The Benefits of Data Observability to SMBs and How to Unlock Them
Data observability is a relatively new discipline in the fields of data engineering and data management. While many are familiar with the longstanding concepts of observability and monitoring in enterprise IT networks and infrastructure, data observability has only really come into the spotlight in the last two years. However, it has managed to turn a lot of heads in that short time.
The uptake of traditional observability and monitoring solutions has been driven by the proliferation of cloud applications and infrastructure as organizations find it increasingly difficult to keep an eye on the growing number of systems they have deployed across various locations. Much in the same way, we’re now seeing this happen with data.
Having healthy data goes beyond simply understanding its quality.
Nowadays, real-time is king, and that requires solutions that can alert organizations to issues with their data before those issues are propagated into downstream applications and processes. This is where data observability comes in.
Small and mid-size businesses (SMB) require the extra support observability provides, as they have fewer human resources to deliver projects to support business decisions and operations, and therefore have less time to resolve issues with their data and pipelines.
It is clear that data observability solutions can bring tremendous benefits and time savings by enabling these individuals to be more proactive in their data management and engineering activities.
What is Data Observability?
Many IT and data professionals are familiar with the concepts of monitoring and alerting, either in data or other IT systems. The simplest form of this is perhaps data quality profiling, where analyses run on a daily basis, and users are alerted when certain rules are violated, or thresholds are crossed. Nowadays, these kinds of processes can be embedded into data pipelines so that monitoring and alerting occurs in real-time, giving organizations the capabilities to handle data in motion. Data observability, however, goes beyond simple monitoring and alerting and helps users to answer the why questions like, “Why has my pipeline execution failed?” or, “Why is there no data on my dashboard?”
Think of any enterprise data or analytics system as a car:
Monitoring is equivalent to the various sensors capturing important metrics about the health of the vehicle, perhaps the engine temperature, fuel level, tire pressures, etc., and making those metrics available to the driver via the gauges on the dashboard in real-time.
Alerting is represented by the lights on the same dashboard. These are illuminated to warn the driver of an issue where the health of the vehicle is below optimum, e.g., low fuel level or low tire pressure. These indicate to the driver that some light maintenance is required before things go very wrong, and the vehicle is no longer able to function correctly.
When the warnings are more serious and complex—such as an engine warning light or service advisory—drivers don’t have the ability to diagnose issues like this on their own. They must rely on a mechanic with specialist equipment to read the error messages and translate them into a solution to fix the vehicle.
This is what data observability brings to the table for organizations. Like a mechanic, it can take the alerts from the monitoring outputs and understand what has caused the issue. Perhaps by interpreting the error messages or by knowing how the vehicles systems are interconnected and how to put things right again. Taking the analogy one step further, data observability can also play the role of some car manufacturers, who can even collect data from vehicle sensors in real-time and use it to predict and prevent failures.
Data observability makes it possible to alert users of issues, inform them of the causes and predict upcoming failures. These alerts are sent in real time when, for example, data quality rules are broken. They can show the data lineage, point to where things went wrong and perhaps even flag if there are anomalous data volumes being ingested compared to previous runs. Having this level of insight into data operations is a game-changer for SMBs.
This article originally appeared on dbta.com. To read the full article, click here.
Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.
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