What is Data Ingestion? Definition, Challenges, and Best Practices

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What is Data Ingestion? Definition, Challenges, and Best Practices
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Businesses are generating more data than ever before. Each day, enormous amounts of data are accumulated from various sources, including websites, social media, sensors, IoT devices and more. This data contains valuable insights that can help organizations make informed decisions that drive business growth. However, it’s often stored in separate systems that makes it difficult to access and use.

Data ingestion is the process of gathering this data from various sources, processing it, and storing it in a single destination where it can be harnessed for analysis and decision-making.  

To help you understand the value of data ingestion and how it works, we’ve created this guide that outlines what data ingestion is, why it’s important, and different types of data ingestion. We also discuss the benefits and challenges of data ingestion and outline a few best practices to help your organization make the most of this crucial process.   

What is data ingestion?  

At its core, data ingestion is simply the process of extracting raw data from different sources and moving it to a target repository where it can be further analyzed or processed. It is the first step of the data pipeline and is a subset of data integration.  

The data that’s ingested can include structured data, like databases and spreadsheets, or unstructured data, like text documents, images, and log files. This data can be pulled from various sources, including IoT devices, CRM systems, and SaaS apps. Regardless of the data’s form, the objective of data ingestion is to make it accessible, reliable, and ready for analysis.  

One example of a target repository for ingested data is a data lake. This environment can ingest raw data in various formats without the need for it to be structured. By being able to collect large amounts of data at a time, data lakes open the way to analytics at a large scale. When leveraged properly, this provides numerous benefits to companies. The key is to work with a data lake provider that can approach data lakes the right way.  

At C&F, we work with numerous companies across regulated industries to properly harness data lakes for data-driven decision making and business growth. We do this by tailoring approaches that are agile, well-governed, and legally compliant. You can read more about our data lake process in this blog article.  

Ultimately, data ingestion serves many purposes, including automating business processes and helping organizations make more informed, data-driven decisions.   

Why is data ingestion important? 

Data ingestion plays an important role in helping businesses manage their data and make use of its insights. Below are several examples that highlight the importance of data ingestion.  

Real-time decision making 

In today’s fast-paced business environment, decisions need to be made quickly. Data ingestion allows organizations to access data in real-time so they can make informed decisions quickly and easily.  

Data integration 

Most companies have data stored in several locations and different formats. Data ingestion tools and processes allow organizations to integrate data from various sources into a unified repository, making it easier to analyze and receive insights.  

Better-quality data 

Part of the data ingestion process involves data cleansing and processing. This ensures data is managed more efficiently, increasing its reliability and leading to a more refined dataset. Depending on what the data is being used for, it might be standardized and transformed into a common format in an automated and repeated way. The result is better-quality data that can meet data quality objectives.  


As companies grow, their volumes of data also grow and it becomes necessary to scale data ingestion processes. Using modern data ingestion tools makes it easy for organizations to handle large volumes of data in an efficient way.  

Security and compliance 

Complying with data security laws and regulations is essential for all businesses. Part of the data ingestion process can include mechanisms to enforce security measures and ensure organizations are complying with privacy laws. 

Types of data ingestion 

There are different methods of data ingestion. The type of data ingestion your organization may benefit from will usually depend on how quickly you need access to the data, the sources your data is coming from, and what you will be using the data for.  

The three main types of data ingestion are batch processing, real-time processing, and lambda, which is a combination of the first two. We outline these below. 

Batch processing 

In batch processing, data is collected and transferred to the target site in batches according to scheduled intervals. These intervals can be scheduled to occur automatically or be triggered by a user query or application.  

Batch-based processing is useful because it allows for complex analysis of large historical datasets. Batch data ingestion is also a good option for companies that only need to collect specific data points on a daily basis or don’t rely on data to make decisions in real-time. 

Batch processing is supported by ETL (extract, transform, load) pipelines. These convert raw data to match the target system before it’s loaded, ensuring systematic and accurate data analysis in the target repository. In the past, batch processing has been easier and less expensive than real-time ingestion, however this is quickly changing with modern data ingestion tools.  

Real-time processing 

Real-time processing is also known as stream processing. This type of data ingestion involves collecting and transferring data from source to target in real-time. Instead of loading data in batches, each piece of data is collected and transferred as soon as it’s recognized by the ingestion layer.  

One of the major benefits of real-time processing is that a company can analyze or report on their complete dataset without having to wait for it to extract, transform, and load more data.  

Real-time ingestion is essential for organizations that need to respond to new information quickly, such as the stock market. They’re also important for making rapid operational decisions and identifying and acting on new insights.  

Lambda architecture 

Lambda architecture-based ingestion consists of both batch and real-time ingestion. This type of data ingestion consists of three layers: batch, serving, and speed. The first two layers, batch and serving, index data in batches. The third layer, speed, indexes in real-time any data that hasn’t yet been picked up by the first two, slower layers. This ongoing collaboration between the different layers ensures that data is complete and available for query with minimal latency.  

This type of data ingestion provides the benefits of both batch and real-time processing. You get a full view of your historical batch data with less latency and a lower risk of data inconsistency.  

