Here's how you can involve data engineers in evaluating and selecting new technology solutions.

To involve data engineers in evaluating and selecting new technology solutions, integrate them into cross-functional teams. Conduct workshops to gather their insights on requirements, potential challenges, and integration needs. Encourage them to research and test solutions through pilot projects, allowing hands-on evaluation. Facilitate regular feedback sessions to discuss findings and preferences. Leverage their expertise to assess performance, scalability, and compatibility with existing systems, ensuring informed decisions that align with technical and business objectives.

Assessing needs is crucial for involving data engineers in evaluating and selecting new technology solutions. ?? Identify the specific challenges and requirements your team faces. ?? Gather input from data engineers to understand their pain points and needs. ?? Define clear objectives and outcomes you aim to achieve with the new technology. ?? Consider the current infrastructure and how the new solution should integrate with it. ?? Prioritize features and functionalities that are essential for your team’s success. Involving data engineers in assessing needs ensures that the selected technology aligns with practical requirements and maximizes efficiency.

Involving data engineers in evaluating and selecting new technology solutions begins with assessing needs. At Qodek, we hold brainstorming sessions where data engineers discuss pain points and desired capabilities. For instance, when we faced challenges with data processing speed, we identified the need for a more efficient ETL tool. Involving the team from the start ensures that the chosen solution addresses real issues and enhances productivity.

Researching options is essential for involving data engineers in evaluating and selecting new technology solutions. ?? Conduct a thorough market analysis to identify available technology solutions. ?? Review case studies and user reviews to understand the performance and reliability of different options. ?? Compare the features and functionalities of various technologies to see how they align with your needs. ?? Involve data engineers in evaluating the technical specifications and compatibility with existing systems. ?? Stay updated on emerging technologies and industry trends to ensure you consider the latest advancements.

Researching options is a collaborative effort. I encourage my data engineers to explore various technologies and share their findings. During my tenure at Figma, we formed small teams to investigate different data storage solutions. Each team presented their pros and cons, leading to informed discussions and a more comprehensive understanding of available options. This collective research approach leverages diverse expertise and perspectives.

It's a great way to learn the big picture, for instance cloud technologies - how a slight change in architecture impacts the overall costs, performance, processing steps, data pipeline resilience, etc. Research brings ideas, ideas bring innovation, innovation brings new solutions for existing problems this bringing the real value.

Testing feasibility is crucial before making a decision. At Microsoft Azure, we implemented a proof-of-concept phase for new technologies. Data engineers were tasked with running pilot projects to test the scalability, compatibility, and performance of potential solutions. This hands-on testing allowed us to identify potential pitfalls and ensured the technology could handle our data workloads effectively.

Testing feasibility is crucial for involving data engineers in evaluating and selecting new technology solutions. ?? Conduct pilot tests to assess how the new technology performs in a real-world environment. ?? Involve data engineers in setting up and running these tests to gather practical insights. ?? Evaluate the technology’s integration with existing systems and workflows. ?? Analyze performance metrics, scalability, and reliability during the testing phase. ?? Collect feedback from data engineers on usability and potential challenges. Testing feasibility ensures the chosen technology meets practical requirements and supports smooth implementation.

Implementing small proof of concept or small scale test cases is always a way to go. Not all of them will be successful though,but none of that is actually wasted time - your team is gathering valuable insights and lessions learned to gain leverage for further use.

Costs should be measured by TCO (Total Cost of Ownership) vs Time. This means considering 4 factors: - Cost of Software - Cost of Engineering Resources (not just to build but also maintain or add new features) - Cost of Infrastructure - Time it takes to deliver In life and engineering, there is always tradeoffs. Considering these in totality will help you choose the right solution.

Evaluating costs involves both financial and resource considerations. At Qodek, we involve data engineers in cost-benefit analyses to understand the total cost of ownership, including licensing, implementation, and maintenance. For example, when evaluating a new data visualization tool, we assessed not only the purchase price but also the training time required for our team to become proficient with the tool.

Evaluating costs is essential for involving data engineers in evaluating and selecting new technology solutions. ?? Consider the total cost of ownership, including initial purchase, implementation, and ongoing maintenance. ?? Involve data engineers in estimating the resources and time required for integration and training. ?? Compare the costs of various solutions against their benefits and features. ?? Assess potential hidden costs, such as downtime during implementation or additional support needed. ?? Ensure the selected technology provides a strong return on investment and aligns with the budget. Evaluating costs thoroughly helps make informed decisions that balance financial constraints with technological benefits.

Engaging vendors is an opportunity for data engineers to ask technical questions and assess vendor support. When we evaluated cloud service providers at Azure, we arranged meetings with vendors where our engineers could delve into specifics about integration capabilities, performance benchmarks, and support services. This direct interaction helps ensure the solution fits our technical requirements and provides the necessary support for implementation.

Planning integration involves detailed coordination with data engineers to ensure a smooth transition. At Figma, we developed a comprehensive integration plan that included timelines, responsibilities, and risk assessments. Data engineers played a key role in mapping out how the new technology would integrate with our existing systems, identifying potential challenges, and planning mitigation strategies. Their involvement was crucial for a successful implementation.

When evaluating and selecting new technology solutions, I think it's important to also consider the ... Collaboration: Involve data engineers in discussions with other departments to understand their specific needs. This will ensure that the chosen technology solution meets the diverse needs of the entire organization. Business relevance: Work closely with business stakeholders to ensure the technology solution aligns with the organization's strategic goals and delivers measurable business value. Future-proof: Evaluate the long-term viability and assess how well the new technology can scale as the business grows and adapt to future technological developments.

Additionally, fostering a culture of continuous feedback is vital. Post-implementation, I encourage data engineers to provide ongoing feedback about the new technology’s performance. At Qodek, we hold regular review meetings to discuss what’s working well and what could be improved. This iterative feedback loop ensures that the technology continues to meet our needs and allows for adjustments as necessary, maintaining alignment with our evolving business objectives.

- Attending conferences, meetups, and talks can be invaluable, as they provide insights into how other companies have successfully implemented or utilized similar tools. - It's crucial to assess whether the tool is genuinely needed and, if so, what specific business value it will bring to the organization. - When developing solutions, always design with future changes in mind—build systems that allow for easy adaptation and scalability. - Providing training and encouraging the team to stay updated with new technologies can also help ensure smooth adoption and effective use of new tools.