Saturday, June 22, 2024

How to succeed as a data engineer without the burnout

Programming LanguageHow to succeed as a data engineer without the burnout


When building and maintaining a modern data platform, the pressure on data engineers from businesses can be immense. The key to success in this challenging environment isn’t to work even harder; it’s to be smart about what you work on, and how it is implemented.

By embracing the foundational principles of DataOps—including a well-planned modular infrastructure, proactive monitoring, robust data governance, resilience, and a collaborative learning culture—engineers can ensure data platforms of any size run efficiently and smoothly, with minimal maintenance effort and clear business value.

This guide outlines a series of best practices designed to make the lives of data engineers less stressful by fostering an environment where systems run seamlessly, maintenance is predictable, and value delivery is consistent. The techniques discussed not only make data platforms more performant and valuable from a business point of view, but also significantly reduce the risk of burnout.

The first step towards making your life as a data engineer smoother in the long term is aligning your data platform with the specific needs of the business from day one. Start with an assessment of the needs of your business to get a thorough understanding of what kind of data your platform will be expected to handle. Specifically focus on the three Vs of data: volume, velocity, and variety of data—these are the crucial parameters that will dictate the broad strokes needed for your infrastructure.

For example, large volumes of generated data will require suitable storage that can scale, as well as efficient data processing. Big data technologies such as Hadoop and cloud-based storage solutions like Amazon S3 are designed to handle large volumes of data with scalable storage and distributed processing.

A high velocity of data production can be well handled by event buses like Apache Kafka and Amazon Kinesis, which can power real-time analytics, monitoring, fraud detection, and other use cases where immediate data processing is essential. Data lakes support the high-speed ingestion of data in its native format, offering a flexible solution for managing real-time and historical data across various domains.

Finally, if you have a lot of variety, such as a mixture of structured, semi-structured, and unstructured data, you will need a storage and processing framework that is highly flexible. Data lakes again come into play here by allowing the storage of all types of data in a single repository, which is advantageous for analytics across diverse data sets. Data lakehouses merge the flexibility of data lakes with the management features of data warehouses, providing a unified platform that supports various data types and analytics in a more structured manner.

Talk to stakeholders across different departments to gather insights into how data is collected and processed and get to grips with the ultimate purpose of data collection. With this kind of preparation, you’ll be much better positioned to build the right kind of infrastructure for your data platform from the start, saving you potential headaches down the line. (Keep in mind: it is still important to do this kind of ‘field research’ to understand the needs and goals of stakeholders even if you are inheriting and improving on an existing system rather than building it from scratch.)

For instance, a retail company may deal with large volumes of transactional data daily (volume), requiring real-time analysis to adjust pricing or inventory (velocity), and manage a wide range of data types from sales figures to customer feedback (variety). Engaging with people from sales, marketing, and customer service can provide insights into the nuances of data collection and processing, such as the need for integrating point-of-sale systems with online sales data and customer interaction records.

You’ll want to think carefully about the underlying basis of your infrastructure, too—possibly using cloud-native solutions and/or containerization. You can leverage the flexibility of the cloud to scale resources according to your operational needs, while containerization, for instance, encapsulates your applications in a manner that makes them portable and environment-agnostic. Both these approaches can significantly diminish the intricacies and burdens traditionally linked to overseeing infrastructure. These can unlock the potential for vastly improved scalability of your platform, as well as making it easier to manage.

That said, there are some challenges associated with cloud-native solutions. Chief among them is support for legacy applications, which may be critical to business operations but may not transition smoothly to the cloud without complex and costly refactoring. Sectors like healthcare and finance also face stringent data protection and privacy regulations that can complicate the shift to cloud services. Additionally, there are some highly specialized scenarios requiring very low latency where traditional on-premises solutions might still be preferred. And while containerization offers benefits like application portability and efficiency, managing container orchestration and ensuring security within containerized environments can introduce a learning curve for those new to the technology.

Adopting a strategy of working smarter rather than harder is essential for reducing costs in terms of both money and effort—as well as laying the groundwork for sustainable growth and faster decision-making. On a personal level, this means organizing your day so you spend the least amount of time on repetitive tasks and safeguard larger chunks of focused time. One example: master your command line skills and the shortcut keys on your IDE of choice.

