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The advent of cloud computing has led to a significant realignment in corporate data processing and application development strategies. This realignment has standardized the entire cloud development stack, from data engineering to application development to data visualization. Standardization necessitates a more focused skill set that is relevant to the entire spectrum of cloud development.

Monolithic and isolated applications residing on on-premises servers and utilizing proprietary message protocols and engineering methods are obsolete. Instead, applications are increasingly focused on cloud-native architecture with decoupled microservices, standardized messaging protocols, and common authentication patterns. To take full advantage of the cloud's capabilities and achieve operational efficiency, businesses should rely on experts in cloud engineering, infrastructure, and best practices. Cloud certification provides this expertise, and cloud-certified professionals make a greater and more immediate impact on their partner companies.

The age of the cloud has enabled a new operating environment for corporations. We envision a more agile corporate structure in the Cloud Age. Instead of large, lumbering monoliths with overly complicated and inefficient governing structures we see a more nimble approach to achieving the holy grail of corporate structures: being able to take advantage of economies of scale while also outflanking agile startups. We see a world of compromise between the vested interests of Wall Street and the ambitions of the hungry entrepreneurs. Welcome to Corporate Engineering 101.

One key task of cloud architects is to map business requirements to cloud applications. A key strategy is to break down your application requirements into individual use cases and components. This makes mapping services and resources much easier to manage and maintain.

Let's take a look at the current state of artificial intelligence in 2024, its capabilities, its limitations, and how effectively it could replace certain positions. How can businesses focus AI to drive real domain development, profitability, and improve customer service? Could AI do this more efficiently and effectively than a human could? AI and automation in general are great at replacing menial and repetitive work, but what is the limit given the impressive power of AI?

The age of the self-driving car is here. We see this as a revolution in transportation, not simply an evolution, which will have as profound an impact on transportation as the invention of the automobile itself. In this article we will detail the history of the automobile, the development of self-driving cars, and how self-driving cars will impact society. We believe that the robotaxi revolution will not only make transportation more efficient, but it will completely transform how cities are designed and evolve.

Data as a service (DAAS) is an operating methodology which seeks to improve the functioning of dedicated data teams. It focuses on agile development and a decentralized operating structure to quickly, efficiently, and accurately develop and deploy advanced data products. The team focuses on developing algorithms, generalized tooling, customer relations, and agile development in order to provide a superior service to end users. We like to compare it to a kitchen in a busy restaurant. The data team develops custom meals according to end user specifications by focusing on a rigorous process to ensure a high quality product made custom to order for every unique customer.

I don't think that Bruce Lee had data in mind when he developed his philosophy, but I find the idea to "be like water" is a great way to look at data. His wonderful suggestion to "be shapeless, formless" - to be able to take any shape - can apply to data engineering in surprising, interesting, and useful ways. Bruce tells us that water can "flow, or it can crash", likewise data can flow, crash, transform, or represent but the essential element, the integrity of the data itself, is always maintained.

The rise of AI is driving the formation of a new profession: the AI psychologist. An AI psychologist is a highly specialized professional who interprets how and why an AI reaches certain conclusions about its environment and chooses to perform a particular action. These interpretations can be used to better train or program advanced AI systems. The AI psychologist will become more necessary as AI algorithms become more complex and humans more greatly interact with AI systems. These conclusion will improve how AI performs in the real world, help to understand the phenomenon of hallucinations, and provide a better customer experience for end users of AI powered products.

We test ChatGPT's capacity to answer LSAT example questions to determine whether it is capable of resolving higher-order and abstract logical queries.

A collection of reviews on books we consider to be highly influential on the industry.