10 Major DataOps Principles to Overcome Data Engineer Burnout Simplified

on Data Engineer, Data Engineering, Data Warehouses, DataOps, ETL, ETL Tutorials • June 30th, 2022 • Write for Hevo

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If you’ve worked in the big data industry, you’ll probably relate to the survey from the Data kitchen. In some ways, data engineering is similar to software engineering, but data engineers have not adopted the best practices that software engineers have been perfecting for decades. When a problem occurs in data pipelines, data engineers are expected to fix it using ad hoc processes that simply do not scale.

One widespread issue is relying on the “heroism” culture. When there is a problem, people rush to help, but heroism is not a strategy. It is not scalable. It contributes to burnout and turnover. People who leave the company take their tribal knowledge with them, so burnout has a measurable business impact.

There is an endless stream of issues: customer requests, broken systems, and errors. One issue is that the data engineers are viewed as cost-cutters rather than value creators. People think of them as firefighters, and when there is a power outage, it is expected from them to work around the clock to fix it.

Before encountering the 10 Major DataOps Principles to Overcome Data Engineer Burnout, This blog will first introduce you to some general topics and then give you a few reasons why a Data Engineer burnout. Let’s begin.

Table of Contents

What is DataOps?

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DataOps is a set of practices, processes, and technologies that combine an integrated and process-oriented view of data with automation and agile software engineering methods to improve quality, speed, and collaboration while also promoting a culture of continuous improvement in the field of data analytics.

While DataOps began as a collection of best practices, it has now evolved into a new and distinct approach to data analytics. DataOps encompasses the entire data lifecycle, from data preparation to reporting, and acknowledges the interdependence of the data analytics team and information technology operations.

To monitor and control the data analytics pipeline, DataOps employs statistical process control (SPC). With SPC in place, the data flowing through an operational system is constantly monitored and validated to ensure that it is working properly. An automated alert can notify the data analytics team if an anomaly occurs.

DataOps is not associated with any specific technology, architecture, tool, language, or framework. DataOps tools promote collaboration, orchestration, quality, security, access, and usability.

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Data Engineering as a Profession

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A data engineer is an information technology professional whose primary responsibility is to prepare data for analytical or operational use. These software engineers are typically in charge of creating data pipelines that combine information from various source systems. They integrate, consolidate, and cleanse data before reorganizing it for use in analytics applications. They want to improve data accessibility and optimize their company’s big data ecosystem.

The amount of data with which an engineer works varies depending on the organization, particularly its size. The more complex the analytics architecture, and the more data the engineer is responsible for, the larger the company. Certain industries, such as healthcare, retail, and finance, require a greater amount of data.

Why do Data Engineers Burnout?

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Data Engineers wake up worried, thinking about the minor details they overlooked. They are chastised for failing to meet milestones. When the analytics team can’t turn on a dime, users roll their eyes. These issues affect everyone in the industry.

Rather than directing negativity at the enterprise, others, or oneself, data engineers can use workplace challenges as fuel to transform pain into system building. Burnout and a lack of data project success are not signs of personal failure or that the field is broken. It emphasizes the work that must be done to build systems based on automation and data toolchains.

Data Kitchen has polled 600 data engineers, including 100 managers, to learn how they are doing and how they feel about their work. The overall conclusion was that 97 percent of data engineers are burned out.

According to the survey, the following factors contributed to burnout:

  • The never-ending flow of blunders
  • Manual processes suffocate innovation.
  • A Consistent Flow of Half-Baked Requests
  • Finger-pointing and blaming
  • Data governance restrictions Policies

So to take things in a better way here are the 10 major DataOps Principles to Overcome Data Engineer Burnout.

10 Major DataOps Principles to Overcome Data Engineer Burnout

The 10 Major DataOps Principles to Overcome Data Engineer Burnout are as follows:

1) Establish Your System

The majority of data engineers in the world think of rapidly finishing every task on their to-do lists and moving on. It is a wrong sentiment to have for one of the pioneering jobs in the industry. The job of a data engineer is to not just check tick boxes off the tasks in their to-do list but to see the bigger picture of their job — to automate and reduce the length of their to-do list. The consensus of the industry is to swiftly do their job, and get home, but only a few see the bigger picture.

