Data Extraction Definition


What is Data Extraction? Definition and Examples - Talend

What is Data Extraction? Definition and Examples – Talend

We have access today to more data than ever before. The question is: how do we make the most of it? For many, the biggest challenge lies in finding a data integration tool that can manage and analyze many types of data from an ever-evolving array of sources. But before that data can be analyzed or used, it must first be extracted. In this article, we define the meaning of the term “data extraction” and examine the ETL process in detail to understand the critical role that extraction plays in the data integration process.
What is Data Extraction?
Data extraction is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured. Data extraction makes it possible to consolidate, process, and refine data so that it can be stored in a centralized location in order to be transformed. These locations may be on-site, cloud-based, or a hybrid of the two.
Data extraction is the first step in both ETL (extract, transform, load) and ELT (extract, load, transform) processes. ETL/ELT are themselves part of a complete data integration strategy.
Data Extraction and ETL
To put the importance of data extraction in context, it’s helpful to briefly consider the ETL process as a whole. In essence, ETL allows companies and organizations to 1) consolidate data from different sources into a centralized location and 2) assimilate different types of data into a common format. There are three steps in the ETL process:
Extraction: Data is taken from one or more sources or systems. The extraction locates and identifies relevant data, then prepares it for processing or transformation. Extraction allows many different kinds of data to be combined and ultimately mined for business intelligence.
Transformation: Once the data has been successfully extracted, it is ready to be refined. During the transformation phase, data is sorted, organized, and cleansed. For example, duplicate entries will be deleted, missing values removed or enriched, and audits will be performed to produce data that is reliable, consistent, and usable.
Loading: The transformed, high quality data is then delivered to a single, unified target location for storage and analysis.
The ETL process is used by companies and organizations in virtually every industry for many purposes. For example, GE Healthcare needed to pull many types of data from a range of local and cloud-native sources in order to streamline processes and support compliance efforts. Data extraction was made it possible to consolidate and integrate data related to patient care, healthcare providers, and insurance claims.
Similarly, retailers such as Office Depot may able to collect customer information through mobile apps, websites, and in-store transactions. But without a way to migrate and merge all of that data, it’s potential may be limited. Here again, data extraction is the key.
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Data Extraction without ETL
Can data extraction take place outside of ETL? The short answer is yes. However, it’s important to keep in mind the limitations of data extraction outside of a more complete data integration process. Raw data which is extracted but not transformed or loaded properly will likely be difficult to organize or analyze, and may be incompatible with newer programs and applications. As a result, the data may be useful for archival purposes, but little else. If you’re planning to move data from a legacy databases into a newer or cloud-native system, you’ll be better off extracting your data with a complete data integration tool.
Another consequence of extracting data as a stand alone process will be sacrificing efficiency, especially if you’re planning to execute the extraction manually. Hand-coding can be a painstaking process that is prone to errors and difficult to replicate across multiple extractions. In other words, the code itself may have to be rebuilt from scratch each time an extraction takes place.
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Benefits of Using an Extraction Tool
Companies and organizations in virtually every industry and sector will need to extract data at some point. For some, the need will arise when it’s time to upgrade legacy databases or transition to cloud-native storage. For others, the motive may be the desire to consolidate databases after a merger or acquisition. It’s also common for companies to want to streamline internal processes by merging data sources from different divisions or departments.
If the prospect of extracting data sounds like a daunting task, it doesn’t have to be. In fact, most companies and organizations now take advantage of data extraction tools to manage the extraction process from end-to-end. Using an ETL tool automates and simplifies the extraction process so that resources can be deployed toward other priorities. The benefits of using a data extraction tool include:
More control. Data extraction allows companies to migrate data from outside sources into their own databases. As a result, you can avoid having your data siloed by outdated applications or software licenses. It’s your data, and extraction let’s you do what you want with it.
Increased agility. As companies grow, they often find themselves working with different types of data in separate systems. Data extraction allows you to consolidate that information into a centralized system in order to unify multiple data sets.
Simplified sharing. For organizations who want to share some, but not all, of their data with external partners, data extraction can be an easy way to provide helpful but limited data access. Extraction also allows you to share data in a common, usable format.
