As companies shift towards digital operations, data has become a critical aspect of business success. To leverage data for growth, it must first be collected and transformed into a format that’s fit for analysis. This is where “Data Extraction” comes in, serving as the starting point for the journey from data to insights.
Data extraction is the backbone of ETL (Extract, Transform, Load), a process that drives the data and analytics workflows of many organizations. It’s the most demanding stage of any data-related project, requiring careful planning and execution to ensure a smooth data pipeline. Factors like data sources, extraction methods, and the accuracy of extracted data all play a role in determining the success of the data extraction process.
In this blog, we’ll delve into the exciting world of data extraction, exploring how a properly designed and executed process can make the rest of the data pipeline more efficient and result-oriented. Get ready to learn how data extraction can drive business growth and bring your data insights to the next level.
Table of Contents
What is Data Extraction?
Data Extraction, Source: Opensourceforu
Data extraction is the process of collecting data from various sources for the purpose of transformation, storage, or feeding it to another system for subsequent analysis. Data extraction is also known as data collection as it involves gathering data from different sources such as web pages, emails, flat files, Relational Database Management Systems (RDBMS), documents, Portable Document Format (PDFs), scanned text, etc. The sources through which this data is extracted may be structured or unstructured.
With structured data, the data adheres to a specific form or schema, for example, a database table with clearly defined columns of a particular data type and values contained in rows. In contrast, unstructured data does not conform to any definite structure. As a result, it can be more tedious to extract data from unstructured sources such as free-form text, images, web pages, etc.
Nowadays, data is also being retrieved from recording/measuring devices like sensors and the Internet of Things (IoT) devices. All of this means that data extraction is now required at a cross-section of input sources, some of them at the edge of computing. Therefore, it is essential that any data extraction routine be both robust and capable of delivering consistent data to the next layer of the data pipeline toolchain.
What is The Need for Data Extraction
The importance of data extraction cannot be ignored as it is an integral part of the data workflow that transforms raw data into competitive insights that can have a real bearing on a company’s bottom line. Any successful data project first has to get the data portion of the project right as inaccurate or faulty data can only lead to inaccurate results regardless of how well-designed the data modeling techniques may be.
The process of data extraction generally shapes raw data that may be scattered and clumsy into a more useful, definite form that can be used for further processing. Data extraction opens up analytics and Business Intelligence tools to new sources of data through which information could be gleaned.
For example, without Data Extraction, data from web pages, social media feeds, video content, etc., will be inaccessible for further analysis. In today’s interconnected world, the data derived from online sources can be used to gain a competitive advantage through sentiment analysis, gauging user preferences, churn analysis, etc. Therefore, it means that any serious data operation has to fine-tune the data extraction component to maximize the chances of a favorable outcome.
Hevo is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 150+ data sources and will let you directly load data to your data warehouse. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully-automated solution to manage data in real-time and always have analysis-ready data.
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Let’s look at some salient features of Hevo:
- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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What Is Data Extraction in ETL?
A central data store like a cloud warehouse collects and stores information from one or more data sources using the Extract, Transform, and Load (ETL) process. Data extraction represents the first step in ETL, which is a tried and proven data paradigm for
- Extracting data from multiple sources using APIs or webhooks and staging it into files or relational databases.
- Transforming it into a format that’s suitable for reporting and analytics by enriching and validating the data, applying business rules, and enforcing consistency across all data fields.
- Loading the high-quality, transformed data into a target data store like a data lake or a data warehouse to make it available for other stakeholders for reporting and analysis.
Data Extraction in ETL, Source: Nix United
Organizations of all sizes and industries use the ETL approach for integrating their marketing, sales, and customer service applications, data services, and unstructured files.
A well-engineered ETL pipeline with an apposite data extraction process can provide novel business insights and ensure the completeness of information, helping stakeholders make decisions with clear information, and eliminating confusion from indefinite data.
Data Extraction vs Data Mining
It’s easy to confuse data extraction with data mining. Although these two terms are related, they refer to different processes with different goals.
| Data Extraction | Data Mining |
| Primary Use | Retrieve data efficiently from one or multiple data sources for storage or analysis. | Identify hidden patterns efficiently from large and existing data sets. |
| Operates On | Semi-structured and unstructured data sources. | Structured data sets. |
| Engineering Expertise Required | Minimal, because using the right ELT/ETL tool you can simplify the process. | High, since it involves knowledge of various techniques such as statistical analysis, machine learning, and artificial intelligence, along with other tools to extract useful information from data. |
| Methodology | Proven and definite. | Innovative and (often) experimental. |
| Operational Technologies Involved | ELT/ETL tool and a data store. | OLAP database systems, data warehouse, transformation tools, ML & AI systems. |
Data Extraction vs Data Mining, Source: Zapier
Challenges of Data Extraction
Even though data extraction is one of the most essential steps in the journey toward data analysis, it is not without its own challenges. Some of these include
Data Volume Management: Your data architecture is designed to handle a specific ingestion volume. If data extraction processes are created for small amounts of data, they may not function properly when dealing with larger quantities. When this happens, parallel extraction solutions may be necessary, but they can be challenging to engineer and maintain.
