Data Extraction and ETL
Data extraction and ETL are two terms that are often interchanged. However, each refers to something slightly different. Nonetheless, there is data extraction within ETL.
Data extraction is a computer science process that allows for the collection and replication of data from a database before it can be used for other secondary activities. Such secondary activities include data processing and data analytics.
The process of data extraction is critical for the activities of most businesses and organizations. Different data extraction tools are used to collect data from various databases before it is processed. For example, a company may want to analyze all the feedback on their products that are available on the internet. Data extraction tools can be deployed over a wide range of platforms such as social media and forums. This data can then be used to draw conclusions.
Data extraction is the first critical step of the data processing tool ETL.
ETL stands for extract, transform, and load. It is the standard process of copying or duplicating data from one destination (database) to another. Typically, each destination or source has a different set of rules and processes that governs how it works. ELT is critical to the implementation of data warehousing, which involves reporting and analysis of data to make business decisions.
Most ETL systems ensure that the data that is collected from the source is represented in a standard and consistent quality so that the data will not produce different outcomes when processed. As a result, doing this ensures that the standards for ETL are always met.
The processes with ETL usually occur simultaneously. That is, since the data extraction process can take a long time, all three processes of extraction, transformation, and loading takes place at the same time. Therefore, as data extraction occurs, the piece of data that has been extracted gets transformed and loaded. This process continues without any stoppage until the last piece of data is extracted, transformed, and loaded.
The three processes of ETL are discussed below.
The extract stage in ETL is the first. This step is usually the most important. Here, data is extracted from the primary source. As a result, data must be extracted correctly so that the right interpretation can be made. The most commonly used formats for data-source are JSON, flat files, XML, and relational databases.
At the transform stage, a set of guidelines are used to prepare data before it is loaded. Therefore, data cleansing occurs at this stage. Data cleansing is a process that involves removing clutter and unwanted data points from a set of data. It is critical to use standard rules when cleansing data to ensure uniformity when data is collected elsewhere.
The load stage is when data is stored in a storage system. The data is stored in a data warehouse or a delimited flat file. However, protocols for the storage of data vary from one organization to another.
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