Benefits of data ingestion 

There are many ways an organization can benefit from the data ingestion process. We’ve outlined just some of these benefits below.  

Availability of data  

A business’s data is often siloed, existing in separate systems and not always easily accessible. Data ingestion ensures that data from different departments across your organization is moved to a unified environment where it can easily be accessed and analyzed.  

Data transformation and uniformity 

Data collected from different sources usually comes in different formats, making it difficult to analyze and use in decision-making. Part of the data ingestion process uses advanced software and ETL tools to convert, clean, and structure different types of data into a usable format before delivering it to the target site. Collecting and cleansing this data unifies dozens of types and schemas into a single, consistent dataset that can be used for BI and analytics. 

Business insights through analytics 

Real-time data ingestion feeds analytics and BI tools, allowing organizations to quickly gain valuable insights, spot issues, and recognize opportunities to improve performance with informed, data-driven decisions.   

Analytics is where data is transformed into a strategic business asset that can be used to drive success. With effective data design that reveals quality insights, organizations can use data analytics to make informed decisions that increase job satisfaction, promote higher levels of engagement, and improve their competitive edge in the data-driven business world.  

Improve apps & software tools 

Data ingestion technology can help engineers move data quickly to improve software and apps and ensure users get the best experience possible. 

Save time & money 

Data ingestion tools allow organizations to automate many manual tasks that previously had to be carried out by data scientists. This can save businesses time and money by allowing teams to focus on other tasks.  

Challenges of data ingestion 

Data ingestion pipelines are continuously becoming easier for businesses to set up, maintain, and scale, however it still comes with some challenges. Some of these data ingestion challenges are described below.  


When importing data from various sources to target systems, it may be staged at several points throughout the data ingestion pipeline. This process has the potential to make sensitive data more vulnerable to security breaches.  

Legal regulations 

Data teams performing data ingestion must comply with various data privacy and protection regulations, such as GDPR and HIPAA, to ensure they remain within the law. This can add more cost and complexity to the process.  

Data scale & variety 

Most businesses are experiencing increased data volumes, velocity, and variety year on year. This can make it challenging to ensure data quality and conformity to the required format and structure. It’s also likely that most organizations will continue to see an increase in data types and sources. As a result, it can be difficult to build a data ingestion framework that will continue to perform into the future.   

Maintaining quality 

It can be difficult to maintain reliable data when carrying out complex data ingestion processes. As a result, it’s important for organizations to establish processes around checking data quality and completeness.   

Best practices for data ingestion 

To help your organization make the most of data ingestion, we’ve included some best practices to guide you throughout the process.  

Choose the right method  

Be sure to carefully consider the best data ingestion method for the type and volume of your data sources. As a guideline, use batch processing for large and stable data sets that don’t call for immediate analysis and real-time processing for dynamic and time-sensitive data that calls for quick insights.  

Use data ingestion tools to automate the process 

Data ingestion tools help automate repeatable tasks and simplify the process with features like data extraction, transformation, and loading (ETL), data integration, data validation, and data monitoring.   

There are many benefits to using tools to automate the data ingestion process. Firstly, they speed up the process while reducing the risk of human error. Data ingestion tools also free up valuable resources and give your team more time to focus on other efforts, making data ingestion efforts more scalable. Finally, they increase data processing time so end users can access the insights they need quicker.  

To provide a real life example of how automation tools can improve processes, let’s look at one of our clients, a Fortune 50 industry giant in the animal health sector. After implementing one of our tools, AdaptiveGRC, we were able to increase operational efficiency of governance, risk, and compliance by 90%. This led to a 51% reduction in system operation costs and an overall simplification of compliance processes for our client.  

The importance of these tools can’t be overestimated, especially when it comes to regulated industries .Of course, the type of data ingestion tools you should use will depend on various factors, including compatibility with the source and destination systems, scalability requirements, cost, and complexity of implementation. Some examples of data ingestion tools include Apache Kafka, Apache NiFi, Azure Data Factory, and Google Cloud Dataflow.  

Keep a copy of all raw data  

It’s essential that you protect your raw data and store it in a separate database in your data warehouse. This database will act as a backup in case something should go wrong with the ingestion process. Make the raw data layer strictly read-only and don’t allow any tools or users to have write access.  

Document and monitor your process 

This is one of the most important best practices of data ingestion. Documenting and monitoring your data ingestion process can help you track, audit, and troubleshoot your data flow. It also ensures that, should something go wrong, it’s easy for another member of the team to take over.  

Proper documentation should include keeping a clear record of your data sources, destination, schema, format, quality, lineage, and metadata. You can also document the various ingestion tools you’re using and connectors you’ve set up within those tools.  

Monitoring involves using different metrics, logs, and dashboards to visualize the performance, status, and issues of your data ingestion process. This can help you spot and respond to issues in your data flow.  

Unlock the true potential of your data 

By enabling real-time insights, improving data quality, and streamlining data integration, the data ingestion process helps organizations make informed decisions that drive their business forward. To explore how your business can harness the power of its data and benefit from data ingestion, get in touch with our team.  

With more than 20 years of experience working with the world’s largest companies, we can help you find the right solutions to address your unique challenges and opportunities. Together, we can transform your organization into a data-driven industry leader.