Data Source/API Constraints: Data sources vary and so do extractable fields. So it’s important to consider the limitations of your data sources when extracting data. For instance, some sources like APIs and webhooks may have restrictions on how much data can be extracted at once.
Synchronous Extraction: Your extraction scripts must run with precision, taking into account factors such as data latency, volume, source limitations, and validation. The symphony of extraction becomes a complex masterpiece when multiple architectural designs are utilized to cater to different business needs.
Prior Data Validation: Data validation can happen at the extraction stage or the transformation stage. If done during extraction, one should check for any missing or corrupted data, such as empty fields or nonsensical values.
Intensive Data Monitoring: To ensure the proper functioning of your data extraction system, it is important to monitor it on several levels, including resource allocation (e.g. computational power and memory), error detection (e.g. missing or corrupted data), and reliability (e.g. proper execution of extraction scripts).
Data Extraction Techniques
There are broadly two ways to extract data from heterogeneous sources: logical extraction and physical extraction. Both methods involve crawling and retrieving data, but they differ in how the data is collected and processed.
Logical Extraction involves extracting data from a database or other structured data source in a way that preserves the relationships and integrity of the data.
Logical data extraction typically uses a database management system’s (DBMS) query language or API to extract the data in a structured format that can be easily imported into another database or system. The extracted data will also retain the relationships and constraints that are defined in the source system’s schema, ensuring that the data is consistent and accurate.
It can be of three types:
- Full Extraction for pulling data in its entirety from the source system.
- Incremental Extraction for pulling updated or changed data from the source system.
- Source Driven Extraction (or CDC) for capturing and recording any changes made to a source at regular intervals.
Physical Extraction involves copying raw data files from a storage device without regard for the relationships between the data elements.
It can be of two types:
- Online Extraction, when extracting data directly from a live system while it is still in operation (real-time data replication).
- Offline extraction, when extracting data from a system that is not currently running (may not provide real-time data replication).
Explore data extraction techniques in more detail here- 2 Data Extraction Techniques Every Business Needs to Know.
Benefits of Data Extraction Tools
ETL/ELT tools that automate data extraction from disparate data sources can offer a lot of advantages to data engineers, scientists, and business analysts:
- Plug-and-Play Connectivity: Most data extraction tools like Hevo Data offer plug-and-play connectors to your most frequently used business applications. With a few clicks, you can connect your source and start ingesting data.
- Greater Sense of Control: Imagine the hassle of creating a new pipeline connection for each new data source, and fixing broken pipelines every time APIs change. With automated ETL/ELT data extraction tools, you can be worry-free, move fast, and spend time on high-value tasks.
- Economies of Scale: Scaling data extraction by creating parallel solutions is like wheels within wheels. In such cases, data extraction tools can be more cost-effective than manual data extraction.
- Easy Compliance: Data extraction tools can help organizations to comply with data governance regulations by allowing them to track and audit data changes.
Improving data quality, automating data collection, and making data-driven decisions can be made simpler through the use of these widely used ETL/ELT tools.
- Hevo Data: Experience effortless data flow with our no-code pipeline platform. Enjoy easy setup and over 150 connections, all backed by round-the-clock support at unbeatable prices.
Hevo has simplified a lot of our tasks. We have scrapped our entire manual data integration process and switched to automation. We use Hevo’s data pipeline scheduling, models, and auto-mapping features to seamlessly move our data to the destination warehouse. We flatten certain columns from our incoming data using the Python interface in Hevo and our risk team uses Models to write SQL queries to get the required data for reporting.
– Vivek Sharma, Data Engineering Lead, Slice
- Import.io: Extract web data at scale in a simple and efficient way, turning unstructured data into structured data ready for analysis.
- Octoparse: A visual web scraping tool with point and click interface that enables users to extract data from any dynamic website and save it in various formats like CSV, Excel, and more.
- Parsehub: Easily extract data from JavaScript and AJAX pages, search through forms, automate data collection processes, and integrate the collected data into various applications.
- OutWitHub: A powerful tool for everyone that offers an intuitive interface with sophisticated scraping functions and data structure recognition.
- Web Scraper: Another simple and powerful application to extract data from websites, automate data collection processes, and save the collected data in various formats like CSV, JSON, and more.
