Feature image What is Data Ingestion_

What is Data Ingestion?
Meaning, Benefits and Best Practices

By Laurent Mauer · September 15, 2022 · 10 min read

Imagine you have a big data set but don’t have the time or resources to analyze it.

What are you going to do?

This is where data ingestion comes in.

Data ingestion is the process of transferring data from one system to another. It can be a quick and efficient way to get your data ready for analysis.

But that’s not all data ingestion can do.

It can also improve the quality of your data. By cleansing and transforming your data, you can make sure it’s ready for analysis.

In this article, we’ll talk about what data ingestion is and the benefits of using it. We’ll also introduce some of the best tools for data ingestion.

What is Data Ingestion?

You might be wondering what data ingestion is.

Simply put, it’s the process of getting data into a system.

But there’s more to it than that. Data ingestion is about more than just getting data into a system. It’s also about making sure that the data is of high quality and is in the right format for the system you’re using.

This is important because if you’re trying to analyze data, you need to make sure that the data you’re working with is clean and accurate. Bad data can lead to inaccurate results and can even mess up your entire system.

That’s why data ingestion is such an important step in any data analysis process.

By taking the time to clean and format your data, you can ensure that you’re getting accurate results every time.

What are the Types of Data Ingestion and their Differences?

When it comes to data ingestion, there are two main types: batch-based and real-time/streaming.

  • Batch-based Data Ingestion

Batch-based ingestion is where you collect data from a variety of sources into a central repository before performing any analysis. This can be done on a scheduled basis or in response to an event.

  • Real-time/Streaming Data Ingestion

Real-time/streaming ingestion is where the data is collected and analyzed as it comes in. This is perfect for capturing data in motion, such as from sensors or social media feeds.

  • Batch mode vs Real-time/streaming mode 

There are pros and cons to each type of ingestion.

Batch mode is best suited when there are no explicit time constraints on the data. However, latency between ingestion and availability creates challenges with this method as well.

Real-time ingestion is best suited for use cases that require real-time insights and data. It can also be used for capture of metrics, patterns and trends. The main drawback of real-time data ingestion is that it places constraints on the kind of analytics you can perform on the data as it becomes available.

What are the Key Benefits of Data Ingestion?

There are many benefits to data ingestion. But let’s focus on the three key benefits: efficiency, improved quality, and time savings.

  • Efficiency

When it comes to efficiency, data ingestion can help you quickly and easily load data into your system. This can save you a lot of time and hassle. Plus, it means you can get started on your analysis right away.

Imagine you’re a data analyst. You’ve been asked to look into the data from the past month and see what insights you can glean.

But there’s a problem—the data is in different formats, and it’s scattered all over the place.

It would take you hours, if not days, to gather all the data and put it in a format that you can work with. But with data ingestion, you can avoid all that hassle.

  • Improve Data Quality

Improved quality is another key benefit of data ingestion.

When you have accurate, clean data, you’re able to get a better understanding of what’s happening in your business. You can also make better decisions, faster.

There are a few benefits to using data ingestion to improve quality.

First, it can help you get more out of your data. This might include formatting the data, adding missing information, or removing anything that’s not needed.

The goal is to make sure the data is ready for analysis so that the results are accurate. By preparing the data correctly, you’re able to extract more value and insights from it.

Second, it can help you identify errors and inconsistencies in the data. This is especially helpful when you’re working with large datasets. By identifying errors early on, you can fix them before they cause problems down the line.

Finally, data ingestion can help you ensure accuracy and consistency across different datasets. This is important when you’re trying to compare results or create reports.

  • Time Savings

Lastly, data ingestion can help you save time.

With clean data in hand, you won’t have to spend valuable time cleaning it up yourself. You can get straight to the analysis and find the insights you need to drive your business forward.

How Does Data Ingestion Work?

So you might be wondering, how does data ingestion actually work?

It’s a process of importing data into a system, and it can be done in a number of ways.

The most common way is to use an import tool, which can connect to your source system and pull the data in automatically.

This is a great option if you have a lot of data to import, or if your source system is changing frequently.

Another option is to use an API to connect to your source system.

This is a good choice if you want more control over the data that’s being imported, or if you need to import data that’s not easily accessible.

There are also a number of third-party tools that can help with data ingestion, and it’s worth exploring all of your options to find the one that’s right for you.

Data Ingestion vs ETL: What’s the Difference?

