Data Ingestion Best Practices Simplified 101
In order to improve operations and customer service, organizations and businesses must gather and process data. By analyzing this information, they can gain insight into the needs of their stakeholders, consumers, and partners and remain competitive in their field. Data ingestion is the most efficient way for businesses to handle large amounts of potentially inaccurate or unreliable data.
Table of Contents
In this article, you will learn about 7 different data ingestion best practices to follow and why they are useful.
Table of Contents
- What is Data Ingestion?
- Data Ingestion Best Practices
- Final Thoughts
What is Data Ingestion?
Data ingestion is the process of importing data from various sources into a data storage system or database. It is a crucial step in the data pipeline that enables organizations and businesses to make informed decisions based on accurate and up-to-date data. There are three types of data ingestion: real-time, batch-based, and architecture-based data ingestion.
There are various tools and technologies available to facilitate data ingestion, including batch processing systems, stream processing systems, and data integration platforms. Data Ingestion is typically performed in real-time or near real-time, depending on the requirements of the data pipeline and the needs of the organization. By automating data ingestion, businesses can save time and resources and ensure that their data is consistently accurate and up-to-date.
Data Ingestion Best Practices
Here are 7 best data ingestion practices you can follow to avoid facing challenges:
Implement Alerts at the Sources for Data Issues
Yes, adding alerts at the source of the data is one of data ingestion best practices. By setting up alerts at the source, you can be notified of any issues with the data as soon as they occur, which can help you address the problem quickly and minimize the impact on your downstream processes. Some examples of data issues that you might want to set alerts for include:
- Data Quality Issues: If the data is incorrect, incomplete, or invalid, set up an alert to notify you so that you can take corrective action.
- Data Availability Issues: If the data is not being generated or transmitted as expected, you should set up an alert to notify you so that you can investigate the cause of the problem.
- Data Security Issues: If there is a security breach or other issues that could compromise the data, you should set up an alert to notify you so that you can take appropriate action to protect the data.
It is also a good idea to set up alerts at various points in your data ingestion process to monitor the overall health of the process and identify any issues that may arise.
Make a Copy of All Raw Data
It is generally a good idea to keep a copy of your raw data before applying any transformations to it. This is especially important if the raw data is difficult or expensive to obtain, as it allows you to go back to the original data if you need to re-process it or if something goes wrong during the transformation process.
It can also be useful to have a copy of the raw data for future reference in case you need to perform additional analyses or compare the results of different transformations. In addition, keeping a copy of the raw data can help ensure the integrity of your data, as it provides a way to verify the accuracy and completeness of the transformed data.
Implement Automation for Data Ingestion
Automation can can help to reduce the time and effort required to ingest data, allowing you to focus on other tasks. It can also help to ensure that data is ingested consistently and accurately without the possibility of human error. Automation can also be useful for handling large volumes of data, as it can allow you to scale up your data ingestion process as needed. Additionally, automation can help to improve the reliability and robustness of your data ingestion process, as it can reduce the risk of problems such as data loss or corruption.
Use Artificial Intelligence
Using artificial intelligence (AI) in data ingestion can be especially useful when dealing with large volumes of data or when the data sources are varied and complex.
Some potential benefits of using AI for data ingestion include the following:
- Improved Accuracy: AI algorithms can be trained to recognize and correct errors or inconsistencies in data, improving the overall accuracy of the data.
- Enhanced Security: AI can help to secure data during the ingestion process by identifying and flagging potentially malicious data sources or activity.
- Greater Flexibility: AI-powered data ingestion can adapt to changing data sources and requirements, making it easier to incorporate new data into your systems.
There are a variety of AI techniques that can be applied to data ingestion, including machine learning, natural language processing, and image recognition. The specific approach that is best for your organization will depend on your specific needs and goals.
Establish Expectations and Timelines Early
Setting expectations and timelines early helps ensure that all stakeholders have a clear understanding of the project goals and timelines and helps mitigate the risk of delays or unexpected issues arising. By setting expectations and timelines early, you can also establish clear communication channels with stakeholders and ensure that everyone is on the same page throughout the ingestion process. This can help ensure that the project stays on track and is completed successfully.
Idempotence is a property of an operation or function where it will always produce the same result, no matter how many times it is repeated. In the context of data ingestion, this means that the process of importing data should produce the same result regardless of how many times it is run. This is important because it allows you to safely re-run the ingestion process if something goes wrong without worrying about ending up with duplicate data.
There are a few ways to ensure idempotence in a data ingestion process:
- Use a Unique Identifier for Each Record: This allows you to check for the presence of a record before trying to import it. If the record already exists, you can skip the import and avoid creating a duplicate.
- Use “Upsert” Operations: These are database operations that insert a new record if it doesn’t exist or update an existing record if it does. This allows you to run the ingestion process multiple times without creating duplicates.
- Use a Versioning System: By storing a version number or timestamp with each record, you can ensure that you only import newer versions of records and avoid overwriting existing data.
Implementing idempotence in your data ingestion process can save you a lot of time and headaches by eliminating the need to manually deduplicate data or fix errors caused by importing the same data multiple times.
Document Your Pipelines
Documenting your data pipelines helps in multiple ways:
- It helps you understand how your pipelines work: Writing documentation can help you to better understand the various steps involved in your data pipelines, which can be especially useful if you are new to the project or if the pipelines are complex.
- It makes it easier for others to understand your pipelines: If you are working on a team, or if you need to hand off your pipelines to someone else, clear documentation will make it much easier for them to understand how the pipelines work and what they are used for.
- It helps you to maintain and troubleshoot your pipelines: Having documentation in place can make it easier to identify and fix problems with your pipelines and to make updates or changes as needed.
- It can serve as a reference for future work: Well-documented pipelines can be a valuable resource for future projects, as they provide a clear overview of how the data was processed and can serve as a starting point for similar work.
To document your data pipelines effectively, you should include the following:
- A clear overview of the purpose of the pipelines and how they fit into the overall data workflow.
- Detailed descriptions of each step in the pipelines, including the specific tools and processes used.
- Any relevant notes on the data sources and outputs of the pipelines.
- Any assumptions or constraints that were considered when designing the pipelines.
- Any relevant code snippets or configuration files.
By taking the time to document your data pipelines, you can ensure that they are easy to understand, maintain, and reuse.
In this article, you learned about data ingestion best practices and why you should use them. By following these best practices, you can ensure the success of your data ingestion projects and avoid potential failures.
Getting data from many sources into destinations can be a time-consuming and resource-intensive task. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 40+ free sources).Visit our Website to Explore Hevo Data
Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag-n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Want to take Hevo Data for a ride? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.