There’s a rising demand to manage data through cloud computing. Google Cloud platform comes in handy to help with data storage; that’s why every data analyst needs to have vast knowledge about the industry to maximise its potential. In this article, we will cover the basics of cloud computing and its impact on the data storage industry.
What is Cloud Computing?
Cloud Computing is a real-time availability of computing resources through a distributed collection of servers that host applications, databases and IT infrastructure for accessibility through an internet connection. Google Cloud platforms provide maintenance of the servers and IT infrastructure for seamless, scalable computing power and data storage. The pay-as-you-go model can be cost-effective for setting up IT infrastructure and easy scalability for small and medium businesses in real time.
Furthermore, storing data and applications in the cloud can provide cross-boarder transfer of information without hassles. The challenge that comes with big data has made cloud computing relevant for managing data storage by deploying machine learning applications due to the high demand for artificial intelligence. The top cloud providers in the world are GCP by Google, AWS by Amazon and Azure by Microsoft.
What is Google Cloud Platform (GCP)?
Google Cloud Platform is an array of cloud computing tools powered by Google data centre and runs on the same infrastructure as Gmail, Google Drive, and Google Docs for data analysis and storage. Google provides innovative solutions to data analysts and consultants through artificial intelligence, data analysis and learning machines. Google first came to the public notice in 2008 by announcing a preview release of its App Engine for a developer that allows users to run their web applications on Google.
This was two years after Amazon had launched its computing services with the release of S3 cloud storage and EC2. Google was quoted to have said that its aim for App Engine was to “make it easy to get started with a new web app and then make it easy to scale when that app reaches the point where it’s receiving significant traffic and has millions of users.” in 2011 Google made App Engine officially supported Google products to enhance seamless users experience.
Use of Google Cloud Platform
Google Cloud Platform offers a wide range of services that cut across load balancing to server management that supports application development, testing, and deployment. Google platform big data and analytics help process and analyse large data while it also trains and deploys machine learning models. Google Cloud Platform identify management and key services for connection and analysis of data from different Internet of Things (IoT).
How Does the Google Cloud Platform Work?
Google Cloud Platform is a combination of cloud computing services offered by Google to businesses with tools for properly running their application and data on the Google platform. Google Cloud Platform provides low latency and high performance to users through Google’s global network data centre. Google services can be accessed in 6 ways, namely:
The Google Cloud Console: it’s a web-designed interface that provides seamless management of the Google Cloud Platform.
The Cloud CLI Tool: these tools help users interact with Google Cloud Platform services through the command line.
The Cloud SDK: there are various tools that develop and deploy applications to the Google Cloud Platform.
Google Cloud API: with this tool, users can access Google Cloud Platform programmatically.
Cloud Marketplace: users can get easily deployed and integrated solutions from Google, ISVs, and SaaS providers.
Cloud Shell: Google Cloud Platform can run commands direct into a web browser through a secure and preconfigured environment.
Services Provided By Google Cloud Platform
- Application Development Process
This involves App Engine managing to build and host web applications for seamless creation and deployment of applications without infrastructural management, server maintenance, or scaling complications through serverless cloud function that helps developers build and run applications and microservices. Cloud Code simplify deployment, management and scaling across Google Cloud Platform services for a unified user experience in various cloud resources.
Furthermore, Google Cloud Platform can automate the process of building, testing and deploying software through Cloud Build for faster and seamless compilation of code, creating docker images, running tests, and deploying software to GCP Kubernetes, etc. Kubernetes engine is built on an open source and Google Cloud Platform (GCP) to deploy, load balance and manage containerised applications on a cloud-based system without hassle. While Compute Engine, as a cloud-based infrastructure-as-a-service, allows organisations to run virtual machines and applications on the Google platform for scalable computing resources.
