Data Ingestion: Engineering Better Entryways for Big Data

The bigger that big data gets, the more ways it has to work its way into an organization.

Data can be entered manually by employees within an organization. Or, in the case of customer-facing apps, users enter data into systems themselves. That virtual tonnage of email everyone in an org wakes up to on Monday mornings? That’s its own incoming deluge of data, as are aspects of all the other documents, communications, and correspondence that take place during the working day.

But all these multifarious ways data can enter a business aren’t the same. These are windows, convenient yet inefficient ways for data to enter a business. They don’t scale. They’re hard to automate. While they may provide access, they don’t guarantee accessibility, let alone quality.

So in addition to windows, data needs doors. And in modern business organizations, those doors are data ingestion processes.

What is data ingestion?

Data ingestion is the process of importing new data into a system or organization from various sources, which can include online services, databases, streaming data, IoT devices, and more.

This process is managed by specific layers within the overall data architecture, which are responsible for interfacing with these diverse data sources, performing initial processing, and ensuring the data is properly integrated into the system for further use.

In many organizations, data engineers often take the lead in identifying proper sources for data ingestion. Data analysts can help engineers ensure that the data ingestion process will satisfy relevant business needs, while data architects will oversee how data sources will work as part of the overall data management framework.

Depending on the needs or structure of an organization, the data ingestion process may also involve data integration tools that can provide interfaces for connecting to APIs, databases, and other data-producing systems. Some of the more advanced systems can automatically discover data sources—either cataloging these sources or ingesting the data automatically as well.

Automated or not, the data ingestion process itself involves a series of sequential steps:

1. Source identification: First, data sources for the ingestion process are identified. Handled by a data architecture’s source layer, these origination points for the data may be internal or external systems and may produce structured or unstructured data.
2. Data collection: The ingestion layer then collects data from all identified sources, bringing it into the system. While the data is being collected, the ingestion layer will sometimes perform some initial data processing (e.g., validation, light transformation, routing).
3. Data transportation: Once ingested, the data is then transported to the storage layer where it is temporarily stored in a data repository, often a database, data lake, or data warehouse. It will remain here until the newly ingested data is needed.
4. Data transformation: It’s rare that ingested data will meet the needs of downstream software, apps, and programs by default. So when the data is needed, it then moves to the processing layer where all needed data transformation will take place. The transformation process typically involves data cleaning, aggregation, and enrichment. While typically applied later on in the life cycle, change data capture (CDC) may be applied at this point in the ingestion process when database synchronization is a key concern.
5. Data storage: Once transformed, the newly structured data is then moved and stored in the consumption layer. In a sense, this is the final layer of the ingestion process, where it can be used for data science, data analytics, and business intelligence applications.

Some data architectures will include other layers we’ve omitted here, such as analytics, security, and serving layers, for example. But those we’ve detailed through the steps above provide a simple, clear breakdown of how a typical data ingestion process would function.

How is data ingestion related to ETL?

A common question—data ingestion and ETL are closely related but not entirely the same. Or, perhaps it’s better to say that ETL is a form of data ingestion, but not all forms of data ingestion are ETL.

This is because ETL is defined by the exact sequence of steps taken—extracting, transforming, loading—that data undergoes as part of this specific ingestion process. While a popular method of pipelining data into an organization, the nature of ETL may or may not be a fit depending on the specific needs of a business.

Pros and cons of different types of data ingestion

There are two main ingestion methods (or modes) of data ingestion: stream and batch ingestion.

There are indeed different types of data ingestion, and they are typically referred to as "methods" or "modes" of data ingestion, each with its own pros and cons depending on the use case.

Batch ingestion

Batch ingestion, which differs from batch processing, is an ingestion method where data is brought into a system at specific intervals in batches or chunks. The collection intervals will vary (i.e., weekly, hourly, daily) depending on several variables including data volume to be collected, storage constraints, and business requirements.

Batch ingestion is ideal when real-time availability and analysis of data aren’t a priority. Business examples of data flow related to this situation would be the influx of weekly inventory updates or daily sales reports.

Pros: Easy to manage, minimally complex, and efficient for ingesting large volumes of data. This efficiency is partly due to the fact that batch data ingestion can be scheduled during off-peak hours, making better use of system resources when overall demand is low

Cons: In no way suitable for real-time data processing. By default, batch ingestion creates a delay between data creation and when that data can be read, analyzed, or used downstream.

Stream (or real-time) ingestion

During stream ingestion, data ingestion is continuous, happening as soon as data is available from source systems. Being instantly available to an organization makes streaming a must for real-time data processing and analysis.

This makes stream ingestion essential for use cases where immediate data analysis is necessary, such as fraud detection, healthcare monitoring, and the functionality behind ecommerce recommendations.

Pros: The enablement of real-time decision-making based on consistently up-to-date data.

Cons: Streaming ingestion to minimize latency can be more complicated and resource-intensive than batch ingestion, therefore often necessitating more robust infrastructures.