Accuracy and precision. Manual processes and hand-coding increase opportunities for errors, and the requirements of entering, editing, and re-enter large volumes of data take their toll on data integrity. Data extraction automates processes to reduce errors and avoid time spent on resolving them.
Types of Data Extraction
Data extraction is a powerful and adaptable process that can help you gather many types of information relevant to your business. The first step in putting data extraction to work for you is to identify the kinds of data you’ll need. Types of data that are commonly extracted include:
Customer Data: This is the kind of data that helps businesses and organizations understand their customers and donors. It can include names, phone numbers, email addresses, unique identifying numbers, purchase histories, social media activity, and web searches, to name a few.
Financial Data: These types of metrics include sales numbers, purchasing costs, operating margins, and even your competitor’s prices. This type of data helps companies track performance, improve efficiencies, and plan strategically.
Use, Task, or Process Performance Data: This broad category of data includes information related to specific tasks or operations. For example, a retail company may seek information on its shipping logistics, or a hospital may want to monitor post-surgical outcomes or patient feedback.
Once you’ve decided on the type of information you want to access and analyze, the next steps are 1) figuring out where you can get it and 2) deciding where you want to store it. In most cases, that means moving data from one application, program, or server into another.
A typical migration might involve data from services such as SAP, Workday, Amazon Web Services, MySQL, SQL Server, JSON, SalesForce, Azure, or Google Cloud. These are some examples of widely used applications, but data from virtually any program, application, or server can be migrated.
Data Extraction in Motion
Ready to see how data extraction can solve real-world problems? Here’s how two organizations were able to streamline and organize their data to maximize its value.
Domino’s Big Data
Domino’s is the largest pizza company in the world, and one reason for that is the company’s ability to receive orders via a wide range of technologies, including smart phones, watches, TVs, and even social media. All of these channels generate enormous amounts of data, which Domino’s needs to integrate in order to produce insight into its global operations and customers’ preferences.
To consolidate all of these data sources, Domino’s uses a data management platform to manage its data from extraction to integration. Running on Domino’s own cloud-native servers, this system captures and collects data from point of sales systems, 26 supply chain centers, and through channels as varied as text messages, Twitter, Amazon Echo, and even the United States Postal Service. Their data management platform then cleans, enriches and stores data so that it can be easily accessed and used by multiple teams.
Advancing Education with Data Integration
Over 17, 000 students attend Newcastle University in the UK each year. That means the school generates 60 data flows across its various departments, divisions, and projects. In order to bring all that data into a single stream, Newcastle maintains an open-source architecture and a comprehensive data management platform to extract and process data from each source of origin. The result is a cost-effective and scalable solution that allows the university to direct more of its resources toward students, and spend less time and money monitoring its data integration process.
The Cloud, IoT, and The Future of Data Extraction
The emergence of cloud storage and cloud computing has had a major impact on the way companies and organizations manage their data. In addition to changes in data security, storage, and processing, the cloud has made the ETL process more efficient and adaptable than ever before. Companies are now able to access data from around the globe and process it in real-time, without having to maintain their own servers or data infrastructure. Through the use of hybrid and cloud-native data options, more companies are beginning to move data away from legacy on-site systems.
The Internet of Things (IoT) is also transforming the data landscape. In addition to cell phones, tablets, and computers, data is now being generated by wearables such as FitBit, automobiles, household appliances, and even medical devices. The result is an ever-increasing amount of data that can be used drive a company’s competitive edge, once the data has been extracted and transformed.
Data Extraction on Your Terms
You’ve made the effort to collect and store vast amounts of data, but if the data isn’t in a readily accessible format or location, you’re missing out on critical insights and business opportunities. And with more and more sources of data appearing every day, the problem won’t be solved without the right strategy and the right tools.
Talend Data Management Platform provides a comprehensive set of data tools including ETL, data integration, data quality, end-to-end monitoring, and security. Adaptable and efficient, Data Management takes the guesswork out of the entire integration process so you can extract your data when you need it to produce business insights when you want them. Deploy anywhere: on-site, hybrid, or cloud-native. Download a free trial today to see how easy it can be to extract your data on your terms.
What is Data Extraction? [ Tools & Techniques ]

What is Data Extraction? [ Tools & Techniques ]

