Google is the leader in providing cloud-based solutions to millions across the globe. Data storage and management is taking the business forward! It has put forward the proposition of using cloud services to put the data management strategy in place. GCP (Google Cloud Platform) is the most affordable option for businesses so as to start their activities on the cloud. You can choose Google Cloud Consulting services so as to manage tasks flawlessly. Lead the business towards success through proven cloud computing services.
There is a sharp rise in the use of cloud technologies so as to power the computing infrastructure for everyone. Organizations are moving their high-performance workloads like data analytics and ML to the cloud. Google is at the forefront with its cloud solutions, and the data lifecycle service helps manage a cluster of data.
Google cloud consulting is the best option to take full advantage of the cloud transition. It becomes part of the strategy for businesses looking to enhance their user experiences. GCP is offering a host of services for businesses as public cloud infrastructure. Data analytics is very much part of the GCP offerings enhanced by Google AI and ML solutions. The platform is popular among SMBs, and its adoption simplifies a lot of things. It is the article that covers details related to the Google Cloud Platform and the Data Lifecycle Services.
Table of Contents
- What is the Google Cloud Platform (GCP)? All to know about it!
- Different Phases of Google Cloud Platform Data Lifecycle
- First Phase – Data Ingestion
- Second Phase – Data Storage
- Third Phase – Data Processing and Analysis
- Fourth Phase – Explore and Visualize
- Final Thoughts!
What is the Google Cloud Platform (GCP)? All to know about it!
Google Cloud Platform provides reliable and scalable options for businesses. The services help to clients compute and store data. It also assists developers so as to build, test, and deploy applications on remote data centres. The Google cloud platform covers storage and cloud computing services with the help of suitable web solutions. Google is simplifying the file system and helps to handle requests via different commands.
The GCP services are robust, and one of the ways to navigate the options so as to consider the solutions based on primary computing needs like –
IaaS assists the running of virtual machines without the need to invest in or manage the computing infrastructure. The IT services opt for IaaS solutions because the workload is temporary and experimental.
PaaS is the next step and builds on the IaaS model. The clients opting for PaaS service get the benefits of IaaS. They also underlines infrastructure like middleware and operating systems.
SaaS is available via the internet, and the service providers host and deliver the entire infrastructure. Clients access the resources through specific deliverables like backup and recovery tools.
Businesses are leveraging the Google Cloud Platform interface for MS Office so as to make the necessary edits. You can also start saving the files to the remote data centres after installing a plugin for the same MS Office program suite. Google Cloud inserts the metadata into the files so as to track the changes across all copies.
The documents get synced to the master file in Google Cloud Platforms. It also enables the updating of all the downloaded documents so as to maintain the records.
Different Phases of Google Cloud Platform Data Lifecycle
Cloud computing is ensuring the co-existence of software and hardware products in remote data centres. All the computing products are working together so as to deliver specific services to enterprises. The Google Cloud Platform services allow the easy access and use of tools via web interfaces. Users can create simple projects via the intuitive, web-based GCP Console and manage access of services to admin.
There is an explosion of data due to business digitization and the growth of IoT devices. The data lifecycle of GCP enables the strategic use of big data for business. Big data is changing the way businesses run so as to utilize data efficiently. The Google Cloud Platform is assisting in the collection and structuring of data so as to enhance the decision-making pool. It boosts the data analytics capabilities so as to integrate them into strategies and operations.
Let us now understand four different phases of Google Cloud Platform Data Lifecycle in detail –
First Phase – Data Ingestion
It is the first step so as to manage data using Google Cloud Services. There are various sources for data ingestion, and user data gets captured from hosted applications. The machine data comes from Stackdriver Logging, and data from IoT devices are input using a server-less messaging queue. GCP is addressing the challenges so as to migrate bulk data from different cloud platforms.
The Google Cloud Service at the time of ingestion includes:
- Compute Engine – Ensures the running of virtual machines for hosting and computing
- App Engine – A fully managed serverless platform
- Google Kubernetes Engine – It gets used for container management
- Transfer Appliance – A high capacity storage server
- BigQuery Data Transfer Service – It means automation of the data movement
- Cloud Logging – Used for collecting log data
- Cloud Storage Transfer Service – It manages the transfer of data
Data collection is one of the vital aspects of the management of data sets, and there are multiple sources for collecting raw data. It includes data generated by event logs of apps, social network interactions, and digital transactions. There are also bulk data available in files like CSV, Parquet, or NoSQL, and the datasets may get located on other cloud platforms.
Second Phase – Data Storage
After data ingestion, there comes the need so as to save data in all formats and locations for easy accessibility. Data storage is one of the crucial aspects of data management. Google cloud offers the best alternative to secure data on remote data centres. The data storage takes place on appropriate cloud storage locations based on the use case.
The GCP services helpful for storing data include:
- Cloud SQL – A fully managed RDBMS that has PostgreSQL and MySQL engines
- Cloud Firestore – It includes a flexible NoSQL database for storing JSON data
- Cloud Storage and BigQuery – Firebase scalable object storage device and managed data warehouse
- Cloud Bigtable – A fully managed NoSQL database for managing large workloads
GCP has storage options like BigQuery or Cloud Spanner or open source solutions like Cloud SQL. The filtration of data gets done on factors like – SQL or No-SQL, and structured or unstructured databases.
Third Phase – Data Processing and Analysis
GCP provides an array of tools so as to process and analyze the data stored in remote data centres. The tailored services availed by businesses takes care of the data cleaning procedures via Cloud Dataprper.
It can also create Cloud Dataflow pipelines by using the intuitive UI. We can do the complete workflow orchestration via Apache Airflow, so as to allow the end-user to create direct acyclic graphs.
The services included during big data processing include:
- Cloud Dataproc – It is a fully managed service for Apache Spark, Apache Hadoop and other open-source frameworks. Dataproc is also used so as to read and write data natively in Cloud Storage and BigQuery.
- Dataflow –Unify the programming and execution model with help of a fully managed serverless service provided by Dataflow. It also helps to create on-demand resources and pre-processing get used Machine Learning models.
- Cloud Dataprep – The service offers a visual interface for exploring and preparing data for evaluation. It ensures location-specific data processing with BigQuery and Cloud Storage.
- BigQuery – Store data on the GCP with fully managed data warehouse. BigQuery is a scalable option and supports complex schemas. Also, it is so preferred for user analysis and business intelligence.
Fourth Phase – Explore and Visualize
It is the last stage of GCP’s data lifecycle service and involves data exploration. The stage is also useful for extracting insights so as to make informed decisions for businesses.
The services included in this stage are:
- Datalab – It is the web-based interactive tool useful for the exploration and visualization of data. Write the Python programs on web-based notebooks so to be easily shared with collaborators.
- Looker – It offers tools so to enhance the data experience from BI to workflow integration and custom data apps.
- Data Studio – You get interactive reports and dashboards so as to get the visual representation of live data in the form of graphs and charts.
- BigQuery BI Engine – It is the fast analysis of BigQuery that ensures interactive analysis of complex datasets. The BI Engine also easily integrates with Data Studio and helps in the creation of interactive reports.
The GCP services have made a tremendous impact in the business world so as to secure large datasets. The data lifecycle services collects, stores, and evaluates the datasets so as to make business-savvy decisions. It is making gradual progress in the market so as to secure information in remote data centres. Google has a proven track record in big data and analytics. A business has access to the same infrastructure and Google services internally. The platform simplifies the data management and analytical tasks.