Organizations in every industry are becoming increasingly dependent upon data to drive more efficient business processes and a better user experience. As the data collection and preparation processes that support these initiatives grow more complex, the likelihood of failures, performance bottlenecks, and quality issues within data workflows also increases.
Preventing these issues is critical for ensuring the production of high-quality datasets (as is the timely identification of such problems when they do occur). Visibility into pipelines, and the data they process, plays a big role in this effort. Keep reading this article for a primer on:
- Data pipelines and related challenges
- How implementing end-to-end observability enables more efficient and effective data workflows
What is data engineering?
Data engineering is the practice of designing and building the systems used for processing and managing data. These systems can include procedures for collecting, aggregating and transforming data (and more) with the goal of producing quality datasets for use in data-driven initiatives. Together, these processes form the data pipeline.
Data engineering functions can be taken on by those in various engineering roles within the organization.
Challenges within data pipelines
Over the past decade, data workflows have grown significantly in both importance and complexity. This is due to both:
- Increasing business demand for effective data-driven applications
- A remarkable growth in the volume of data generated
With that, addressing the challenges associated with building and managing data pipelines has become more critical. Let’s take a closer look at a few of the common issues that plague data pipelines.
Failures and performance issues
Failure is an obvious and major concern when it comes to any operation that depends upon software and infrastructure — data pipelines are no exception. Data pipelines can experience failures for a multitude of reasons, including:
- Data sources that are unavailable
- Corrupt input data
- Issues with infrastructure
- And more
In addition to failures, lackluster performance is also a common problem within data pipelines. Consider a scenario in which a data transformation process is experiencing slowness. This can occur for a variety of reasons; for example, it could be that the procedure does not have the resources it needs to be able to process data in a timely manner.
No matter the cause, you’ll need to identify and quickly resolve bottlenecks within data pipelines, otherwise as they will slow the delivery of the output needed to drive key business initiatives.
Data quality problems
If the output of a data pipeline does not meet expectations and is not usable by the business — maybe the data is incomplete, inaccurate, etc. — then the data engineering team may be facing an issue with data quality. This type of problem can be complicated to resolve and require heightened visibility into the ways the data was processed.
Observability strategies for data workflow management
As with the development of traditional software applications, many of the problems within data pipelines can be caught through testing. Still, it’s pretty much a guarantee that issues will arise in production.
In many cases, observability within data pipelines can help to lessen the impact of incidents. Observability is defined as “the ability to measure the internal states of a system by examining its outputs.” In this case, observability gives data engineers greater visibility into:
- Pipeline performance
- The data being processed
This observability accelerates the process for recognizing trouble spots within pipelines, since it provides engineers with the information and insights to identify the existence of an issue and begin to narrow the path for root cause analysis.
Let’s take a look at a few strategies for making a data workflow as observable as possible.
Performance monitoring & log analysis
Observability and monitoring are not one and the same. That’s why any effective strategy for end-to-end observability must contain a strategy for monitoring. In the realm of data pipelines, this includes monitoring for key metrics that provide a deeper understanding of process performance.
For organizations leveraging batch processing to accomplish tasks within a data workflow, the length of time that it takes for the process to complete is critical to monitor. A long-running batch process can indicate a performance problem, yes, and it can also have an impact on the execution of dependent jobs and threaten the delivery of output data to the applications and personnel who rely upon it.
Additional metrics to track for batch processing include:
- Batch sizes
- Occurrences of exceptions
- Resource utilization
For stream processes, you'll want to track:
- Resource utilization
- Error rate
You’ll also need to ensure that proper personnel are being notified in real-time when the performance of jobs within the pipeline is being impacted. By establishing a baseline expectation for the metrics being collected and leveraging software that allows for the configuration of automated alerts, you’ll reduce the time it takes for data engineering teams to identify the existence of performance issues. So, if a process exceeds its expected runtime or experiences an above-normal error rate, data engineers can analyze it immediately.
Moreover, the ability to correlate these metrics with log data provides engineering teams with a more efficient path for analysis by helping to provide context that narrows the search for root cause, thereby increasing the chances of resolving the problem quickly (and thus limiting the impact downstream).
Data lineage for data quality
When building observability into data pipelines, it is critical to enable visibility directly into data processing itself. No matter how efficiently a pipeline performs, if the output is inaccurate, incomplete, or otherwise unusable, then it’s all for naught. By implementing data observability, engineers will have an easier time pinpointing the location of issues within the pipeline that are resulting in poor data quality.
One way in which data can be made more observable is by implementing data lineage. Data lineage allows engineers to:
- Identify the source of data records.
- Trace the records’ transformation history through a data workflow.
Data lineage plays an important role in enabling organizations to trust and rely upon the output of a data workflow. In the case of an unexpected end result, it’s now much easier for engineers to efficiently examine the data’s path to identify the problem in the pipeline.
To simplify this process, use observability tooling to establish data lineage. These tools enable teams to gain a more complete understanding of how the resulting output was constructed by allowing engineers to dive into the operations that took place at each step in the workflow.
Additionally, monitoring datasets for freshness and monitoring data quantity can assist in ensuring completeness and reliability. Instances of outdated or missing data are telltale signs of problems within a data workflow. Making this information readily available allows for action to be taken at the earliest possible point when discrepancies are present.
Finally, tracking schema changes within databases is essential. This allows organizations to monitor who is making database changes and why, thereby reducing the likelihood of modifications that will negatively impact applications and users who depend on the data stored in these tables.
Observability facilitates better data engineering
When data engineering teams build data workflows with observability in mind, you’ll have the information necessary to efficiently improve performance and data quality.
Observability provides engineers with a heightened level of visibility into their data pipelines, allowing them to quickly identify areas of concern. In doing so, root cause analysis can be executed in a more targeted manner. In other words, by enabling more rapid identification of problematic locations in the pipeline, incidents can often be resolved in shorter time frames. This helps ensure that data pipelines meet their service level agreements (SLAs) while preserving the viability of the organization’s data-dependent applications.
Moreover, by adopting practices that increase visibility into pipeline processes and data quality, data engineering teams gain insights that can assist them in continuously improving their workflows as they evolve. Over time, this will result in healthier, more resilient pipelines that are less susceptible to failures.
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