Data observability has quickly become a critical piece of the data engineering puzzle. By definition, observability is “the degree to which something can be observed.” In data science, this means having visibility into all aspects of the data pipeline to detect issues and correct them in near-real-time. Six key roles must be filled to achieve effective data pipeline observability.

Data Ingesting

The data collector is responsible for pulling data from various sources and storing it in a central location. This could be a database, data warehouse, or data lake. The data collector needs to have a comprehensive understanding of the data landscape and be able to collect both structured and unstructured data.

The data ingestion role is responsible for taking the raw data collected by the data collector and transforming it into a format that the rest of the data pipeline can use. This includes things like cleaning up insufficient data, normalizing data types, and creating schemas.

Data Processing

The data processor is responsible for transforming raw data into a format that other applications can consume. This may involve ETL (extract, transform, load) processes or data cleansing. The data processor must have a thorough understanding of the data acquired and be capable of processing it in an effective manner.

Data Analyzing

The data analyzer is responsible for generating insights from the processed data. This may involve running queries, building models, or creating visualizations. The data analyzer must be able to communicate its findings to members of the team effectively so any abnormalities can be recognized right away.

The data analyst is also responsible for monitoring the data pipeline and detecting issues. This may involve watching for unusual spikes in data volume or errors in the process.

Data Monitoring

The data monitor is responsible for ensuring that the data pipeline is running smoothly and detecting any issues that may arise. This may involve setting up alerts, creating dashboards, or conducting performance analysis. The data monitor needs to have a deep understanding of the data pipeline and be able to identify potential problems.

Data monitoring is not just about detecting issues. It’s also essential to track the data pipeline’s performance and identify any areas that need improvement. The data monitor should be able to identify bottlenecks and potential points of failure. Additionally, the data monitor should be able to track the progress of the data pipeline over time and measure the effectiveness of various changes.

Data Storing

The data store is responsible for retaining the data over a long period of time, which may include using a cloud storage service or making backups. The integrity of the data must be preserved, and it needs to remain accessible when required.

A data warehouse, data lake, or other storage solution may be required. The data store must also be able to handle a large amount of information and support a wide range of applications.

Data Query

The data query component is responsible for making the data accessible to other applications. This may involve creating an API or setting up a data connection. The data query component must be able to handle high volumes of requests and provide fast responses.

It is also important to have a detailed understanding of the data landscape in order to select the appropriate data sources.

Conclusion

These are the six key roles that need to be filled in order to achieve effective data pipeline observability. With data engineering advancing every day, it’s important to know the roles of data observability to ensure success in your own framework. Thanks for reading.