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Intelligent Document Processing as a Data Source for Data Ingestion Data Ingestion: The First Step Towards a Secure and Sustainable Data Strategy

Data Ingestion describes the automated extraction, structuring, storage, and transfer of data. This process makes it possible to install a smooth data pipeline. The preparation of heterogeneous data into a structured, cloud-based data management system enables it to be analyzed automatically in real time, offering a decisive market advantage.

With its Intelligent Document Processing service, Retarus provides an essential data source for data ingestion. The services enable companies to digitize all business communications, make them available in a structured form in the required format and thus automate end-to-end workflows.

From an Unstructured Source to a Cloud-Based Data Management System: This Is Data Ingestion

Data ingestion describes a process in which large volumes of data are imported from various sources and merged into a storage medium. This target medium is usually a cloud-based or locally installed ERP system. However, the data can also be fed into a data warehouse, a data mart, or a data lake.

In order to create added value, the data from these storage mediums must be easy to retrieve, use, and analyze. It must also be structured to create a powerful data pipeline. Special data wrangling tools are required for this structuring. In summary, data ingestion involves digitizing unstructured data, analyzing it, extracting it, structuring it, storing it, and processing it on a target medium.

Data warehouse

The term data warehouse refers to a central database system that can be used by companies for analysis purposes. This system collects and stores important data from various data sources and supplies them to downstream systems. The advantage of a data warehouse is that it provides a global view of data from very different data sets.

Data mart

A data mart is a subject-oriented database. Often but not always, it is a sub-segment of a data warehouse. However, while data warehouses contain all of a company’s information, data marts only meet the needs of specific business functions or departments.

Data lakes

Data lakes are large pools of raw data for which no use has yet been determined. These data lakes can contain both structured and unstructured data in large quantities for subsequent analysis. In contrast to a data warehouse, which transfers collected data directly into structures and formats, a data lake allows for data to also be stored in its raw format.

Real-Time or Batches: Each Type of Data Ingestion Has Its Advantages

There are currently three possible approaches to successful ingestion: Real-time ingestion, batching data ingestion, and micro-batching. Depending on project constraints and data sources, any of these options may be the optimal data strategy.

Real-Time Data Ingestion

Real-time data ingestion, also known as stream ingestion, imports each data element as it becomes available. This means that each data element is processed as an individual object. This type of data ingestion is very costly, but is especially worthwhile for analytics that need to be consistently up-to-date. Real-time data ingestion is the only solution for applications that rely on real-time data. For example, real-time data processing is essential for stock market trading.

Batch Data Ingestion

Batch data ingestion is the most common type of data ingestion. Here, source data is collected at fixed intervals and grouped according to defined criteria. This method is less expensive and therefore useful for companies that collect specific data on a daily basis and do not need to make decisions in real time.


As the name suggests, micro-batching is the intermediate stage between real-time data ingestion and batch data ingestion. Although the data is also divided into groups, it is imported in much smaller steps. It is not processed individually; the transfer time is much shorter than for large batches.

Data Ingestion vs. ETL

Data ingestion and ETL, or extract, transform, and load, are very similar processes, but they differ in their goal. Data ingestion extracts and structures data to prepare it for an application that requires a specific format. For this, the data sources do not need to be linked to the target.

ETL is different. This specific process primarily refers to data preparation for data warehouses and data lakes. Its focus is on long-term storage for use in business intelligence (BI) and other analytics. ETL is therefore also a data ingestion process, but it involves not only the extraction of data and its transfer, but also the transformation of the data before it is sent to its destination.

The Advantages of Data Ingestion

Data ingestion offers several advantages that can give users the edge in highly competitive markets.

High availability of data

One of the most important benefits of ingestion is the immediate availability of information. Data that was previously stored locally in various locations can be accessed anytime and anywhere through centralized, cloud-based storage. With the help of defined authorizations, departments and functional areas can access precisely the data they need.

Simple analysis thanks to structuring

Data integration and ingestion simplify analysis, especially when combined with an ETL solution and related standard formatting. Data is easier to process thanks to the reduced complexity. Pipelines can deliver data to the data warehouse immediately and completely automatically.

High flexibility

Together with an intelligent document processing service, data capture tools can also process unstructured data formats. Automated processing of letters, PDFs received by email, or faxes is therefore no longer a problem. This flexibility enables smooth processes in all areas.

A more solid decision-making foundation for companies

Various analysis tools provide valuable BI insights from the multitude of data sources. With the help of processed data, problems and opportunities can be quickly identified and better decisions can be made.

Here’s How Companies Are Tackling the Challenges of Data Ingestion

These are the challenges faced by companies who are looking to establish data pipelines:


The most important aspects when dealing with sensitive business data are data security and protection. In data ingestion, data is made available at several points in the data pipeline. With Intelligent Document Processing, Retarus supports companies in meeting local and global data protection and security requirements at all times: Retarus’ cloud services are fully GDPR-compliant and meet other domestic and international security and compliance requirements such as EU Directive 95/46/EC, ISAE 3402, and SOC 1 and SOC 2 Type II.


As data volumes grow, so does the need for more storage systems and servers. These are expensive and costly to maintain because of data security and privacy regulations. However, this is only an issue when using on-premises providers.

Data Quality

Keeping data quality high is particularly challenging. Retarus Intelligent Document Processing correctly recognizes up to 98 percent of source data with its powerful Intelligent Document Recognition (IDR) feature, which uses multiple OCR engines. The addition of human-in-the-loop offers a recognition rate of up to 100 percent. This is how Retarus creates optimal conditions for the smooth, automated further processing of digitized data.

Fragmentation and Data Integration

Data ingestion is often problematic because overlaps occur when different business units access the same source. Vendors also fail to integrate different third-party sources into one data pipeline.

How Retarus Solves Its Customers’ Data Challenges

Retarus offers more than just a SAAS solution. With its Managed Service, this enterprise cloud provider keeps the IT department’s workload to an absolute minimum. Thanks to professional workshops focused on process improvement and support in connecting new customers, user tasks are kept to a minimum and important resources are spared.

Retarus Intelligent Document Processing offers smooth workflows and, thanks to data capture via a multi-OCR engine with additional human-in-the-loop, a large amount of data can be digitized almost error-free in a short amount of time. The entire process is 100% compliant with the strictest data protection requirements, including the GDPR.

In addition, Retarus Cloud Services help companies to organize their business processes efficiently. Retarus Service Managers provide customers with personal support throughout all project phases. Comprehensive consulting, solution designs tailored to the customer, and 24/7 support in the customer’s preferred language are also part of the service.

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