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Data ingestion is required for organizations and businesses to make better decisions in their operations and provide better customer service. Businesses can understand the needs of their stakeholders, consumers, and partners through data ingestions, allowing them to stay competitive. Data ingestion is the most effective way for businesses to deal with tons of inaccurate and unreliable data.

How is data ingestion done?

It is performed in various ways. Top of these ways include;

  • Real-time  - Ingesting data in real-time is also known as streaming data.  It is the most crucial method of ingesting data, especially when the information is time-sensitive. In this method, data is retrieved, processed, and stored in real-time for real-time applications, such as decision making.

  • Batch - The batch approach entails shifting data at predetermined times. This method is excellent for recurring processes, such as reports that must be generated on a regular basis, such as daily.

  • Lambda Architecture - The lambda architecture is a method that combines real-time and batch procedures. This strategy combines the advantages of the two methods. It makes use of real-time ingestion to extract information from time-sensitive data. It also makes use of batch ingestion to provide a broad view of recurring data.

Best Practices:

Self-service data ingestion 

Many organizations have multiple data sources. All of this data must be ingested before it is stored and processed. Data continues to grow in size and metrics, requiring enterprises to continue to add the resources required to manage it. If the ingestion process is self-service, it relieves the pressure to constantly expand resources through methods such as automation, and the focus is now switched to processing and analysis. The ingestion process becomes very simple, requiring little to no assistance 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 task 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 ingestion process. Manual intervention increases the number and frequency of errors in the 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 options that can help businesses improve their performance and results by easing the decision-making process from their data.

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