The value of data within businesses continues to grow at exponential rates. Data can provide valuable context and unlock business-critical insights. As W. Edwards Demings said, “In God we trust, all others must bring data.” When used skillfully, data can change the game for businesses; it can provide soundness to decision-making where mere intuition or past experience may be flawed.
But how exactly is this done? How do businesses collect their data from all the various places that produce them, whether it is databases, excel spreadsheets, or SaaS sources like marketing tools, and place them within a destination that makes it valuable to be unlocked? And what are the different ways, or methods of data extraction, to accomplish this?
Extraction: The ‘E’ in ETL
In data warehousing, ETL refers to the three-stop process where data is combined from multiple data sources into a warehouse. This is done to use it for analytics, unlocking business insights through patterns and trends that can be gleaned from this data. The three steps under ETL are Extraction, Transformation, and Loading; hence, data extraction techniques in data warehousing fall under the first step in the ETL process.
Data is extracted from data sources like spreadsheets, SaaS sources, and databases using different data extraction tools and techniques. The relevant data needs to be identified and located and then extracted in a form that is ready for transformation.
After this, the data is cleaned and transformed into a format that is suitable for entering the warehouse. This may involve transformations like removing missing values, standardizing terms, or adjusting time zones.
In the final step, the data is then loaded into the warehouse, ready for the various activities to be performed on it.
Data Extraction in Data Warehouses
Data extraction is the process of crawling disparate data sources and hence retrieving data from them. It is generally done for the purpose of migrating the data to a destination that supports further data processing or analysis. This destination is often a data warehouse due to the advanced capabilities of data warehouses when it comes to storage, processing, and reporting.
The process of data extraction can be accomplished using different data extraction techniques. The choice of the data extraction technique depends on various factors like the required business outcomes, the type of data being extracted, the use case, etc.
The data being extracted can often fall into one of these categories-
- Operational data: This is data that, once extracted, can be analyzed to increase the operational efficiency of an organization and produce better processes.
- Customer data: Customer data from various sources can provide a more holistic view of the customer, allowing teams like marketing and sales to make more informed decisions about selling.
- Financial data: Financial decisions, comparisons, and analyses can be made on an organizational level by extracting financial data.
In this blog, let us explore these various data extraction methods and understand when they can be used.
Data Extraction Techniques/Methods
Before extraction, the data scientist or engineer often needs to decide upon the optimal data extraction strategy in order to ensure the desired outcomes.
Questions that may arise include-
- the load of data to be extracted,
- whether it needs to be extracted on a regularly updated basis,
- whether the data is outdated, etc.
- stage in the ETL process (initial load or maintenance of data)
Two broad considerations exist for data extraction; logical and physical extraction. Logical extraction is the preferred choice, but physical extraction may be necessary in the case of outdated source systems.
Logical data extraction is further subdivided into three data extraction methods.
Full extraction is when data is pulled in its entirety from the source system from which it is being extracted. It often needs to be done when the data is pulled from the source for the first time or when there is no need to anticipate periodic or future updates to the data. There is no associated logical information with this data, like timestamps, as there is no need to take care of source changes.
As full extraction can involve high volumes of data, it is not advisable to always perform it as it may place a load on the network. This is why an incremental extraction is usually preferred for all data extraction after the first initial load. The exception is when the source does not have any provisions for tracking data changes or updates.
In contrast to full extraction, incremental extraction is when the updated or changed data is extracted from the source system. It is a more complicated process than full extraction, as the extraction tool needs to identify the relevant data to be extracted.
In this, the extraction tool looks for any changes in the information from a particular time or date. It may also access this information from a special changing table. All of this is information that is typically provided by the source system, which means that the data engineer will need to add extraction logic to the source system before extraction is attempted.
Change Data Capture or other Update Notification
Instead of incremental extraction being done at regular intervals to extract updated data, some systems allow for the provision of providing updates whenever the data is changed. Change Data Capture (CDC) is one such system. This allows for real-time or near-real-time access and analysis of data, as the data can be extracted as soon as it is updated within the source system.
Online extraction means the extraction of data directly from the source system. The extraction tools need to connect either directly to the source system or to a transitional system that can access the data and store it as a copy or a near-exact, more structured copy. The transitional system may not even be physically distinct from the source system but would be a separate location where the data is transformed into a predefined format.
In some cases, online extraction is not possible. An example of this could be when the data is outdated. The data is staged outside the source system and then extracted from here. The data is either already structured or the structure is created through extraction routines.
In this article, you’ve learned about the different data extraction techniques, both logical as well as physical. Based on the circumstances that a data engineer is faced with, each of these methods might be picked in different circumstances. Reliable, near-real-time data extraction may become difficult as a business scales and the number of data sources climbs as well, increasing the manual work required. This is when a tool like Hevo Data becomes handy, with 200+ plug-and-play integrations which will allow you to set up data extraction in minutes.