Data Extraction vs Data Ingestion
Since the development of data processing, many terms have come to the limelight. Now, there are tens of words and expressions that define different processes within data management and data science. The number of these terms makes it possible to have similarities between many tools. Data extraction and data ingestion are examples of tools that can quickly be interchanged. With this article, you will learn what each term means in relation to data processing.
Data extraction is an integral part of data processing. It is used to collect data in different forms and from several sources. Similarly, once this data is gathered, it is pushed into a storage system, which can be local or cloud-based. In general, data extraction involves everything except that analysis or processing. The process of data analysis is not part of data extraction. Apart from data collection, data extraction also involves restructuring of unstructured data. This process is done after or before the data is stored. In addition, data restructuring is essential as it allows you to store data points considering similar variables. Likewise, information is gotten from several files with different formats. The type of sources of data can include websites, text folders, emails, PDF documents, and other forms of files. Most of these files are mostly located on the internet. If you intend to extract data from different sources, it is critical you provide limiters as variables and parameters. Using these limiters help you to include and exclude the type of information you collect.
Data ingestion is a process that can be considered part of data extraction. It can be described as a system that transports information from diverse locations into a storage system. It is quite similar to data extraction. However, data ingestion is not part of the ETL (extract, transform, and load) scheme. At the center of any analytical system is data ingestion. It gives analytic and reporting systems access to information continuously and consistently. Data ingestion occurs in different ways. The type of data ingestion used depends on the system model. In general, three main types of data ingestion methods are used. These methods are:
- Real-time data ingestion;
- Batch data ingestion;
- Lambda (Combined real time and batch).
The method of data ingestion you use depends on what you want to achieve.
As the name implies, real-time ingestion of data involves the collection and transfer of data to a storage in real-time. This method consistently works to move data from one location to another for the purpose of analysis. As a result, it is used for really essential data processing needs that cannot be delayed.
The batch data ingestion method collects and transfers data from source to destination in batches. The process works based on a predetermined schedule or a trigger event.
The lambda approach is a bit more sophisticated. It combines both the batch and real-time data ingestion method. This type of process is used when data ingestion process is divided into sections within a given system.
Other useful articles:
- How to Extract Data from PDF
- Data Visualization
- Data Analysis
- Web Data Extraction
- Data Labeling
- Data Portability
- Brief Introduction of PDF Extractor SDK
- History of PDF
- Data Extraction Techniques
- Using Google Analytics for Data Extraction
- Data Extraction from PDF
- Data Extraction Software
- Using Python for Data Extraction from PDFs
- Web Scraping Tools to Save Time on Data Extraction
- Data Extraction Use Cases in Healthcare
- Data Extraction vs Data Mining
- Data Extraction and ETL
- TOP Questions about Data Extraction
- How Data Extraction Can Solve Real-World Problems
- Which Industries Use Data Extraction
- Types of Data Extraction
- Detailed Data Extraction Process
- Things to Consider Before Data Extraction
- What is an ETL Database
- How ETL is Done
- Is ETL Part of Data Science
- Who Works with ETL
- ETL vs ELT Use Cases
- Data Extraction Trends in 2022
- Data Extraction vs Data Cleaning
- What is ETL in SQL
- Data Extraction vs Data Collection
- Data Extraction vs Data Ingestion