Data ingestion and ETL (extract, transform, and load) are two terms that are often confused.

But what’s the difference?

ETL is the process of extracting data from one or more sources, transforming it to meet the requirements of the system, and loading it into the target data store.

ETL is a traditional process that has been used for many years in data warehousing and big data environments.

Data ingestion, on the other hand, is a newer process that’s designed specifically for dealing with big data.

It’s a more automated way of getting data into your system, and it can handle large volumes of data more efficiently than ETL.

So which process is right for you?

That depends on your specific needs and the size of your data set. But as more and more businesses move to big data environments, data ingestion is becoming an increasingly important tool in the arsenal.

5 Key Challenges of Data Ingestion to Know

There are many benefits to data ingestion, including improved efficiency and quality. But there are also some key challenges you need to be aware of.

Here are five of the most important:

1. Data Volume

When you’re dealing with large volumes of data, it can be difficult to get it all into the system in a timely manner.

2. Data Variety

Not all data is created equal, and it can be difficult to handle different types of data simultaneously.

3. Data Quality

It’s important to ensure that the data you’re bringing in is of high quality so that it can be used effectively.

4. Time Constraints

There’s often a tight timeline for getting data into the system, and if you don’t meet it, there can be serious consequences.

5. Human Error

Ingestion is a complex process, and mistakes can happen along the way.

5 Must-know Best Practices for Data Ingestion

Imagine you’re the data analyst for a major retailer. Your job is to take all that customer data and turn it into insights that will help the company improve its sales and marketing strategies.

Sounds like a daunting task, right? But it’s a lot easier if you have the right tools and processes in place.

That’s where data ingestion comes in.

Data ingestion is the process of taking data from different sources and putting it into a centralized location.

This can be anything from customer data, to website logs, to social media posts. The more data you can collect, the better insights you can generate.

But data ingestion isn’t just about collecting data. It’s also about making sure that the data is clean and accurate.

That’s why it’s important to have best practices in place for data ingestion. Here are five of them:

  • Automation

As data grows in complexity and volume, human procedures can no longer be used to curate such massive amounts of data.

As a result, you should think about automating the entire process to boost productivity, save time, and eliminate human labor.

For example, if you want to extract data from a delimited file in a folder, you may transfer and purify it into SQL Server.

This procedure must be completed each time a new file is added to the folder.

You may automate the process by employing event-based triggers in a Data Ingestion tool, which can assist improve the entire ingestion cycle.

  • Anticipation

The transformation of data into a usable form is a precondition for data analysis. 

As the volume of data rises, this aspect of their work gets increasingly complex.

As a result, anticipating issues and planning properly is critical to the project’s success.

The first stage in building a data strategy would be to describe and address the obstacles connected with your unique use case difficulties.

For example, identify the source systems available to you and ensure you understand how to extract data from them. You may also seek outside assistance or use a code-free Data Integration tool to aid in the process.

  • Autonomy

Your company may require many new data sources to be absorbed on a weekly basis.

Furthermore, if your organization operates on a centralized level, it may have difficulties in fulfilling every request.

As a result, automating the process or utilizing self-service Data Ingestion can enable business users to manage the process with minimum interaction from the IT staff.

  • Ready-to-use dataset

Data ingestion technologies must provide an appropriate data serialization format. 

Generally, data is in a varied format, therefore transforming it to a single format makes it easier to relate or interpret the data.

  • Latency

Fresh data ensures more agile company decisions. Real-time data extraction from databases and APIs can be challenging.

Various target data sources, such as big object-stores such as Amazon S3 and analytics databases such as Amazon Athena Redshift, can be designed for receiving data in chunks rather than a stream.

 

Conclusion

Data ingestion is a process that helps organizations to efficiently load data into their data warehouses, data lakes, and other data management solutions.

 

The benefits of data ingestion are clear. By using a tool to help with the loading process, you can improve the quality of your data, increase your efficiency, and support BI and analytics initiatives. There are a number of different tools that can be used for data ingestion, so choose the one that best meets your needs.

At RestApp, we’re building a Data Activation Platform for modern data teams.

We designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting your data journey, check out our website and create your free account.

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Laurent Mauer
Laurent Mauer
Laurent is the head of engineer at RestApp. He is a multi-disciplinary engineer with experience across many industries, technologies and responsibilities. Laurent is at the heart of our data platform.
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