One of the biggest trends of the 21st century is the massive surge in internet usage. With major innovations such as smart technology, social media, and online shopping sites, the internet has become an essential part of everyday life for a large portion of the population. Due to this internet surge, data usage and analytics have become a massively important industry. For both consumers and businesses alike, learning more about data analytics and usage can be highly beneficial. One important term to get familiar with is data ingestion.
What is data ingestion? What are the different types of data ingestion? Also, what other terminology is important when looking at data ingestion? What are some of the benefits of data ingestion?
Here is the DevX quick guide to data ingestion.
What is Data Ingestion?
Data ingestion is a somewhat complex process. It is the act of gathering large chunks of assorted data from various locations into a single location such as a data warehouse or data lake. The data transfers to this location by utilizing a data pipeline. Once the data is stored safely it can be used and analyzed by companies or other organizations. By analyzing this database these organizations can make more informed decisions for the future.
What are the Different Types of Data Ingestion?
Batch Ingestion
Batch data ingestion is done at scheduled intervals. These intervals and ingestions points can be based on specific time intervals or specific trigger events. Batch-based ingestion is best suited for markets that do not update or change constantly.
Real-Time Ingestion
Real-time data ingestion, as the name suggests, stores data in real time. Most real-time ingestion completes by using change data capture, or CDC. CDC solutions are constantly monitoring logs for data changes, and then moving these data changes to a single storage site. Unlike batch ingestion, real-time ingestion is great for organizations working in markets that change frequently or require rapid responses. Some examples of these quickly changing markets would include stock trading, social media, or power grid stations.
What Other Terminology is Important?
Data Pipeline
A data pipeline is a method of transport for data from outside sources to a singular storage location. Data pipelines act as a sort of tunnel system for data to travel and process through.
Data Lake
A data lake is a form of storage for data ingestion. Data lakes act as a singular location in which large amounts of data can be stored, secured, and accessed. They store unrefined or raw data that lacks processing.
Data Warehouse
A data warehouse is another form of storage for large amounts of data. However, unlike data lakes, data warehouses contain clean and processed data. Unlike raw data, this data has been processed and is ready to be analyzed for future decision-making.
Data Mart
A data mart is a smaller subsection of the data warehouse. Data marts store smaller, more specific pools of processed data. Usually, data marts tailor specifically to one team, product line, or market.
ETL
Extract, transmit, and load, or ETL, is a process very similar to data ingestion. In fact, some utilize ETL solutions. ETL refers to a series of steps needed to gather and store data from various sources. First, you must extract the data from different sources. Then the data must be processed and cleaned up for analysis, or in other words transformed. And finally, the newly transformed data must be loaded into the target destination.
Data Analysis
The end goal of data ingestion solutions is to store data for eventual data analysis. Data analysis refers to the process of studying gathered data in order to learn and discover useful information about consumers and various markets. Although important to uphold consumer security and privacy, data analysis offers benefits to both businesses and consumers.
What are Some Benefits?
Data ingestion solutions allow organizations to collect large amounts of unrefined data quickly and efficiently. This data is able to be collected, secured, and analyzed in order to learn valuable information and react appropriately. Data ingestion and analysis often lead to better business and consumer relationships, better product marketing, and better consumer experiences.
Final Thoughts and Notes
Data ingestion is a vitally important tool for organizations, businesses, and consumers alike. When effective, it allows for large amounts of data to process, then ready for analysis. Organizations can use either batch-based or real-time ingestion solutions in order to gather data from multiple different sources. The data is extracted from various locations, processed within data pipelines, and then stored in a singular secure location like a data lake or warehouse. With the importance and prevalence of the internet and data usage, learning about data ingestion can be beneficial to you or your business.
