Data Extraction Defined: Tools, Techniques, and Examples

One of the first steps of the data lifecycle is data extraction, which involves collecting data of various types from multiple sources for use in data processes. Because data sources are numerous and bring in data of various types and formats, data extraction helps collate, process, and validate this data before loading it into the target location. Hence, data extraction ensures the availability of only high-quality, usable, and relevant data for processing, storage, and analytics.

Let’s discuss data extraction, its importance in your data management workflow, types of data extracted, some forms of data extraction, and how StreamSets helps simplify your data extraction process via its data integration platform.

What Data Extraction Is and Why It Matters

Data extraction is the act of collecting various types of data from various sources for use in data processing. It acts as the first point for your data management workflow and is vital for the following reasons:

Eliminating data silos: Organizational data occurs in multiple fragments across departments, which can create data silos and limit access when needed. Data extraction helps make this data available for data analysis and business analysts by extracting, standardizing, and transforming this data into formats that are findable and usable across the organization.
Data analysis and improved decision-making: Data extraction is a vital component of every data integration strategy, which helps consolidate data into a centralized location to create a rich, robust dataset for your business needs like customer and marketing analytics, artificial intelligence, machine learning, and other advanced analytics.
Operational efficiency: Data extraction consolidates data from multiple sources and makes them readily available for use when needed, thus reducing time to access and improving the productivity and agility of operations.

The Extract in Extract, Transform, Load (ETL)

The extraction process is usually the first step in data integration, consolidating data into a single storage location for later use. It refers to collecting and retrieving relevant data (structured, semi-structured, or unstructured, among others) from multiple sources like the web, social media, CRM, and ERP systems via APIs, web scraping, integration toolsets, and other extraction methods before loading. 

Often, extraction is the first step in a data integration process. Other steps can include Transform and Load, leading to the now ubiquitous term ETL. Let’s explore the other letters in the acronym. 

Transformation: Data extracted from the first step undergoes processing. This processing may involve cleaning, removal of bad or duplicated data, authenticating, masking sensitive data, or performing audits to ensure privacy and compliance. This step helps improve data quality and accuracy. 

Load: Transformed data is then loaded into a target location, like a data warehouse, data lake, or other storage system. 

Data extraction makes most data-driven processes possible, driving most data management processes like migration, integration, and ingestion.

Why Data Extraction Is So Specialized

The advent of our digital landscape birthed Big Data, which created the need for a highly efficient and cost-saving means of processing data. Big data refers to high-volume, highly-varied data generated from multiple sources at a continuous rate. The sheer volume and varied data nature of big data make it challenging to identify the relevant data that’s best for your use cases and effective data management. 

Developers usually wrote scripts to execute this extraction process in the past. However, as the digital landscape evolves, writing and developing manual extraction tools that can handle the increased number of sources, the volume and complexity of data, and all of the various use cases for the data downstream becomes challenging to build and maintain.

Additionally, the benefits of data-driven insights drive most businesses to utilize channels that continuously extract data from multiple sources. These sources can now include edge computing devices like the Internet of Things (IoT), wearables, and other smart gadgets, which generate massive volumes of data in real-time and can be challenging to sustain. Therefore, your extraction tool must make provisions to seamlessly extract from these multiple sources, scale to increasing demand, and ensure data governance. Additionally, error checks must be present at different levels to ensure the extraction process proceeds with failure.

Types of Data To Extract

Data extraction can involve different types of data, including unstructured and structured data;

Structured data: Structured data has a defined schema. Examples of structured data are those found in relational databases, spreadsheets, and logs. Structured data can undergo either full or incremental data extraction.
Unstructured data: Unstructured data refers to data without a defined schema and can include data from web pages, emails, text, video, and photos. 

Examples of Data Extraction

Here are a few examples of data extraction:

Improving customer experience: An e-commerce organization that allows its users to place orders on their online stores via smartphones, tablets, computers, websites, and social media generates a ton of data daily. Assuming your data analyst wants to determine user shopping behavior to guide its next marketing campaign strategy, the first step is to access customer records like names, email addresses, purchase history, and social media behavior. This process may involve using a data extraction tool to extract and process the data from these sources automatically. Website and social media data are usually unstructured and lack a defined schema, while data from relational database systems are structured. Therefore, your extraction process must ensure schema validation to ensure your data is compatible with the target system before loading.
Student management: Universities with thousands of students each academic year manage a ton of data, from academics to housing, extracurriculars, and financial records. These data are scattered across multiple departments and in various formats. For example, admission letters and transcripts may be in pdf formats, while survey responses from social media may be unstructured or structured. Determining the university administrative spending per student will involve extracting and processing data from all records in every department.
Financial planning: Businesses planning their finances for a fiscal year would need to access metrics present in sales records, purchase costs, operational costs, and more to track their previous year’s performance and improve operational efficiencies.

