How do you follow best practices and standards for cloud computing and data analytics?

Choosing the right cloud service model is a crucial decision. We begin with creating a business case and Application assessment and, derive migration strategies (Rehost, Replatform, Refactor/Rearchitect, Replace, Retire, Retain) and evaluate the Cloud service model based on the migration strategies. IaaS offers virtualized computing resources on a pay-as-you-go basis if you need control over the underlying infrastructure and want to run custom applications. PaaS provides a platform for developing, deploying, and managing applications. For example, you can consume Oracle Database directly from the Oracle cloud, which has much automation. SaaS delivers software applications over the internet, typically on a subscription basis.

In my view, data security (no matter where data is kept and/or processed) is not just a matter of ticking off checkboxes from a checklist or following a set of rules; it's about setting and keeping in place a proactive mindset.

Modern data platforms and cloud services must along data platform capabilities to leverage zero-trust capabilities, and well-advanced cloud and security networking, that helps to accelerate the path towards achieving expected business benefits in digital transformation. Along with flexible and reliable cloud Identity and Access Management, Key Management Systems including secrets management enable achieving a high degree of security and data privacy level. Next, confidential computing is a technology that encrypts and stores an organization’s most sensitive data in a secure portion of cloud computing resources, complementing data encryption at rest and in transit. It represents the highest level of encryption of data in use.

Data Encryption plays a crucial role in cloud data security—Encrypt data both in transit and at rest. Use critical management practices to protect sensitive data. Enforce robust IAM policies and conduct audits regularly. Enable MFA for user accounts to add a layer of security. Apply critical patches to your cloud services, applications, databases etc., Use an observability platform to capture detailed logs and audit trails to facilitate compliance reporting and investigation of security incidents. Conduct regular security awareness training for employees to understand and follow cloud security policies and best practices.

Optimizing data architecture is an ongoing process that requires continuous assessment and adaptation to meet changing business needs and technological advancements. Review and refine your data architecture to align with your organization's goals and objectives. Design and utilize best practices in data modelling (normalization and denormalization).

In my opinion, scalability and elasticity should be both placed at the core of the design as they are the enablers for adopting and adapting to the new technology changes and improvements, together with the right sizing to make data storage feasible. Even if it may seem unclear, or intangible, they would ensure the project continuity and, specially, the explotation of the value of the data (and potentially profitable data monetizations). Questions like expected growth, response times needs, cost and value of the data and others for the short, mid and longer term have to be present at an early design stage.

Data analytics frameworks and tools are becoming mandatory not only for their functionalities but also for the continuity of the developments (and evolution). Technology is moving faster and so creating a faster growing demand than the offer to satisfy it. Human skilled resources are not yet growing at the same speed. The increasing people rotation is impacting on the operational costs (also increasing). The quick mitigation I see is to be as close as possible to "de-facto" standards to allow the evolution, remain updated and allow onboarding the new improvements, with the hope that skilled resources would be available, even if scarce.

Data analytics frameworks and tools are necessary for collecting, processing, analyzing, and visualizing data Data Collection: Apache Hadoop, Kafka, or you can use simply Amazon S3 Data Processing: Informatica is a widely used tool Data Analysis: Python, Tableau, Power BI

To test your analytics solution, need to go one step behind to validate the data which is back bone for your reporting. There must be a data quality framework in place in your architecture to ensure data quality using Great Expectations or any other spark-based data quality framework based on your architecture and tech stack

Testing and validating data analytics solutions is crucial to ensure they are accurate, reliable, and meet the planned business goals. Project scope: Define what you are trying to achieve with data analytics solutions Choose the right tools: Select the right tool/platform to achieve your business goals. Feedback: continuously improve and refine your analytics solution based on real-world results and changing business requirements

Unless exceptionally situations, diving into the dynamic realm of cloud computing and data analytics is not for lone-rangers. You, as well as others, need to know other people's opinions, perceptions, trends and their stability to contribute to your vision. The "why" is simple: the community is a goldmine of experiences, insights, and best practices with many pearls of wisdom that can shape your own path. You can see it as absorption but it is more than that. Posing questions, sharing challenges, and participating in discussions foster a dynamic exchange of ideas, challenges your ideas and may help others to "see" things from a different and agnostic perspective.

Community plays a crucial role in our life. It is a platform to stay updated with the latest and greatest updates, best practices, meeting with experts, etc. Attend regular online or physical meetups provide a space for discussions, questions, and knowledge sharing. These events offer the chance to learn from experts, network with peers, and explore the latest trends. Connect with professionals on social media platforms like LinkedIn and Twitter. Follow relevant hashtags and accounts to stay updated on industry news and engage in discussions. Pursuing relevant certifications in data analytics can validate your expertise and keep your skills up-to-date.

In Summary, Optimizing Cloud computing and data analytics is an ongoing process that requires continuous assessment and adaptation to meet changing business needs and technological advancements. Review and refine your plans to align with your organization's goals and objectives.

In my opinion, the best strategy is to test and validate the best strategy. No strategies are best unless proved to be robust and solving business problem. There is no single medicine for everything. Always test your strategy and hypotheses to draw conclusions.