Data extraction is the process of obtaining data from a database or SaaS platform so that it can be replicated to a destination — such as a data warehouse — designed to support online analytical processing (OLAP).
Data extraction is the first step in a data ingestion process called ETL — extract, transform, and load. The goal of ETL is to prepare data for analysis or business intelligence (BI).
Suppose an organization wants to monitor its reputation in the marketplace. It may have data from many sources, including online reviews, social media mentions, and online transactions. An ETL tool can extract data from these sources and load it into a data warehouse where it can be analyzed and mined for insights into brand perception.
Data extraction does not need to be a painful procedure. For you or for your database.
Extraction jobs may be scheduled, or analysts may extract data on demand as dictated by business needs and analysis goals. Data can be extracted in three primary ways:
Update notification
The easiest way to extract data from a source system is to have that system issue a notification when a record has been changed. Most databases provide a mechanism for this so that they can support database replication (change data capture or binary logs), and many SaaS applications provide webhooks, which offer conceptually similar functionality.
Some data sources are unable to provide notification that an update has occurred, but they are able to identify which records have been modified and provide an extract of those records. During subsequent ETL steps, the data extraction code needs to identify and propagate changes. One drawback of incremental extraction is that it may not be able to detect deleted records in source data, because there’s no way to see a record that’s no longer there.
The first time you replicate any source you have to do a full extraction, and some data sources have no way to identify data that has been changed, so reloading a whole table may be the only way to get data from that source. Because full extraction involves high data transfer volumes, which can put a load on the network, it’s not the best option if you can avoid it.
Whether the source is a database or a SaaS platform, the data extraction process involves the following steps:
Check for changes to the structure of the data, including the addition of new tables and columns. Changed data structures have to be dealt with programmatically.
Retrieve the target tables and fields from the records specified by the integration’s replication scheme.
Extract the appropriate data, if any.
Extracted data is loaded into a destination that serves as a platform for BI reporting, such as a cloud data warehouse like Amazon Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, or Google BigQuery. The load process needs to be specific to the destination.
API-specific challenges
While it may be possible to extract data from a database using SQL, the extraction process for SaaS products relies on each platform’s application programming interface (API). Working with APIs can be challenging:
APIs are different for every application.
Many APIs are not well documented. Even APIs from reputable, developer-friendly companies sometimes have poor documentation.
APIs change over time. For example, Facebook’s “move fast and break things” approach means the company frequently updates its reporting APIs – and Facebook doesn’t always notify API users in advance.
ETL: Build-your-own vs. cloud-first
In the past, developers would write their own ETL tools to extract and replicate data. This works fine when there is a single, or only a few, data sources.
However, when sources are more numerous or complex, this approach does not scale well. The more sources there are, the more likelihood that something will require maintenance. How does one deal with changing APIs? What happens when a source or destination changes its format? What if the script has an error that goes unnoticed, leading to decisions being made on bad data? It doesn’t take long for a simple script to become a maintenance headache.
Cloud-based ETL tools allow users to connect sources and destinations quickly without writing or maintaining code, and without worrying about other pitfalls that can compromise data extraction and loading. That in turn makes it easy to provide access to data to anyone who needs it for analytics, including executives, managers, and individual business units.
To reap the benefits of analytics and BI programs, you must understand the context of your data sources and destinations, and use the right tools. For popular data sources, there’s no reason to build a data extraction tool.
Stitch offers an easy-to-use ETL tool to replicate data from sources to destinations; it makes the job of getting data for analysis faster, easier, and more reliable, so that businesses can get the most out of their data analysis and BI programs.
Stitch makes it simple to extract data from more than 90 sources and move it to a target destination. Sign up for a free trial and get your data to its destination in minutes.
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What is Data Extraction? | Alooma