There are situations where the benefits of both batch and streaming methods are required. Telecommunications and manufacturing are examples of industries where organizations may need to process large volumes of data in real-time, but also utilize periodic, scheduled processing of their data in batches.

A hybrid approach can afford these organizations flexibility, efficiency, and scalability in their data ingestion while ensuring data consistency, regardless if batch or real-time data ingestion is being utilized at any given time.

What are some best practice examples of data ingestion?

Best practices for data ingestion are crucial for ensuring that ingested data is of the highest possible quality. While they of course vary across use cases and organizations, here are 12 key practices for data ingestion:

1. Understand your data sources: Make sure you clearly identify and understand each data source that will be involved in your data ingestion process. This includes assessing the data format, velocity, volume, and variety from each source in order to plan the ingestion process effectively.

In addition to auditing data sources in this way, you may also want to engage with organizational stakeholders to ensure you also appreciate the business context and relevance of each of these data sources.

2. Choose the right ingestion method: Base your choice of data ingestion method (or methods) on the specific data requirements in your organization. As a general rule of thumb, look to batch ingestion to handle large quantities of data when time sensitivity isn’t an issue. When it is, opt for real-time ingestion to ensure that data is processed as it is ingested for immediate use.

3. Implement data quality checks: Data validation and quality checks can prove invaluable during the ingestion process. Ensuring data accuracy, completeness, and consistency before its use downstream helps build trust in an organization’s data systems, increasing the instances it’s leveraged for data-driven decision-making.

Implementing automated data validation rules to catch errors early in the ingestion process also improves overall performance and mitigates costs (both operational and financial) incurred from poor-quality data down the road.

4. Plan for scalability and flexibility: Make sure to design your data ingestion process so that it is both scalable and flexible. Data volumes and variety can fluctuate rapidly, and any modern ingestion process needs to be able to scale to handle increased loads. Failure to do so can lead to bottlenecks, poor performance, or outright failures.

The speed with which data is generated can also vary widely, and your ingestion process needs to be able to accommodate high-velocity data streams as they become more common.

5. Dial in your data transformation: Tune your transformation during ingestion to make sure data ends up in the proper format for storage and analysis. If real-time data isn’t an issue, look to a more specific ETL process to ensure high transformation efficiency, and document transformations for traceability and reproducibility.

6. Vet data storage based on efficiency: Storage solution(s) (e.g., data lakes, data warehouses) should be selected by how efficiently they can handle the type of data being ingested and how it will be used. To further increase efficiency, use indexing strategies and data partitioning to optimize query performance. Also, regularly archive or purge old data to optimize performance and keep storage costs reasonable.

7. Design-in security and compliance: You will also need to ensure that any data ingestion process you design will comply with organizational security policies and data privacy regulations. Implementing role-based access control can bolster overall data security. And take it upon yourself to stay well-versed regarding data privacy laws in order to ensure compliance.

8. Implement continuous monitoring and logging: The data ingestion process needs to be continuously monitored in order to maintain performance and catch errors if/as they occur. Implement logging to track data flow and troubleshoot issues, leverage monitoring tools to track data flow and system health in real time, and set up alerts for any failures or anomalies that occur during the ingestion process.

9. Ensure robust error handling and recovery: Data integrity, system reliability, and operational continuity all hinge on the fidelity of your error handling and recovery capabilities. To help make these capabilities robust, employ retry mechanisms and failover strategies to handle ingestion failures. Regularly test your backup recovery procedures as well.

10. Prioritize documentation and governance: Maintain impeccable documentation of the entire data ingestion process and establish data governance practices to manage data access, quality, and usage. Lineage information and a basic data dictionary can foster transparency, and conduct regular data governance meetings to maintain alignment regarding new policies and practices.

11. Leverage automation: Wherever practical, look to automate the data ingestion process in order to reduce manual effort and minimize the occurrence of errors. Automating data quality checks and alerts specifically can reduce the need for manual monitoring.

12. Optimize always: Make sure you are regularly reviewing and optimizing the performance of the data ingestion process. Doing so allows you to handle data more efficiently, reducing resource consumption whenever possible. Look to optimize SQL queries and data models to maintain performance, and use performance metrics to identify and minimize (or mitigate) bottlenecks.

Opening the door to data ingestion tools

Whether you’re aiming to establish a data ingestion process or engineer a data ingestion pipeline, you’re ultimately working to build better doors for raw data to enter your organization. So, in addition to best practices, pay special attention to the tools you’re putting to work. In our humble opinion, data orchestration platforms should be the first place you look.

Building your ingestion process around a data orchestration platform enables you to automate workflows involved in data ingestion from square one. Best-in-class data orchestration platforms are also built to scale as data volumes grow, are highly flexible, and support error handling and recovery, performance optimization, compliance and security, and more.

All this means by building around a data orchestration platform, you’re integrating ingestion best practices directly into your process, as opposed to trying to apply them down the road. Which can make all the difference when building better entryways for your data.

Enjoying all the info and insights? Good, because we love writing about them.Join our substack to open your own door to getting more inside knowledge piped directly into your inbox.