The basics of Google Cloud Platform (GCP)

The Cloud is a complicated space. It’s not a simple plug and play as most people would imagine. Let’s simplify the Cloud: GCP Edition. The Cloud is a complicated space.

The Cloud is a complicated space. It’s not a simple plug and play as most people would imagine. We have folks from various backgrounds such as developers, network engineers, machine learning engineers, data architects etc. who would have mastery over certain components of the cloud.

When working in an enterprise environment, it is critical that experts are working on the various relevant components and everybody has a place in the data and model pipeline lifecycle. This can include roles such as:

• Security: Handling of Identity and Access Management (IAM)
• Data Architecture: Understanding the interaction between various cloud services and in-depth understanding of on-prem services & requirements
• Model Operationalization: Hands-on understanding of IaaS, PaaS, SaaS features on the Cloud; pipeline automation, optimization, deployment, monitoring and scaling
• Infrastructure: Dynamic requirements of various projects and products to minimize the cost to the company along with agility in application
• Support: End to end knowledge of the Cloud platform being leveraged from professionals to save time on debugging (knowing over learning)

A healthy mix of the above skillsets can lead to a successful movement to move from legacy systems to scaling on the cloud.

Photo by Taylor Vick on Unsplash

The Data Lifecycle on GCP

Google has been in the internet game for a long time. Along the way, they have built multiple great products. When your efforts are experimental and not streamlined, to begin with, it becomes evident in the product portfolio offering.

The same workflow can be achieved on the cloud in multiple ways. The optimization and right choices made is what makes one a Google Cloud Data Professional.

Data Lifecycle is the cycle of data from initial collection to final visualization. It consists of the following steps:

• Data Ingestion: Pull in the raw data from the source — this is generally real-time data or batch data
• Storage: The data needs to be stored in the appropriate format. Data has to be reliable and accessible.
• Data Processing: The data has to be processed to be able to draw actionable insights
• Data Exploration and Visualization: Depending on the consumption of the data, it has to be showcased appropriately to stakeholders

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