What is Cloud Analytics? 6 benefits of using Cloud Analytics

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What is Cloud Analytics?

Cloud Analytics is the process of performing data analysis operations on a public or private cloud, as opposed to performing analytics operations on your Local Infrastructure. It utilises cloud computing to perform complex analytical and data operations in a much faster and optimised way. 

E.g., RunPod allows you to utilise GPU/(s) when working on billions of parameters. It would take a long time if it were processed locally. One example of such processing can be word vector embedding, which can take hours on a local CPU can be done in a fraction of the time using GPUs. 

Cloud Analytics is the way forward in large-scale data processing, as data operations become increasingly complex, such as in the case of LLMs.

Some of the more popular cloud Analytics platforms are Tableau, Thoughtspot, etc.

Functions of a cloud analytics platform

1. Accept and Process different Data Sources - The analytics platforms should be able to read and ingest different data types and process them into a program-readable format. These formats include both structured data types (JSON, XML) vs unstructured data types (DAT, PKL). It should also be able to accept real-time data from various sources, such as CRM platforms etc. This processing of data can be called ETL (Extract, Transform, Load). 

2. Ability to load and run Analytical Models - The analytics platform should be able to run different types of analytical models and simulations on the data, either with pre-defined models or custom-defined models. The platform should have a wide range of analytics and other pre-defined models, catering to the wider needs of companies.

3. Data processing pipelines and auto optimizations - The analytics cloud platform should be able to process batches of data and apply any necessary optimization in streaming or processing of data, in order to ensure a speedy and efficient data processing for the client without having to compromise on data quality or processing time. 

4. Leveraging AI Models -  Analytic cloud platforms would allow you to use the latest AI LLM models to support data ingestion and processing. Some of the more popular models used in Data processing are GPT, Gemma, and the Llama family of models.

5. Providing Storage and data warehousing - Cloud Analytics platforms allow you to store your pre-/post-processed data. It also allows you to store the result of data processing and visualizations, in storage or as central repositories where the older versions of data as well as the latest data, with its telemetry data, are stored in a retrieval-efficient manner for future use. This is known as data warehousing. 

Benefits of Cloud Analytics

Cloud Analytics can reduce the computing load on the Local Infrastructure setup

Performing analytics operations on the cloud such as storage and data processing can reduce the computing load on your local infrastructure. Cloud analytics data can be retrieved via an API or be accessed directly by connecting to the Server platform. 

This can be beneficial as the additional computing power made available can be provisioned for a crucial task in the functioning of the local infrastructure. This sort of cloud analytics requires a Hybrid infrastructure setup. Alternatively, the complete analytics processing of data can be provisioned on the cloud. We will cover both later in the course. You can read more about provisioning Cloud analytics on your local or cloud infrastructure via Terraform using GCP here. - https://cloud.google.com/docs/enterprise/deploy-foundation-using-terraform-from-console

Cloud Analytics can be provisioned for load balancing in case of heavy data loads

Processing Analytics load on the cloud can allow you to provision load balancing on your cloud platform easily without much hassle as compared to manually provisioning a server in your local infrastructure. 

Load Balancing is important as the data load increases or in case there is a requirement to process a lot of data at once. Load balancing provisions more instances as per your traffic and data needs without affecting the throughput of the whole system.  Here is the documentation on load balancing using different GCP nodes here - https://cloud.google.com/load-balancing/docs/load-balancing-overview

Cloud Analytics can prove to be more cost-efficient in the long run

Manually provisioning servers or storage in your local Infrastructure can prove to be costly and the data processing can be slow and inefficient. Cloud-based analytics processing is only billed per usage and storage, server instances, etc can be provisioned as required without too much hassle or the process of resource allocation can be automated as well, as per requirement.

Cloud Analytics can provide better security and firewall provisioning and prevent the local infrastructure from being exposed to the public network

Cloud platforms regularly audit their security and their network and firewall are usually battle-tested before being made available to the internet. This keeps the data on the cloud very secure and almost impossible to access by any malicious group. 

Making your local infrastructure available on the public network can make it susceptible to possible attacks and theft of data in case the security is not audited carefully. This may also require hardware firewalls which can prove to be expensive on the local infrastructure setup.  

Cloud Analytics can allow for faster processing of data

Cloud Analytics can allow you to provision more instances as per the data required to be processed and stored. Cloud analytics can allow you to provision more instances and other resources as per your data processing timeline and budget.

This allows for a more efficient processing and storage of your data. It can depend on your budget, as you have to only pay for the resources you provision.  

It is easier to set up and provision different analytic tools on the cloud vs local infrastructure

Installing and setting up different analytic tools can be much easier on the cloud as compared to setting it up locally. For example, AWS RedShift or GCP BigQuery provisions a complete data infrastructure warehouse structure for your data analysis and processing requirements. It is primarily used for large-scale data analysis, data migration, etc. 

This post covers the basics of a cloud analytics platform, including some of the basic functions of a cloud analytics platform. We cover the benefits of using a cloud analytics platform over running your analytics operations locally. Thank you for reading my post, and if you liked it so far, please consider sharing the post with others as it would help my blog. Thank you.