Thinking from a broader point of view: the job of a data engineer would not be to keep doing the same things every day like a robot. Data science is a magical science that aims to solve the issues we are facing today. In other words: a data engineer fixes problems (or bugs) facing them, not just for the time being, but for the future too!

Data science understands the need for present solutions that bless us over a long period. Thus came Dataops principles. With the help of DataOps Principles, data engineers have the strength and the ability to update our quality of life as humans in the future.

Implementing Dataops principles will liven up your data pipelines with end-to-end transparency and ease the level of observability for you to understand any hindrances that may occur in your code. You may also write test sequences to catch errors in the data. DataOps not only lets you automate governance in your organization but it can also automate manual processes in your pipeline; so you save up on that money too!

The possibilities that DataOps principles provide data engineers with are practically endless. DataOps principles aid in the fleeting establishment of foolproof, easy-to-understand, and centralized analytics for your workplace. The only question they need the answer to is whether their customers deem their data to be valuable. Working smart is the new working hard, and it’s all the easier with Dataops!

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 2) Automation is the Future

How can Automation be a part of DataOps principles? Ever since it has been introduced has been proven to be of massive value to the world. Having already been introduced to software engineering, it has made all of our lives easier! Making out time for the time to automate tasks is an old wives’ tale today!

Back in the day when automation wasn’t a thing, there would be a release engineer in every team who wouldn’t be seen as equal to his colleagues and developers. Today, that job title has seen a revolutionary change and is now termed a DevOps engineer. The majority of these companies have now shifted to automation and devote more than 25% of their staff to these processes as these are the processes that are valuable to organizations!

When we say automation, we don’t mean finding ways to speed up the pair of hands on the keyboard. The world is full of languages and tools, each with its own set of advantages and disadvantages. Automation is the system around the tools. The system creates on-demand development environments, performs automated impact reviews, tests/validates new analytics, deploys with a click, automates orchestrations, and monitors data pipelines 24×7 for errors and drift. These types of automation will increase analytics productivity and quality by a factor of ten.

Automation in itself is a system that creates an environment that is suitable enough to develop and perform automated processes like analytics, reviewing, click deployments, etc. Automation also acts as surveillance for your pipelines so they are always error-free, any time of the day! It has been said that this will improve the quality of life by at least 8-10 times.

3) Don’t be afraid to make mistakes!

What mistakes can you do in DataOps principles? And by mistake, we are talking about the errors in your code! Sure, they do not leave a lasting impression on everyone but it is not recommended to mask your errors or shy away from them. These errors are not to be forgotten and each one is a doorway to automation, as it allows you as a DevOps engineer to automate those errors and establish a system that keeps those errors at bay in the future!

4) Embrace the changes, don’t fear them!

Data engineers are overthinkers, like most of the DataOps principles are. We tend to be overcautious and in a subject that has always put making errors and getting shamed on everybody’s plates. Engineers ensure zero errors in their code and spend additional time on every analytic that is to be delivered which ends up them slowly putting up with the deliverables. This causes the stack of work to rise to the skies which gives the analytics teams everywhere a bad reputation for being unresponsive and slow.

What data engineers do not know is that they do not necessarily need to decide between speed and accuracy. When they automate this system, it tests and deploys newer analytics which is in the pipeline with production. If these pipelines have innumerable tests verifying their quality, the data team ensures that they are working as intended and everything is okay. There’s no fearing change if you are equipped with the right procedures and process!

5)  Meet All Ends

When does a project become ready for production? When will you be able to declare it? When time is limited, it’s tempting to tackle a task as narrowly as possible, toss it over the wall, and declare victory. If there are any side effects, it is someone else’s fault.