- Mailparser: An email parser that can extract data from your email, PDFs, DOC, DOCX, XLS, or CSV files and automatically import this data into Google Sheets.
For more information on the best available data extraction tools, visit 10 Best Data Extraction Tools.
Data Extraction Use Cases
Streamlining ERP Data Entry: How Alpine Industries Processes Thousands of Purchase Order PDFs
Data Extraction Use Case: Alpine
Alpine Industries, a leading manufacturer of solution-based products for commercial and institutional markets, faces a daily inundation of PDF documents such as purchase orders, invoices, shipment notifications, and backorder notifications. Previously, the team responsible for processing these documents had to manually read and enter the information into their ERP system, leading to time-consuming tasks like recording batch payments from large customers.
To overcome this challenge, Alpine Industries introduced a comprehensive data management platform powered by Google Cloud and Docparser, streamlining the entire data process from extraction to integration.
The platform allows for real-time updates of parsed invoices and accurate shipment tracking, enabling teams to easily access clean, enhanced data. This has significantly reduced the workload on customer service from hours to seconds—highlighting the importance of effective data extraction processes.
Red Caffeine: Making Lead Management Easy
Data Extraction Use Case: Redcaffeine
Red Caffeine, a growth consulting firm, assists businesses in enhancing their brand reputation and boosting sales through their diverse range of services like marketing strategy, brand development, website design, digital marketing, and advertising.
Customized solutions are offered to clients across different industries to help them reach their target audience and achieve growth. To provide these tailored solutions, Red Caffeine leverages multiple platforms and tactics for raising awareness, capturing interest, and managing leads.
The key to their success lies in the seamless integration of these groups through effective data extraction techniques. This ensures all components are aligned and working together harmoniously, making data extraction a critical aspect of their business.
Data Extraction FAQs
What is data extraction used for?
Data extraction is used to retrieve data from multiple sources like relational databases, SaaS applications, legacy systems, web pages, and unstructured data file formats(such as PDFs or text files) in order to analyze, manipulate, or store the information for various purposes.
What are the two types of data extraction?
Data extraction is divided into two categories: logical and physical. Logical extraction maintains the relationships and integrity of the data while extracting it from the source. Physical extraction, on the other hand, extracts the raw data as is from the source without considering the relationships.
Is SQL a data extraction technique?
SQL (Structured Query Language) is a popular language to extract data from relational databases. SQL allows you to query the data stored in a database and retrieve the desired information. This information can then be used to populate a data warehouse or for reporting, and analysis.
How extraction is done in ETL?
Extraction is the first step in the ETL (Extract, Transform, Load) process. It involves retrieving data from various sources such as databases, flat files, APIs, and housing information into a staging area for further transformation.
This process can be done manually or automated using software tools. In an automated process, the ETL/ELT tool connects to the data source and retrieves the data. It then performs the necessary transformations to convert the data into a format that can be loaded into the target database.
What are the various types of data extraction in ETL?
There are three main types of data extraction in ETL: full extraction, incremental stream extraction, and incremental batch extraction.
Full extraction involves extracting all the data from the source system and loading it into the target system. This process is typically used when the target system is being populated for the first time.
Incremental stream extraction involves extracting only the data that has changed since the last extraction. This process is used to keep the target system up-to-date and is more efficient than full extraction.
Incremental batch extraction involves extracting data in batches rather than all at once. This process is used when the volume of data is too large to be extracted in one go and must be broken down into smaller chunks.
Embracing the Future of Data Extraction: Trends and Possibilities
Affordable cloud storage and lightning fast computing is pushing more and more data extraction solutions to operate on the cloud. In the years to come, the cloud will continue to revolutionize data extraction by providing fast, secure and scalable access to data. We are seeing more and more companies adopting multi-cloud strategies with advanced data extraction capabilities to retrieve data from multiple sources, in real-time.
As the volume of unstructured data increases, more efficient methods of extracting and processing it will be developed. With growing number of sources, new data extraction techniques will be designed to ensure the protection of sensitive information while being extracted. AI and ML algorithms will play a significant role in automating and enhancing data extraction processes.
We’ll also see the increasing use of IoT devices will drive the growth of edge computing, which will in turn shape the future of data extraction by enabling the extraction of data from remote locations.
Conclusion
Data Extraction is a vast field as the amount of data being produced is increasing exponentially. Various tools in the market seek to address the challenges presented by Data Extraction. One of such tools is Hevo, which allows you to extract data from 150+ data sources, transform it into a form suitable for analysis and connect it to your data warehouse or directly to a Business Intelligence tool of your choice to perform the required analysis.
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