The Art of Data Ingestion

Data ingestion refers to the process of collecting, importing, and moving data from diverse sources to a centralized storage system. It could be a data lake , a data warehouse or any other storage architecture. Ingesting data is a critical phase in the data engineering workflow, as it sets the foundation for successful data processing and analysis. This is the stage where a data pipeline is fully designed despite being defined all through the data engineering cycle. its effectiveness can significantly impact the quality and reliability of your downstream data processes.

By asking the right questions and addressing key considerations, data engineers can build robust and efficient data ingestion pipelines. Whether dealing with structured, semi-structured, or unstructured data. Some of the important questions to ask yourself then , during this phase include:

What is the use-case of the data being ingested? Understand the purpose and intended application of the data being collected and the benefit it holds to the company. This also helps you to avoid having multiple datasets in storage(hence increasing on storage cost) and they are not being used by your team in any way.

Is your source and the systems ingesting the data reliable? Check the quality of the data being ingested ie missing values and inconsistencies and ensure the data is fit to answer questions that the downstream use might have.

Does the data need any transformation before reaching its destination? This entirely depends on the purpose of the data and the downstream users as well. Check if there are any tables that need to be joined or cleaned.

What is the frequency at which the source data changes? . Consider implementing CDC(Change Data Capture) to capture and replicate only the changes in the data rather than ingesting the entire dataset again hence creating multiple versions of the same thing.

How Will You Handle Data Errors and Failures? Plan for ingestion failures and any recovery mechanisms like backups in case you encounter issues while ingesting.

What is the volume of the data being ingested? Ensure the systems receiving the data are capable of handling the amount of data coming in and even more in case the data scales in future.

What format is the current data in ? Plan on how to convert the data to a format that can be accepted by downstream users in case it is not.

Is the data bounded or unbounded? If the data is coming in at real time(bounded) it will be processed differently than data that is coming in batches(unbounded). Also consider how downstream users expect to receive the said data ie in batches or hourly, weekly, monthly.

What Ingestion Tools and Technologies Are Suitable?: Select the appropriate tools and technologies for data ingestion, taking into account factors like data volume, velocity, and variety. Ensure the tools selected give you the most optimum way of ingesting the data while considering the cost.

Mastering the art of data ingestion provides your data pipeline with a strong foundation, allowing you to proactively mitigate potential challenges while guaranteeing that your data remains clean, precise, and readily available to downstream users. By consistently asking the right questions during the data ingestion process, you not only enhance the reliability of your data but also empower your organization to make informed decisions and derive valuable insights from this invaluable resource.