A product is usually labeled as finished when it can serve value in the hands of the consumer. But so many products pass through to our consumers without being finished because every step of the way, someone must have thought that it isn’t their problem to deal with and left it at that. Focusing on data as a whole product rather than narrowly defining projects helps to break down barriers between enterprise groups. When data is viewed as a product, the success of the customer is prioritized.

Data engineers define a finished product by exactly what it is. A product that gives value to the hands of a customer. The code is controlled by version and is parameterized to be recycled across different data environments. These pipelines have inbuilt checks to catch errors and they are tied with frameworks designed for observability. DataOps principles would do great, to look at the bigger picture and observe how every modification and change benefits the entire system.

6) Heroes save, data engineers protect

Being a hero takes time and effort. They are superhumans for a reason. We as humans do not have the physical or the mental capacity to be heroes every day. Instead, in reality, we are a step ahead and find a solution to the problem, to prevent it from recurring. There will never be a second time in DataOps! If these problems are dealt with on a one-time basis, it’ll be a devastating task to fix those errors every time they surface. It’s better to nip the problem from the bud and fix it, once and for all!

7) Look out for your future

What’s the future for DataOps principles? Engineers perform their operations, be it analytical or data, on the granular level, it is code. A sure way to generate more variants of this code is to extract it from someone else’s code, alter it to suit your purposes, and check that it can function on its own. It is not recommended to go by this route, as it leads the business to losses in the future.

For data engineers, anything that reduces their workload is a sign of victory. Teams of data engineers manufacture a standard set of components that can be used across several pipelines. They had vastly distributed systems and several tools. Grouping related abstract features into units that function. These units could be reused and when given APIs, they helped establish causes that spread while reducing losses. The reusability and the design scale were vital features that data engineers should never overlook when delivering customer data values.

8) Enforcing DataGovOps

How can DataGovOps benefit DataOps principles? The governance of data is of utmost necessity in today’s world. It needs to have command over the data but also has a superior motive of assisting the generation of value from data. It applies Dataops principles to govern, as Agile and DevOps pertain to product development departments.

Organizations need DataOps principles, DataOps principles need to be quick and certain in identifying who has access to their data. The establishment of access levels needs to be deployed so that the data gets utilized at its maximum value. Simultaneously, DataGovOps doesn’t have the luxury to take the mission lightly, so the data can’t be misused, which is strictly against the regulations.

It believes in governance-as-code. With automation in DataOps, automation consists of both control and creativity, in harmony working with each other. It establishes the needs of data governance teams in DataOps that aim to deploy control while not encouraging bureaucracy. DataOps principles focus on establishing change in these policies and deploying the automation plans that initiate and analyze governance, alongside reporting it.

9) Set Standards and Improve on Them

Data engineers implement their skills in analysis to derive the structural organizations they work in. Metrics for data engineering (and science) assist organizations to introspect themselves and improve based on feedback.

It is a shock that the time people are in meetings and the errors that reach the final draft and the long time it takes to change the analytics. These productivity numbers and error rates highlighted the way people function as a group and the value they create for customers.

10) Have one aim: Delivering Value to Customers

The significance of DeveloperOps has risen so much in the software engineering industry, yet a portion of these data engineers are oblivious to the ways Dataops principles can be a blessing in their workspace. These are myths that they believe that these systems will be a hindrance to their data teams.

Think of it this way, data operations work like brakes on a bike. A bike has brakes not because it has to go slowly, but so that it can ride fast but in a safe manner. The automation and engineering processes are shown here are to assist data engineers to speed up their processes. There is an initial set-up time that could take a few extra hours but it makes up for the hours the organization saves in the long run.

Conclusion

Back then, people ran into similar issues with the way software is built, which led to DevOps and Agile methodologies. Now we’re having the same debate about data and analytics. Data organizations have large teams that must collaborate in the face of extreme complexity.

This blog has introduced you to DataOps and Data Engineering along with the 10 DataOps principles to avoid getting burned out.

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