Autonomous Cloud Data Pipeline Control: Tools and Metrics

Errors that get seeded in the data as it flows through the data pipeline, propagate throughout the organization and are responsible for 80% of all errors impacting the business users. High-quality data is essential to the success of any business or organization. It’s important to monitor your data pipeline to guard against missing, incorrect, old/not-fresh, or duplicative data. To do so and have an error-free data pipeline, you need to know what metrics to measure and what data pipeline monitoring tools to use. 

Here we share our experiences working with Fortune 2000 companies on the qualities to look for in a data pipeline monitoring and control tool and metrics to monitor. 

Quick Takeaways

  • Poor data quality can result in operational chaos, poor decision-making, and lost profits.
  • Data pipeline control protects against errors and helps clean bad data in your pipeline.
  • The four key data pipeline monitoring metrics are latency, traffic, saturation, and errors.
  • A data pipeline monitoring and control tool must be granular, persistent, automatic, ubiquitous, and timely.

Why is the Quality of Your Cloud Data Pipeline Important? 

All data collected by your company processes through a data pipeline. A data pipeline is simply a set of processes you use to collect data from various sources, transform the data into a usable form, and then deliver that data for analysis. Data can flow through the pipeline in batches or as a continuous stream of information. 

Understanding the data pipeline is necessary to guarantee the data quality your business needs to operate effectively and efficiently. Poor quality data introduced at any pipeline stage can result in poor decision-making, operational chaos, and reduced profit. (According to Gartner, poor data quality costs organizations an average of $12.9 million a year.) 

the data pipeline

Unfortunately, data pipelines can be subject to several issues that put the quality of your data at risk. Not only can bad data enter the pipeline from the original source, but data can be compromised at any stage of the flow. Data leaks are a common problem, with pipelines dropping data when they get out of sync (Cloud Data Pipeline Leaks: Challenge of Data Quality in the Cloud”, Joe Hilleary, Eckerson Group). 

For all of these reasons, monitoring all data as it flows through the pipeline helps ensure the integrity of that data. From the initial source to final delivery, it’s important to monitor the data to make sure that it is intact and accurate and that no errors creep into the data. This is done by providing visibility into the entire process and examining the quality of the data compared to a series of key metrics. 

What is Data Pipeline Monitoring and Control?

Data pipeline monitoring is a set of processes that observe the data flowing through the pipeline and control the flow when incidents are detected and data quality is compromised. It monitors both the pipeline and the data flowing through it.

A data pipeline monitoring system helps you examine the state of your data pipeline, using a variety of metrics and logs. By constantly observing data in the pipeline and the flow of that data, the system can catch data errors as they happen – and before they affect your operations. 

Advanced data pipeline monitoring tools use artificial intelligence (AI) and machine language (ML) technology to sense changes in the data’s fingerprint. It operates automatically to find and correct data errors and notify you and your staff of any issues in the pipeline process.

The best data pipeline monitoring and control tools will do the following:

  • Detect data errors as they occur
  • Immediately notify staff of data errors 
  • Automatically isolate or clean bad data
  • Alert staff of any system outages or incidents
  • Identify any systemic data-related issues
  • Generate data quality reports

Without data pipeline monitoring, the risk of bad data infiltrating your system is very high. Some sources estimate that 20% of all data is bad. With data pipeline monitoring, you can be assured that bad data will be immediately identified, and that you’ll be notified if any errors are introduced in the pipeline process.

Understanding Cloud Data Pipeline Monitoring Metrics

Essential to monitoring your data pipeline are four key metrics: latency, traffic, errors, and saturation. Tracking these data pipeline monitoring metrics will ensure the highest data quality at the end of the pipeline.

data pipeline monitoring metrics

Latency

Latency measures how much time it takes to fulfill a given request. In a typical data pipeline, requests should be handled in a matter of seconds. The greater the latency, the less efficient your data pipeline.

Traffic

Traffic measures how many data monitoring requests are received over a specified period. This is often measured in terms of requests per second. Your data pipeline must be able to handle your traffic load with a minimal amount of latency. 

Saturation

Saturation measures resources allocation for your data pipeline system. A saturated pipeline, typically caused by higher-than-expected traffic, will run slower than normal, introducing greater latency into the process. 

Errors

Errors can be problems with your system or problems with individual data points. System errors make it difficult to process data and fulfill requests. Data errors can result from incomplete data, inaccurate data, duplicative data, and old data. 

Choosing the Right Cloud Data Pipeline Monitoring and Control Tools

It’s important to choose a data pipeline monitoring and control tool that not only identifies and cleans bad data, but also integrates with the way your company’s specific data pipeline operates. 

Five Essential Qualities

A robust data pipeline monitoring and control tool should possess the following five essential qualities:

five essential qualities of a data pipeline monitoring tool
  • Granular, to identify specifically which microsegment of your data are the issues occurring
  • Persistent, to monitor data over time and also such that the results are auditable in the future
  • Automatic, using AI and ML to replace manual monitoring
  • Ubiquitous, to monitor data throughout the entire pipeline
  • Timely, so alerts are generated in real time when errors are identified and data flow is stopped when required

Ask These Questions

Taking those essential qualities into account, ask the following questions of any tool you’re considering:

  • Does it work with both batch and real-time data processing?
  • How much data can it monitor and control during a given period? 
  • How quickly can it monitor a given amount of data?
  • Can it detect when data is flowing?
  • Can it detect if data is complete?
  • Can it detect if data is accurate?
  • Can it detect if data structure or schema has evolved from the past?
  • Can it detect if the actual data itself has been changed during the pipeline process?
  • Does it operate autonomously with a minimal amount of human intervention?

If you can answer yes to all of these questions, you have a data pipeline monitoring and control tool that can do the job for your organization. 

Let DataBuck Monitor and Control Your Cloud Data Pipeline

When you want robust and highly accurate monitoring of your firm’s data pipeline, turn to DataBuck from FirstEigen

DataBuck is an autonomous Data Quality management solution powered by AI/ML technology that automates more than 70% of the data monitoring and control process. It can automatically validate thousands of data sets in just a few clicks and constantly monitor and control the data fed into and flowing through your data pipeline.

Contact FirstEigen today to learn how DataBuck can autonomously monitor and control data pipelines.

(1) “Cloud Data Pipeline Leaks: Challenge of Data Quality in the Cloud”, Joe Hilleary, Eckerson Group https://firsteigen.com/2022/01/cloud-data-pipeline-leaks-challenge-of-data-quality-in-the-cloud/

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