Here's how you can effectively select and evaluate new technology for data architecture as a data architect.

-Start by thoroughly understanding the business needs and objectives driving the adoption of new technology. What problems are you trying to solve? What are the key performance indicators (KPIs) that the new technology should impact? Aligning technology decisions with business goals is essential for success. -Stay informed about the latest developments in data architecture technologies. Research various options available in the market, including databases, data processing frameworks, data integration tools, and data visualization platforms. -Assess how well each technology aligns with your specific use case and requirements. Consider factors such as data volume, velocity, variety, and veracity.

When selecting new technology as a data architect, start by defining business and technical requirements. Research and make a shortlist of potential technologies, and evaluate their features and vendor reputation. Conduct a Proof of Concept (PoC) to test performance and integration. Assess the technology's compatibility with existing infrastructure and future scalability. Evaluate the total cost of ownership, including initial and ongoing costs. Perform risk assessments on security and vendor stability. Involve stakeholders for diverse input and use a weighted scoring model to make an informed decision. Plan the implementation carefully and monitor performance post-deployment for continuous improvement.

In my experience, when considering a new solution, it's important to include two additional aspects: 1) Emphasise the wisdom in learning from the gaps in your legacy systems and ensuring those gaps are integrated as requirements for your new product. It's always more economical to select a solution with data architectural controls built in from the outset rather than remediate later. 2) Seize this opportunity to tackle the most pressing challenge that the tool will resolve and consider aligning the technology with current trends. By doing so, you're solving immediate issues and future-proofing the solution. The tech should be designed to support adding new features, such as GenAI capabilities and staying abreast of other latest trends.

This is a process of learning with the tools that are used to define the gap between current and desired practice. It will helps to define the gap analysis that helps you to know where you are lacking. Firstly understand what is the business requirement. go each step, analyze the gaps and then assess the capacity

The tool options must be evaluate based in non-functionality and functionality requirements that you must meet on your data architecture propose. You have a list of data architecture proposes with its requirements that you can evaluate a tool options. The requirements can be based in scalability, performance, available, development, maintenance, security, etc.

Research and development is one of the best practice to enhance your productivity and learning. There are lot of forums and platforms available online and offline where you can do the same and get fruitful results.

Researching available options is a pivotal step in the process of selecting new technology for data architecture. Through extensive exploration of various tools, platforms, and solutions, we can gain a comprehensive understanding of their features, functionalities, and suitability for our organization. This diligent research empowers us to make informed decisions, considering factors such as scalability, security, and performance, ultimately leading to the adoption of the most suitable technology.

While working for data architecture in the past , we should ensure the tech stack we select supports Horizontal scaling rather then vertical scaling. Technology selection should be done according to how a cloud provider service handles scaling up and down based on load of data. Automation in scaling should be preferred. Evaluate technology that supports data to be distributed across multiple servers when designing solution in IaaS based architecture since this is the effective way to evaluate Scalability. Latency should be measured when data is in transit that would also evaluate consistency and integrity under high load conditions. When designing the infrastructure Reliability and Availability should also be given a check.

this deals with how the way that you effectively scale the performance of a system towards the storage and organizing of the data. Also this depends on the data handling with effectiveness towards time. There are 2 methods to scale a database: horizontal and vertical. Horizontal scaling means adding more computers to run the database software. Vertical scaling means to add more CPUs on a single machine to run the database software. Data engineering and Data architecture generally goes with horizontal scaling

In my experience, selecting and evaluating new technology for data architecture requires a meticulous approach with a keen eye on security. I always prioritize solutions that offer robust security features like encryption, access controls, and threat detection. Thoroughly researching a technology's track record for vulnerabilities and how quickly they are addressed is crucial. Additionally, I conduct pilot tests and proof-of-concept implementations to assess the technology's compatibility with existing systems and its potential impact on overall security. By prioritizing security from the outset, I can confidently integrate new technologies while safeguarding sensitive data.

The security in data engineering can be achieved through The Common Data Security Architecture (CDSA). This defines the comprehensive set of security services to address the needs of individual users and the business applications.The Common Security Services Manager (CSSM) is the core of CDSA which provide direct access to the applications security services through the CSSM security API, or to indirectly access security services via layered security services and tools implemented over the CSSM API. It manages the add-in security modules and re-directs application calls through the CSSM API to the selected add-in modules that will service the request.

Testing the performance of new technology is essential to ensure that it meets your organization's performance requirements and expectations. Conduct thorough performance testing to evaluate factors such as processing speed, data throughput, latency, and resource utilization under real-world conditions. By testing the performance of each solution rigorously, you can identify any potential bottlenecks or limitations and make informed decisions about their suitability for your data architecture.

Performance testing evaluates the responsiveness and scalability of the data warehouse. It identifies bottlenecks and potential performance issues, ensuring optimal data query response times. This can be implemented through data performance testing tools such as ADF and Azure CI CD pipelines.

Planning the transition to new technology is critical for ensuring a smooth and successful implementation process. Develop a detailed transition plan that outlines the steps, timelines, and resources required to migrate to the new technology seamlessly. Consider factors such as data migration strategies, integration with existing systems, user training, and change management to minimize disruption and maximize adoption. By carefully planning the transition process, you can mitigate risks and ensure a successful deployment of the new technology in your data architecture.