets.
For example, one pipeline may consist of multiple cloud, on-premise, and edge data sources, which pipe into a data transformation engine (or ETL tool) where specific ETL processes can be specified to modify incoming data, and then load that prepared data into a data warehouse.
Contrastingly, another pipeline may favor an ELT (Extract, Load, and Transform) pattern, which will be configured to ingest data, load that data into a data lake, then transform it at a later point. However, ETL is the more common approach rather than ELT, and so easily associated with data pipelines.
Types of data extraction
Data can be extracted from many sources, not limited to digital data, like text on hard copy documents can be read using Optical Character Recognition (OCR) scanners and converted to digital text. But among digital types of data extraction, the following are the common methods:
Structured data extraction: This type of extraction is used to extract data that is stored in a structured format, such as a database or spreadsheet. This type of data is often easy to extract because it is organized in a tabular format, with well-defined columns and rows.
Semi-structured data extraction: This type of extraction is used to extract data that is stored in a semi-structured format, such as XML or JSON. This type of data is more complex than structured data because it does not have a fixed schema, but it is still organized in a way that allows for automated extraction.
Unstructured data extraction: This type of extraction is used to extract data that is stored in an unstructured format, such as a text document or image. This type of data is the most difficult to extract because it does not have a fixed format and may require the use of natural language processing or machine learning techniques.
Web scraping: This is a specific type of data extraction that is used to extract data from websites. This process involves sending an HTTP request to a website, parsing the HTML or XML response, and extracting the desired data.
API data extraction: Some companies and organizations expose their data through Application Programming Interfaces (APIs), allowing for easy data extraction by making requests to the provided endpoint and getting the data in a structured format, like JSON or XML.
Data Extraction from Cloud: With the rise of cloud computing, more companies are storing their data on cloud-based platforms, like AWS, Azure and GCP. These platforms have their own set of APIs and connectors that allows for easy data extraction.
The Process of Data Extraction
The data extraction process typically follows the Extract, Transform, and Load pattern described above, which includes the following steps:
Identifying the source of the data: This includes determining where the data is located, such as a database, file system, or web service.
Connecting to the data source: This involves using a connector, driver, or API to connect to the data source and authenticate the connection.
Selecting the data to extract: This step involves specifying the data that needs to be extracted, such as a specific table, query, or set of files.
Transforming the data: This step involves cleaning, formatting, and transforming the data to make it usable for the target system or application. This can include tasks such as data mapping, data validation, and data normalization.
Loading the data: This step involves moving the data from the source system to the target system, such as a data warehouse or data lake. This can include tasks such as data loading, indexing, and partitioning.
Quality check: After the data is loaded, it's important to validate the data quality, by checking the completeness and integrity of the data.
This process can be automated by Extract, Transform, Load (ETL) tools, which allow you to schedule and automate the data extraction process.
Examples of Data Extraction
Some examples of data extraction include:
Web scraping: Automatically extracting data from websites using programs or scripts.
Text mining: Extracting structured information from unstructured text, such as extracting product information from customer reviews.
Data warehousing: Extracting data from multiple sources and storing it in a central location for analysis and reporting.
Business Intelligence: Extracting data from various systems and using it to inform business decisions.
Natural Language Processing: Extracting information from unstructured text and spoken language, such as extracting insights from customer feedback.
Image processing: Extracting information from images, such as identifying objects in a photo or detecting facial expressions in a video.
Database queries: Extracting specific data from a database using SQL or other query languages.
Benefits of Data Extraction
Data extraction is a necessary step in any Data Operations, the overarching benefit is to make all other data operations possible through the efficient extraction of source data. Depending on the tools employed, however, will determine the quality of that process.
Data extraction tools can streamline data extraction through automations, improve data quality by eliminating error prone manual processes, and ultimately add to a company's ability to make better informed decisions.
Reduces or Eliminates Manual Processes — Data extraction tools eliminate manual data entry, and emancipates time and reduces costs.
Improves Data Quality — By eliminating the possibility of data entry errors, data quality improves reliability and significantly.
Improves Decision Making — The results of data extraction are vital in a company’s understanding of their operations from an objective standpoint. Without the ability to measure, making informed decisions is hampered. But with quality data analysis, which begins with well defined data extraction processes and quality data sources, businesses can ensure they are aware of how their business is performing and subsequently make strategic decisions based on data-driven insights.
Data Extraction Tools
Data extraction tools are designed to gather data from a variety of data sources that may include structured, poorly structured, and unstructured data. These tools streamline the data extraction process and can output multiple formats but must work with some combination of data quality and data preparation software in order to output clean and organized data. Data extraction functionality is also found in data integration tools which are capable of creating a consolidated view of data from multiple sources. At the very minimum, data extraction tools must extract structured, poorly structured, and unstructured data, must be able to pull from multiple sources, and must be able to export to multiple readable formats.
The Cloud, Internet of Things (IoT) and data extraction
The innovative technologies of the Cloud and Internet of Things (IoT) have both had tremendous impact on how companies approach their DataOps. The cloud and its ability to rapid store data as well as scale resources has impacted how companies see their data. In particular, the value of data has been decoupled from the need to maintain infrastructure. It has also led to improved technologies of data streaming which are designed to extract data continuously.
It's good that the cloud has made data more manageable, because the advent of the Internet of Things (IoT) has ushered in an untold number of new data sources—the millions of small devices that are now connected together via the internet. And while the data extracted from these sources can increase the competitive edge of companies savvy enough to glean insights from it, it has also added, exponentially, to the volume of data being stored in the cloud and on-premise. The future of data extraction will center around greater ingestion of more complex data sources, like multimedia, and employ more Machine Learning and Artificial Intelligence in the process.