Data Extraction Methods

Various data extraction methods exist, depending on the business need, volume, velocity, and data use case. These methods include:

Full extraction/Replication: This method is standard for populating a target system for the first time. It involves extracting and replicating all the data from a source’s system and loading it into the target system. Full extraction is usually logical and preserves all the relationships between the data. You could also save machine memory by using an offset parameter to perform an extraction that excludes specific data and extracts the rest.  
Incremental batch extraction: Unlike full extraction, incremental batch extraction splits the whole dataset into chunks and extracts and loads it into the target system in multiple batches. This method is used for massive datasets and reduces the network latency of applications during extraction.
Incremental stream extraction: This method is further divided into:

Change data capture: This method only extracts all the changed data since the last extraction and loads them into the target system. This method helps conserve computing and network resources needed during extraction and helps identify changed data in real-time.
Slowly changing dimensions: This method is common for extracting data to warehouses and involves updating the attributes for a given dimension. Slowly changing dimensions either overwrite the old value without keeping a record, add a new row for the new attribute while maintaining the old one, or create a new current value column in the existing record while keeping the original column. This extraction is valuable for attributes that change constantly. For example, HR departments in large organizations may need to update existing staff positions or adjust pay as an employee gets a promotion or changes department.
See how they both compare and contrast here.

Common Issues With Data Extraction

The complex digital landscape and increasing volume of data make data extraction challenging due to the following:

Technical complexity: The increasing volumes of data generated from edge devices like IoT devices and wearables, in addition to data from phones, tablets, and laptops, means data generated keeps growing. These multiple data sources are rarely in a standardized format and may contain low-quality data. Hence, your extraction process must validate data before extraction to ensure these sources are compatible with the target locations. Designing an extraction process that accounts for the multiple data sources, latency, and lack of standardized data can quickly become challenging to develop and maintain.
API constraints and inaccessibility: Apart from database extraction via SQL, another extraction method is via webhooks and APIs. However, APIs among SaaS applications vary, and some may contain paywalls, which limits accessibility to required data. Additionally, some APIs lack proper documentation, and others frequently update this documentation without informing users, which may restrict access during extraction.
Time and cost: Planning and executing data extraction is time-consuming and expensive. Your extraction process must consider data volume, schema changes, and multiple use cases before deciding on the appropriate tooling that guarantees data quality, security, and compliance, which can be costly. In addition, as your data sources and value keep growing, adopting data extraction tools with extensive functionality to service comes at an extra cost.
Ensuring data quality, security, and compliance: Data extracted usually contains redundant, bad, duplicated data or data with Personally Identifiable Information (PII). Your extraction process performs transformation tasks to clean, validate, authenticate, and audit this data to ensure that only high-quality data flows to the downstream processes. Also, your extraction tool must employ encryption and other security measures to mask PII and ensure no data leaks occur while data is in transit or at rest to ensure compliance with data security and privacy laws.
Need for intensive monitoring: An inefficient extraction process with errors leads to low-quality data for analysis and decision-making. Hence, incorporating extensive monitoring checks at different levels during your extraction to help examine the reliability and accuracy of your extraction process is vital.

How StreamSets Simplifies Data Extraction

Manual extraction is complex and challenging, especially with the current complicated digital landscape. However, thanks to data integration tools like StreamSets, you can easily extract and integrate your data with other sources. StreamSets uses its simple, low-code interface to automate your data extraction from multiple data sources to integrate with other sources for creating cloud data warehouses or data lakes.

StreamSets also ensure data quality and compliance for your extraction process. It uses schema-on-read technology to detect and respond to schema changes. StreamSets can also perform transformation tasks like deduplication and cleaning with out-of-the-box, drag-and-drop processors on your data to ensure only high-quality data in your pipelines. In addition, you can limit access to read and write to credentials, pipelines, and fragments according to the user roles, preserving governance.

StreamSets pipelines are reusable; hence, you don’t need to construct new pipelines for every new extraction, saving time and improving productivity for your engineers.

Frequently Asked Questions About Data Extraction

What is the difference between data extraction and data retrieval?

Data extraction involves collecting data from multiple sources for processing, to integrate it with other sources, or create a centralized storage repository. On the other hand, data retrieval involves retrieving data from a database management system via a user query. The end goal of data extraction varies, from processing, to analytics and more. Data retrieval usually serves an immediate need to display database data for use within an application and is often performed manually.

How do you extract data from different sources?

Extracting data from multiple sources occurs in different ways; for example, web scraping helps you extract user, product, and financial data off web pages; SQL helps extract data from database management systems, while APIs and webhooks enable data extraction from SaaS data integration tools like StreamSets.

Is data extraction the same as data mining?

No, they differ in their methods and the kind of data they deal with. Data extraction involves using extraction tools or programming languages to collect existing data from multiple sources. In contrast, data mining involves using statistical methods, ML and AL, to identify patterns and hidden behaviors in large datasets. Additionally, data extraction involves semi-structured and unstructured datasets and is a proven process, while data mining is often experimental and deals with structured datasets.

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