When I was one of only two data engineers serving two on-premise Hadoop clusters to twelve data scientists, I used a very simple rule of thumb to make my life easier at work: automate any action I found myself doing twice. Instead of typing the same series of commands over and over into the terminal, I spent a little time writing a simple script to do the same task. This way, I cleaned up the routine, repetitive tasks from my day and created myself more space to maneuver.

On a company level, this means automating repetitive tasks across the data lifecycle, from ingestion and transformation to testing and deployment. Make sure to master the principles of Continuous integration and delivery (CI/CD) for development, testing, and deployment. CI/CD automates integrating code changes from different contributors and deploying them, ensuring that processes are streamlined and consistent. This approach will make it easier to catch errors quickly, reducing manual checks, while also improving the time-to-market of new features.

Monitoring and optimizing the performance of your data platform is another critical practice. You should monitor on two levels. First, keep an eye on the health of your infrastructure and major processes (like ingestion), with instant alerts on failures or degradation. Second, keep track of the use of storage and computational resources to spot inefficiencies, especially at stakeholder-facing points (for example: the top 50 most inefficient queries running on top of BigQuery dashboard). Consider running bi-weekly session with the analytics team to see which queries can be optimized.

Regular assessments like these help identify bottlenecks and areas needing improvement, so you can be targeted with your valuable troubleshooting time. Use monitoring and analytics tools to pinpoint high-impact issues—like common resource-intensive queries—and look to apply fixes at the source so as to avoid issues downstream.

You should also get familiar with using infrastructure as code (IaC). The past ten years has seen a growing ‘shift left’ as more engineers establish their own infrastructure through automation. Tests are moved ‘to the left’—i.e. earlier—on the project timeline, and so setting up the infrastructure to run said tests is increasingly the responsibility of data engineers. IaC tools have thus become indispensable for avoiding bottlenecks, errors, and manual tasks during high-pressure times, allowing engineers with varying levels of experience to modify infrastructure with guidance from seasoned DevOps professionals. This approach has become more or less standard practice in large-scale software development, ensuring that infrastructure evolves in tandem with the application for more resilient and scalable systems.

In my career, I have mainly used Terraform, which has been growing into an industry standard (albeit with recent licensing changes that pushed some towards an open-source fork). Services like this feature a wide array of providers, encompassing all the major cloud services. It also supports container technologies such as Kubernetes, which will speed up development, collaboration, and system reliability as well as ensuring your platform is free from vendor lock-in. Secure, compliant and efficient modules can be crafted by experts or sourced from the an expansive registry, then reused across your data platform. This approach ensures consistent, repeatable environments but also refines the deployment process, reducing manual labor and minimizing errors. In addition, designing your data infrastructure with modularity in mind will allow you to scale and adapt to evolving business needs and data complexities—without sacrificing performance or efficiency.

IaC platforms will allow you to precisely define infrastructure through declarative code which can automatically organize resource dependencies. Make sure you use modules, which serve as abstracted blueprints for assembling multiple connected resources (rather than focusing on specific hardware components). This will make scalability easier and clearer for everyone. You can use a state file as a universal source of truth, and avoid conflicts with version control and pull request reviews. Many IaC tools even allow you to test for potential errors and project the financial impact of changes before deployment.

It’s essential that the data in your platform is kept high quality: accurate, consistent, and reliable. Use data validation, continuous monitoring, and robust error handling practices to sustain your users’ trust in your data ecosystem. Establishing a data catalog is a critical step, allowing for greater discoverability with descriptions, tags, and categories. Various tools facilitate this, including Apache Atlas, Collibra, and Alation for general use, with Google’s Dataplex Data Catalog and AWS Glue being optimal choices for those on GCP and Amazon’s cloud, respectively.

You can perform regular data audits to identify and eliminate obsolete or redundant information. You’ll also want to monitor data access patterns with a tool like Varonis DatAdvantage. Make sure to establish data retention rules, enabling the automatic transfer of older data to cooler storage solutions or its permanent deletion over time; all cloud platforms offer some sort of built-in functionality here. Access rights can be managed through in-house tools or augmented with specialized third-party authentication solutions like SailPoint and Okta. Finally, keep a close eye on the kind of data that gets into your platform with automated ingestion checks. Tools like DataCleaner offer off-the-shelf solutions for data cleansing, though you can make your own custom scripts too.