What is Data Extraction? | Alooma

Data extraction is a process that involves retrieval of data from various sources. Frequently, companies extract data in order to process it further, migrate the data to a data repository (such as a data warehouse or a data lake) or to further analyze it. It’s common to transform the data as a part of this process. For example, you might want to perform calculations on the data — such as aggregating sales data — and store those results in the data warehouse. If you are extracting the data to store it in a data warehouse, you might want to add additional metadata or enrich the data with timestamps or geolocation data. Finally, you likely want to combine the data with other data in the target data store. These processes, collectively, are called ETL, or Extraction, Transformation, and Loading. Extraction is the first key step in this process.
Structured data
If the data is structured, the data extraction process is generally performed within the source system. It’s common to perform data extraction using one of the following methods:
Full extraction. Data is completely extracted from the source, and there is no need to track changes. The logic is simpler, but the system load is greater.
Incremental extraction. Changes in the source data are tracked since the last successful extraction so that you do not go through the process of extracting all the data each time there is a change. To do this, you might create a change table to track changes, or check timestamps. Some data warehouses have change data capture (CDC) functionality built in. The logic for incremental extraction is more complex, but the system load is reduced.
Unstructured data
When you work with unstructured data, a large part of your task is to prepare the data in such a way that it can be extracted. Most likely, you will store it in a data lake until you plan to extract it for analysis or migration. You’ll probably want to clean up “noise” from your data by doing things like removing whitespace and symbols, removing duplicate results, and determining how to handle missing values.
Usually, you extract data in order to move it to another system or for data analysis (or both). If you intend to analyze it, you are likely performing ETL so that you can pull data from multiple sources and run analysis on it together. The challenge is ensuring that you can join the data from one source with the data from other sources so that they play well together. This can require a lot of planning, especially if you are bringing together data from structured and unstructured sources.
Another challenge with extracting data is security. Often some of your data contains sensitive information. It may, for example, contain PII (personally identifiable information), or other information that is highly regulated. You may need to remove this sensitive information as a part of the extraction, and you will also need to move all of your data securely. For example, you may want to encrypt the data in transit as a security measure.
Batch processing tools: Legacy data extraction tools consolidate your data in batches, typically during off-hours to minimize the impact of using large amounts of compute power. For closed, on-premise environments with a fairly homogeneous set of data sources, a batch extraction solution may be a good approach.
Open source tools: Open source tools can be a good fit for budget-limited applications, assuming the supporting infrastructure and knowledge is in place. Some vendors offer limited or “light” versions of their products as open source as well.
Cloud-based tools: Cloud-based tools are the latest generation of extraction products. Generally the focus is on the real time extraction of data as part of an ETL/ELT process and cloud-based tools excel in this area, helping take advantage of all the cloud has to offer for data storage and analysis. These tools also take the worry out of security and compliance as today’s cloud vendors continue to focus on these areas, removing the need for developing this expertise in-house.
How Alooma can help
Alooma can extract your data — all of it. Do you need to extract structured and unstructured data? Do you need to transform the data so it can be analyzed? Do you need to enrich the data as a part of the process? Alooma can work with just about any source, both structured and unstructured, and simplify the process of extraction. Alooma lets you perform transformations on the fly and even automatically detect schemas, so you can spend your time and energy on analysis. For example, Alooma supports pulling data from RDBMS and NoSQL sources. Alooma’s intelligent schema detection can handle any type of input, structured or otherwise.
Alooma can help you plan. Once you decide what data you want to extract, and the analysis you want to perform on it, our data experts can eliminate the guesswork from the planning, execution, and maintenance of your data pipeline.
Alooma is secure. Alooma is a cloud-based ETL platform that specializes in securely extracting, transforming, and loading your data. If, as a part of the extraction process, you need to remove sensitive information, Alooma can do this. Alooma encrypts data in motion and at rest, and is proudly 100% SOC 2 Type II, ISO27001, HIPAA, and GDPR compliant.
Are you ready to get the most from your data? Contact us to see how we can help!

Frequently Asked Questions about data extraction definition

What do you mean by data extraction?

Data extraction is the process of obtaining data from a database or SaaS platform so that it can be replicated to a destination — such as a data warehouse — designed to support online analytical processing (OLAP). Data extraction is the first step in a data ingestion process called ETL — extract, transform, and load.

What is data extraction example?

Data extraction defined It’s common to transform the data as a part of this process. For example, you might want to perform calculations on the data — such as aggregating sales data — and store those results in the data warehouse.Nov 21, 2018

What are the two types of data extraction?

Types of Data Extraction Tools In terms of Extraction Methods, there are two options – Logical and Physical. Logical Extraction also has two options – Full Extraction and Incremental Extraction. All data is extracted directly from the source system at once.May 26, 2020

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