Unravel Data: What are the top challenges faced by DataOps teams?

By By Kunal Agarwal CEO and co-founder, Unravel Data
DataOps is a culture - a methodology deployed to bring the data and operations teams closer together in order to reduce cycle times and make analytics r...

DataOps is a culture - a methodology deployed to bring the data and operations teams closer together in order to reduce cycle times and make analytics run smoother.

In essence, DataOps orchestrates, monitors and manages the data factory. Without it, the most valuable resource organisations have at their disposal is susceptible to being wasted. As a result, possessing large volumes of data can cause more problems than it solves. It is for this reason that DataOps exists - to solve the cultural challenges that inhibit organisations from making the most of their data. Let’s outline the key problems that DataOps teams face and how they can be prevented from derailing the show.

Lack of visibility of data usage

Due to the high-value nature of data, businesses often strive to collect as much as possible. This is to build the most valid possible sample of the subject they are analysing. While more data often leads to better insight, there is more work to be done across a wider range of information to get the data ready for use and aligned with other data sets. This can lead to a lack of visibility of where the data is being used, how and with what level of compliance.

When analysing large volumes, clusters are created to make handling said data more efficient. A cluster is similar data, grouped together to make it easier to handle. As such, data within the same cluster share more similarities than to data in different clusters. However, while this helps with analysing the data, often, having full visibility over all cluster can prove difficult - making it slow to resolve issues and optimise performance across data sets. For example, it is not always inherently clear which applications are causing cluster usage (CPU, memory) to spike, or if various clusters are actually being used.

Not having a good understanding of pipelines

A data pipeline is technology that eliminates many manual steps from data processing and enables a smooth automated flow from one stage to the next. Automation is one of the most powerful tools in the DataOps team’s playbook. Automating the processes involved in extractions, transforming, combining, validating and loading data for analysis increases effectiveness and efficiency.


However, when a DataOps team does not have a full understanding of how their data pipeline works, this can negate all the benefits that it offers. For example, a data pipeline that needs to be completed by 4am every morning is consistently being delayed and completes two hours behind schedule. Without knowing the in-depth workings of the pipeline such as SLAs and the type of pipeline you are working with, whether it be batch, real-time, could native or open source, you cannot fully benefit from the data.

Due to the complexity of modern data pipelines, no team should have to try and figure it all out on their own.

Unable to control/manage runaway jobs

With many processes and jobs running simultaneously across different clusters and pipelines, controlling and managing everything is no easy task. It is not always possible to foresee exactly how long or intensive all jobs will be, and this leads to some taking far more resources than needed or expected, affecting overall clusters and starving other applications.

As well as helping with pipelines, a primary benefit of automation is managing jobs and resources. Using AI and advanced analytics, issues across the stack can be identified quickly so the DataOps team can take action with individualised recommendations and automation to improve and optimise the performance of all ongoing jobs. This also applies to clusters. Configuration parameters can be automated and consistently adjusted to improve the overall application performance on an ongoing basis taking all other ongoing tasks into consideration with a full view of the stack.

How to quickly identify inefficient applications that can be reviewed and acted upon

Often, the cause of performance degradation can be deep-seated and well-hidden. Having to quickly triage and understand root-causes is extremely difficult without having deep Hadoop Application expertise, for example. This can cause lengthy and time-intensive troubleshooting that isn’t an efficient use of the team’s time resources.

While we have previously spoken about monitoring performance and making adjustments to optimise - for issues like degradation -  a more in-depth solution is required. Again, AI and advanced analytics are at the core of the resolution. The specific ways to enhance performance is: clear-cut code to use, settings to tweak and resources to reallocate. Full-stack solutions can show exactly what impact these changes will have on performance so can have a clear view of the direct implications of actions taken.

By helping to track, diagnose, troubleshoot and optimise all the systems - and all the applications that run on them - the complexity of driving reliable performance, and in turn real value, can be realised.

Through managing application performance, businesses are able to gain full visibility and understanding across the modern data stack. This results in stable optimised performance for greater productivity within ops and dev teams, as well as lower costs while the data is providing greater value to the business. To put it simply, it’s the tool kit to troubleshoot and tweak all parts of the analytic pipeline. As such, application performance management technology used by DataOps teams can cut through the complexity and help to find the insights, the answers and the solutions.

With new technologies, it is possible to also take it one step further. A correlated end-to-end understanding of the full data stack can be achieved by using AI-powered DataOps - a fundamentally different approach that harnesses intelligence, guidance and automation to make data work in the critical applications that power businesses. If DataOps is left unattended and underappreciated, this can lead to slow processes, inefficient jobs and wrongly configured pipelines, with the team taking on ever-more responsibilities. With the introduction of AI, advanced analytics and automation to the DataOps arsenal, teams can begin to fully operationalise the data they have at their disposal without the pain.



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