At the core of a stress-free approach to data engineering lies the principle of resilience. In an environment as dynamic as this, downtime is a given—but being unprepared need not be. Use the idea of defensive pessimism by planning for the worst-case scenario and working out from there. Design self-healing data pipelines with protections against failures at every stage to cut down the need for emergency fixes—a notorious drain on your engineers’ time. Redundancy and failover mechanisms, as well as real-time monitoring, will enable your platform to autonomously recover from disruptions, calling for human intervention only when absolutely necessary. This also reduces the stress tied to unforeseen failures, freeing engineers to dedicate their energies to innovation over constant troubleshooting.

Have a first responder routine is in place: this is a weekly shift within the data engineering team, when one team member is fully dedicated to run morning monitoring checks, answer stakeholders questions, troubleshoot, and put out minor fires. You should streamline communication with stakeholders: for instance, by agreeing where they can ask questions, the expected timeframe of response, and arranging where system health updates will be published. This should prevent the rest of your data engineers from having their focus interrupted and make sure that fire-fighting is evenly spread across all team members.

Adhere to best practices in data platform design to lessen the impact of data corruption, including making data immutable through processing stages with snapshots and replication. Have your system automatically check data quality at various stages. Of course, this is not possible at every stage: you need to consider the computational demands. But even minimal checks are preferable to none. When issues are detected, avoid the temptation to merely address symptoms downstream, which could lead to more significant problems later; instead, do a thorough root cause analysis and apply a fix at the source where you can.

When things do go wrong, make sure you have a robust disaster recovery strategy with clear first-aid documentation to ensure readiness. You’ll need clear communication protocols to avoid people wasting time figuring out who to contact. Practice executing your strategy with fire drills using chaos engineering tools to simulate real-world outages and test your response. And in the aftermath of a real incident, get into the habit of conducting a post-mortem analysis so you can learn from the event and making necessary adjustments to prevent future occurrences.

Adhering to industry standards and regulatory requirements is very important for keeping stakeholder trust and protecting sensitive information. A chain is only as strong as its weakest link, so opt for a comprehensive defense strategy with security mechanisms at every layer, rather than relying solely on perimeter defenses like firewalls. That said, keep usability in mind and strike a careful balance, focusing most of your attention on protecting the parts of your pipeline that are truly critical.

Incorporate robust data governance and compliance measures into your platform’s design early on. Define clear policies, procedures, and controls to manage data effectively, ensuring your platform complies with regulatory standards like GDPR (or equivalent). That way, you can avoid the nightmare of last-minute adjustments and ensure compliance throughout the data lifecycle.

When it comes to personal data, consider minimizing its storage unless absolutely necessary for your business operations. You can often, for example, anonymize personal data or use aggregation techniques to mitigate risks associated with storing identifiable data. And if there is a data breach, have a clear action plan for a swift and effective response. Here it’s crucial to encourage a culture of openness: team members should feel they can report issues without fear of reprisal.

Central to all these strategies is fostering a company culture that views mistakes as opportunities for learning rather than reasons for punishment. In training, and outside it, continually reinforce the value of openly discussing and learning from errors. This also helps encourage experimentation, allowing team members to delve into new ideas, techniques, and technologies which can keep your data platform cutting-edge.

Keeping your team abreast of the latest industry trends, tools, and technologies is crucial for driving innovation and enhancing the capabilities of your data operations. A culture that fosters a growth mindset not only propels your team forward but also ensures that your operations are always cutting-edge. Sharing knowledge, experiences, and best practices among team members is vital. By facilitating this exchange, for example, via “Lunch and Learn” sessions or demos, you encourage collective growth, diminish knowledge silos, and bolster innovation within your team.

Finally, keep in mind that there’s no point building something amazing if no-one uses it properly. While you build and manage your data platform, make sure to share your knowledge and bring everyone else along with you. Encourage information sharing and collaboration across teams, and train internal stakeholders to effectively use the platform you build. Once they understand this, they can start to generate the insights that can empower the entire organization to make informed decisions—ensuring that the hard work of data engineers is properly valued and recognized.

Overall, it’s important to remember that a great data engineer doesn’t just manage data: their job is also to improve how data is managed with smarter and more efficient methods, while making it visible and usable for the rest of the company. Keep these principles as your compass, and let them guide you towards solutions that are not only technically sound but also meaningful and impactful. It all adds up: every small improvement you make is a step towards a more manageable workload and a more effective platform. Take pride in helping businesses understand and leverage their data—and use smart strategies to avoid burning yourself out along the way.

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