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Data Ingestion – Definition, Challenges, and Best Practices

By |2022-11-25T07:04:51+00:00March 16th, 2020|

Organizations today rely heavily on data for predicting trends, forecasting the market, planning for future requirements, understanding consumers, and making business decisions. But to accomplish these tasks, it is essential to get fast access to enterprise data in one place. This is where data ingestion comes in handy. But what is it? It refers to the extraction of information from disparate sources so that you can uncover insights hidden in your data and use them for business advantage. It is best to partner up with companies that offer efficient data ingestion services for accurate and timely insights.

data ingestion meaning

What is Data Ingestion?

It is defined as the process of absorbing data from a variety of sources and transferring it to a target site where it can be deposited and analyzed. Generally speaking, the destinations can be a database, data warehouse, document store, data mart, etc. On the other hand, there are various source options, such as spreadsheets, web data extraction or web scrapping, in-house apps, and SaaS data.

Enterprise data is usually stored in multiple sources and formats. For example, sales data is stored in Salesforce.com, Relational DBMSs store product information, etc. As this data originates from different locations, it must be cleaned and converted in a form that can be easily analyzed for decision-making using an easy-to-use data ingestion tool. Otherwise, you will be left with puzzle pieces that cannot join together.

Data ingestion can be performed in different ways, such as in real-time, batches, or a combination of both (known as lambda architecture) depending on the business requirements. Let us look at ways to perform it in more detail.

  • Real-Time Ingestion

Data ingestion in real-time, also known as streaming data, is helpful when the data collected is extremely time-sensitive. Data is extracted, processed, and stored as soon as it is generated for real-time decision-making. For example, data acquired from a power grid has to be supervised continuously to ensure power availability.

  • Batch Ingestion

When ingestion occurs in batches, the data is moved at recurrently scheduled intervals. This approach is beneficial for repeatable processes. For instance, reports that have to be generated every day.

  • Lambda Architecture

The lambda architecture balances the advantages of the above-mentioned two methods by utilizing batch processing to offer broad views of batch data. Plus, it uses real-time processing to provide views of time-sensitive information.

After understanding what data ingestion means, let’s delve into its benefits and challenges.

Data Ingestion Benefits

Data ingestion has numerous benefits for any organization as it enables a business to make better decisions, deliver improved customer service, and create superior products. In other words, the data ingestion process helps a business gain a better understanding of its audience’s needs and behavior and stay competitive which is why ample research should be done when looking for companies that offer data ingestion services.

Overall, data ingestion is one of the most effective ways to deal with inaccurate, unreliable data.

Challenges Associated with Data Ingestion

The following are the key challenges that can impact data ingestion and pipeline performances:

  • Sluggish Processes

Writing codes to ingest data and manually creating mappings for extracting, cleaning, and loading data can be cumbersome as data today has grown in volume and become highly diversified.

Therefore, there is a move towards data ingestion automation. The old procedures of ingesting data are not fast enough to persevere with the volume and range of varying data sources. Hence, an advanced data ingestion tool is required to ease the process.

  • Increased Complexity

With the constant evolution of new data sources and internet devices, businesses find it challenging to perform data integration to extract value from their data.

This is mainly because of the ability to connect to that data source and clean the data acquired from it, like identifying and eliminating faults and schema inconsistencies in data.

  • The Cost Factor

Data ingestion can become expensive because of several factors. For example, the infrastructure you need to support the various data sources and patented tools can be very costly to maintain in the long run.

Similarly, retaining a team of data scientists and other specialists to support the ingestion pipeline is also expensive. Plus, you also have the probability of losing money when you can’t make business intelligence decisions quickly.

  • The Risk to Data Security

Security is the biggest challenge that you might face when moving data from one point to another. This is because data is often staged in numerous phases throughout the ingestion process. This makes it challenging to fulfill compliance standards during ingestion.

  • Unreliability

Incorrectly ingesting data can result in unreliable connectivity. This can disrupt communication and cause loss of data.

Data Ingestion Best Practices

To deal with the challenges associated with data ingestion, we have compiled three best practices to simplify the process:

Anticipate Difficulties and Plan Accordingly

The prerequisite of analyzing data is transforming it into a useable form. As the data volume increases, this part of their job becomes more complicated. Therefore, anticipating the difficulties in the project is essential to its successful completion.

The first step of the data strategy would be to outline the challenges associated with your specific use case difficulties and plan for them accordingly. For instance, identify the source systems at your disposal and ensure you know how to extract data from these sources. Alternatively, you can acquire external expertise or use a code-free data integration tool to help with the process.

Automate the Process

As the data is growing both in volume and complexity, you can no longer rely on manual techniques to curate such a huge amount of data. Therefore, consider automating the entire process to save time, increase productivity, and reduce manual efforts.

For instance, you want to extract data from a delimited file stored in a folder, cleanse it, and transfer it into the SQL Server. This process has to be repeated every time a new file is dropped in the folder. Using a data ingestion tool that can automate the process by using event-based triggers can optimize the entire ingestion cycle.

Furthermore, automation offers the additional benefits of architectural consistency, consolidated management, safety, and error management. All this eventually helps in decreasing the data processing time.

Enable Self-Service Data Ingestion

Your business might need several new data sources to be ingested weekly. And if your company works on a centralized level, it can face trouble in executing every request. Therefore, making the process automated or opting for self-service data ingestion can empower business users to handle the process with minimal intervention from the IT team.

Wrap Up

Hopefully, by now you understand what data ingestion means along with its efficient usage. Additionally, data ingestion tools can help with business decision-making and improving business intelligence. It reduces the complexity of bringing data from multiple sources together and allows you to work with various data types and schema.

Moreover, an efficient data ingestion process can provide actionable insights from data in a straightforward and well-organized method. Practices like automation, self-service data ingestion, and anticipating difficulties can enhance your process by making it seamless, fast, dynamic, and error-free.

Explore the data ingestion capabilities of Astera Centerprise by downloading the free trial version.