- Storing & Managing Data
For storing and managing data, developers can use Cloud SQL, which is well-designed and highly scalable to store and manage data in the cloud. Cloud Spanner can manage large amounts of structured and semi-structured data for ACID transactions, SQL, and multi-region replication. Furthermore, Cloud Datastore can help developers store and retrieve data through a seamless and scalable means that eliminates hassle. In contrast, the data are designed and distributed across various servers by Bigtable for scalable and low latency solutions for handling large data on the Google Cloud Platform. Users store their data as objects, blobs, files, and archives for specific business needs.
- Machine Learning & Artificial Intelligence
Machine Learning and artificial intelligence help data scientists and developers build and deploy machine learning for scalable Google Cloud Platform infrastructure while reducing time and effort. It also provides advanced image and video analysis for easy integration by developers into applications to analyse in real time. Developers can use Cloud Vision API to detect objects, logos, faces, and texts from images and videos. Machine learning and artificial intelligence enable the processing and analysis of text data in a faster and more accurate form by leveraging machine learning algorithms for extraction of information and insight from unstructured text to perform various text analyses such as sentiment analysis, entity recognition, part of speech tagging and syntax analysis.
Cloud translation API on machine learning and artificial intelligence is designed to translate text to different languages through highly efficient neural machine translation technology. This technology’s support of multiple languages makes it helpful to translate website content, chatbot responses, etc. Furthermore, machine learning tools can help convert spoken words into written text, making it easier for users to interact with applications, services, and devices through their compatibility with various languages and accents. It can also offer faster, scalable and seamless cloud-based solutions that train and deploy learning machine models to reduce complexity and cost-effective building and maintenance of in-house machine learning infrastructure.
- Managing & Analysing Data
This cloud-based data solution transforms and processes big data through its seamless and intuitive interface in real time without hassle. Managing and analysing data can help data scientists and developers efficiently work with large-scale data by connecting with different data sources to process and store large data while performing complex analysis tasks. Sometimes, data could be lost, deleted or corrupted on the Google Cloud Platform by hardware failure, human error or security breaches, and this can affect the operation of businesses that depend on data stored in the cloud.
Managing and analysing data provides fast and seamless cloud services on Apache Hadoop and Apache Spark jobs on the Google Cloud Platform for powerful and cost-effective processing and analysis of large-scale data for quick real-time spin and storage. Managing and analysing data offers serverless, scalable, low-cost warehouse solutions designed to process and analyse data in real-time for businesses and organisations. Users can also store and analyse large-scale data without the hassle of technical complexities.
Google Cloud Platform Advantages
- Google Cloud Platform offers broader cloud services for businesses that cut across computing power, networking, data storage, data analytics, machine learning, artificial intelligence, app deployment and API integration for seamless user experience.
- Google Cloud Platform offers businesses wide demographics reach through its global reach for implementation of multi-region redundancy.
- It offers robust security that includes identity and Access Management (IAM), Key Management Service (KMS), and Security Command Center (SCC) for advanced security measures for businesses while hosting on the cloud.
- The innovative and technological advancements place Google Cloud Platform ahead of its competitors by offering customers cutting-edge services such as integrating artificial intelligence for analysing data.
Disadvantage of Google Cloud Platform
- The complex pricing of Google Cloud Platform can be challenging for businesses to forecast and manage expenses on cloud computing.
- Customer support on the Google Cloud Platform is relatively limited, leaving customers stranded with a hard-to-access support team.
- Google Cloud Platform is a proprietary platform that makes it difficult to migrate data and deploy applications whenever businesses decide to leave the platform.
Conclusion
Google Cloud Platform can be a great tool to build and run applications on the cloud to perform large-scale data analytics and machine learning. This infrastructure involves various tools to provide access and management services to protect data.
Bonus
Cloud computing can be valuable for any data professional, considering many companies use cloud workspace to store valuable information. If you want to know more about the Google Cloud Platform, here are some resources to help you.
Google Cloud for Data Scientist: this resource covers insight on using Google Cloud as a data scientist.
Google Cloud for Data Science: This educational blog shows how to set up a Google Cloud.