Data ingestion involves transporting data from different sources of raw data into a storage medium so that it can be accessed, used, and analyzed by data analysts and scientists in an organization. The storage medium can be typically a data warehouse, data mart, or simply a database, while its sources can be from applications, databases, spreadsheets, or raw data scraped from the web. In any data analytics architecture, the data ingestion layer is its backbone. There exist different methods of ingesting data, and the data ingestion layer is designed according to a particular model and architecture.
Why data ingestion?
Organizations and companies need data ingestion to enable them make better decisions in their operations and making superior products, and delivering better customer service. Through data ingestions, businesses are able to understand the needs of their stakeholders, customers, and partners thus staying and remaining competitive. Data ingestion is the ultimate way for businesses to deal with tons of inaccurate and unreliable data.
How is data ingestion done?
Data ingestion is performed in various ways. Top of these ways include;
The choice for each of these ways depends on the requirements of each organization. Let us discuss each of these ways in details;
Real-time data ingestion
Ingesting data in real-time is also known as streaming data. It is the most important way to ingest data especially when the data being collected is time sensitive. In this method, the data is extracted, processed and transferred to storage in no time for real-time use, e.g. decision making.
Batch data ingestion
Batch data ingestion involves moving data at scheduled intervals. This method is appropriate for recurrent processes e.g. reports that have to be generated periodically, say, daily.
The combination of real-time and batch data ingestion methods is what is known as the lambda architecture. This method leverages the benefits of these two methods. It leverages real-time data ingestion to provide insights from time sensitive data. It also leverages the batch ingestion method to provide broad insights of recurrent data.
What are the data ingestion best practices?
Self-service data ingestion
Many organizations have several sources of data. All this data needs to be ingested before storage and onward processing. Data continues to grow in size and metrics and requires that the organizations continue to add the resources needed to handle the data. If the process of data ingestion is self-service, through measures such as automation, it eases the pressure to continuously add the resources and focus is now shifted to processing and analysis. The ingestion process becomes very easy and will require minimum to no intervention from technical personnel.
Automating the process
As organizational data continues to grow, both in volume and complexity, manual techniques of handling and processing it can no longer be depended on. The need to automate every process along the way increases to see that you save time, reduce manual interventions, minimize system downtimes, and increase the productivity of the technical personnel.
Automating the ingestion process offers additional benefits including; architectural consistency, error management, consolidated management, and safety. These benefits come in handy to reduce the time taken to process data.
Anticipate challenges and planning appropriately
The imperative of any data analysis is to transform it into a usable format. As data continues to grow in volumes and type, so do the complexities of data analysis. When there is a process that can help you anticipate these challenges in advance, you will have an easier time completing the whole data processing tasks successfully. Data ingestion is one big process that helps you anticipate these challenges, plan accordingly in advance, and work on them efficiently as they come, without necessarily having to incur any loss of time and output.
Use of Artificial Intelligence
Making use of Artificial Intelligence concepts such as statistical algorithms and machine learning eliminates the need for manual interventions in the data ingestion process. Manual intervention increases the number and frequency of errors in the data ingestion process. Employing Artificial Intelligence not only eliminates these errors but also makes the whole process faster and increases the accuracy levels.
Data ingestion reduces the complexities involved in gathering data from multiple sources and frees up the time and resources for subsequent data processing steps. The emergence of data ingestion tools such as DQLabs has seen the creation of efficient data ingestion options that can help businesses improve their performance and results by easing the decision-making process from their data.
An efficient data ingestion process provides actionable insights from data in an efficient, straightforward, and easy to understand method. Data ingestion best practices like the use of AI, automation, making data ingestion process self-service, and anticipating challenges and planning appropriately enhances the data ingestion process by making it fast, dynamic, seamless, and accurate.