Data Ingestion: What is it?
Confused on what Data Ingestion is? We’re here to help.
Data ingestion refers to transporting data from various sources to a proper storage medium to be accessed and used when needed. Data ingestion approaches can vary. One example of data ingestion: taking data from multiple in-house systems and putting the data into a business-wide reporting or analytics platform, otherwise referred to as a data lake (or data warehouse).
There are a few ways data ingestion is performed:
Real-time - Real-time is also referred to as streaming data. This method is crucial, especially in the case of time-sensitive information. Real-time data ingestion means data is retrieved, processed, and stored in real-time to be used by real-time applications such as decision making.
Batch - This approach entails shifting data at predetermined times. This method is best for recurring processes like weekly reports that need to be pulled or generated.
Lambda Architecture - The Lambda method combines the two (real-time and batch) processes. It allows for the extraction of time-sensitive data while also providing a broad view of recurring data.
Data Definitions:
Self-Service Data Ingestion
Self-service data ingestion refers to the application of a tool that makes it easy for non-technical employees to connect data from various sources to a destination where they can use it with self-service analytics and BI tools. The size of data stored only continues to grow in size and metrics, requiring enterprises to keep adding to the resources used to maintain and manage their data. When the ingestion process is self-service, it can relieve pressure to expand these costly resources constantly. Instead, an enterprise can switch its focus to processing and analyzing without asking for assistance from technical personnel.
Automation
Organizational data continues to grow in size and complexity. Manual techniques of handling and processing data can seem archaic as a result. Because the data can no longer be manually processed, enterprises need to automate every process along the way. When doing so, enterprises can see an increase in time saved. Additionally, they’ll see a reduction in manual interventions and minimal system downtimes. Other benefits include increased productivity for technical personnel who no longer need to be a part of this process.
Some Tips:
Prepare For Challenges
The goal of any data analysis is to transform it into a usable format. With the continual growth of data in both volume and type, enterprises will also see increased complexities. When there’s a process in place that can assist with anticipating these challenges, an enterprise will have a smoother time completing the data processing task successfully. Data ingestion itself is a process that helps an enterprise anticipate challenges, prepare accordingly and promptly with minimal loss of time and resources.
Utilize A.I. When Possible
Putting ai to use (such as statistical algorithms and machine learning) can help eliminate the need for manual input throughout the ingestion process. Errors are inevitable when it comes to manual intervention. By employing A.I. will not only eliminate mistakes but additionally make the process faster and smoother.
Ultimately, data ingestion minimizes the complexities involved in gathering data from various sources. Data ingestion also frees up time and resources that could be put to better use. The decision-making process, which can sometimes feel cumbersome and complicated, is also made more accessible when an enterprise implements data ingestion. If you’re not sure where to begin, let’s chat!
