Companies should consider adopting data solutions that optimize their effectiveness to overcome said challenges and unlock the broad potential of their data assets. By leveraging the latest technologies and industry best practices, businesses streamline operations, reduce costs, and gain a competitive edge.
VirtusLab understands the importance of taking a cost-effective and tailored approach to each client’s needs. We design customized solutions that optimize data management processes and deliver long-term value by leveraging the latest technologies and industry best practices.
To help you get started, we explain gradual engineering improvements and have shared some key areas to focus on, including reducing support and maintenance costs, preparing for platform version upgrades, ensuring resilience to changing business requirements, and handling legacy systems.
Gradual engineering improvements: Choose the best approach for your company’s needs
Businesses often have pre-existing tools and platforms when they require a new data solution. However, these existing systems may not be equipped to meet the unique requirements and challenges faced by the business.
Creating a new data platform may seem logical technically, but it can be costly and time-consuming. From a business perspective, there may be more efficient approaches than investing significant resources into building a new platform.
Creating a new data platform also introduces new challenges, such as integration issues with existing systems and potential disruptions to ongoing operations. These factors add to the complexity and latent costs of building a new data platform . As a result, businesses may consider alternative approaches to address their specific data needs.
Let’s take a look at your possibilities.
Short-term solutions create long-term costs
Businesses may have encountered consulting companies offering “tactical solutions” that repurpose their existing data platforms to provide efficiency. While these may seem like a quick fix to their data challenges, they may cost businesses more in the long run.
Short-term solutions that miss meeting your specific needs result in technical debt. This means that these solutions may become challenging to maintain, develop, and troubleshoot over time. Technical debt hinders business growth and leads to suboptimal outcomes.
New data platforms create costs and consume valuable time
When addressing data challenges, businesses may consider a long-term strategy involving creating or purchasing a new data platform that meets their specific needs. While this approach offers long-term consistency and efficiency, it is costly, time-consuming, and misses out on rendering results during production, especially in the short term.
As a quick solution, some product companies may propose an approach that involves purchasing their proprietary product, like a data platform, to solve the business’s issues. However, this approach can be costly, requiring enterprises also to extend their license and rewrite all existing applications to fit the new platform.
Such an approach is challenging to implement, leading to further technical debt in the long run. Additionally, a proprietary product may avoid including a business’s unique needs, leading to suboptimal outcomes.
It’s important to carefully evaluate whether a new platform is genuinely needed and whether it aligns with your business’s goals and resources. Take a holistic approach to data solutions and consider all options, considering your specific needs, challenges, and goals.
Gradual engineering creates opportunities in short-term and long-term
A gradual engineering approach gives businesses cost-effective solutions that build upon existing infrastructure and leverage present tools and platforms. This approach allows businesses to utilize their technology investments while still achieving the desired outcomes and improvements.
However, gradual engineering requires expertise in the latest technology and industry best practices. An experienced service provider like VirtusLab offers the necessary knowledge to design and implement gradual engineering improvement that meets the unique needs of your business while minimizing the risks and costs associated with building a new platform from scratch.
Gradual engineering improvements are the way to go for cost-effective results
Next to data management challenges, you need to operate and grow your business. In most situations, a gradual engineering approach that focuses on identifying the most critical areas for improvement and implementing small changes over time is often the optimal solution.
This approach helps you avoid the risks associated with big-bang projects, such as rewriting everything at once to an entirely new technology. Instead, you achieve incremental gains and improve your data management process more sustainably and efficiently.
Incorporating solutions that are easy to extend and maintain delivers business value from the beginning of your project. This ensures that your business benefits from the improvements immediately without incurring unnecessary costs or disruptions.
At VirtusLab, we understand the importance of taking a cost-effective and tailored approach to each client’s needs. By leveraging the latest technologies and industry best practices, we design customized solutions that optimize your data management process and deliver long-term value. Ultimately, we aim to help businesses optimize their data management process and achieve long-term success.
How to improve data engineering gradually
If you are looking to update your data platform gradually, we have collected some key areas from our experienced engineers at VirtusLab:
Reducing support and maintenance costs
Support and maintenance costs are essential aspects of data engineering. By reducing these expenditures, you streamline your data engineering process and improve the overall efficiency of your team. It is critical to regularly monitor and analyze your development process to find potential improvements. Typical steps you can take to reduce support and maintenance costs include:
- Migrating to a more stable and faster build tool setup. This helps improve the speed and reliability of your data engineering process.
- Consolidating all the different operations created over time into one system to log information about how they run, their errors, and warnings make it easier to manage and reduces the risk of missing important information.
- Improving your CI/CD pipeline’s performance and stability also helps to increase the number of changes in a shorter period with more test coverage.
- Automating and unifying alerts help to identify bottlenecks in your existing setup and reduce the likelihood of false positives. This includes constantly searching for tools to improve support experience and job observability.
- The introduction of a framework to simplify and automate reruns and data backfills, including dependent graphs reruns if not already provided by the scheduler.
Preparation for platform version upgrades
Upgrading your platform to its latest version demands an update of various libraries and frameworks used in the system, ensuring cross-build compatibility and decommissioning outdated technologies.
It’s best to stay updated with new releases of your platform’s frameworks and tools. Introducing them as soon as possible is advisable to simplify the preparation process. Doing so prevents bulk upgrades that require updating multiple components and extensive testing efforts.
Keeping track of new releases and promptly introducing them helps you stay current and avoid dealing with complex upgrades down the line. It’s essential to plan and remain proactive in managing your platform to ensure it runs smoothly and efficiently.
Resilience to changing business requirements
Ensuring data accuracy and completeness is crucial for meaningful data analysis. Data systems must be adaptable to changing business needs to stay relevant. These tips help to remain competitive:
- Consider using a Data Lake architecture to store and manage data. This approach involves storing raw data and creating transformations only in the second stage, making it easier to recreate tables even with completely changed logic.
- Evaluate implementing common mechanisms for rerunning large portions of tables to improve data quality and completeness.
- Adopt a unified approach to all your jobs, and regularly gather feedback from developers and users to improve the system continuously.
Handling legacy systems
Legacy source systems may lack active support or have limited business understanding, making it difficult to replace them. Instead of waiting for a replacement, consider reverse engineering the outputs produced by these systems.
Work with business users to understand their data requirements and how to extract them from the legacy system. Develop strategies for structured data extraction, ensuring consistency and accuracy. Use modern data analysis tools to effectively analyze and report on the extracted data.
Conclusion
Data is a critical asset that unlocks valuable insights and opportunities for businesses. However, effectively managing data can be a complex and challenging task. Businesses should adopt data solutions that optimize their effectiveness and streamline operations while reducing costs and gaining a competitive edge.
At VirtusLab, we believe that gradual engineering improvements offer the optimal solution for businesses looking to improve their data management process. Our approach focuses on identifying critical areas for improvement and implementing small changes over time, avoiding the risks and costs associated with big-bang projects.
Partnering with a service partner like VirtusLab helps businesses achieve discussed benefits and stay ahead of the curve in today’s data-driven world. With a customized approach that balances short-term gains with long-term profitability, VirtusLab helps businesses maximize the value of their data assets and position themselves for success.
We are committed to helping businesses maximize the value of their data assets and position themselves for long-term success. Contact us today to learn more about how we can help you achieve your data management goals.
Curated by
Sebastian Synowiec
