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Cloud data analytics is important because it allows organizations to process and analyze large amounts of data in a cost-effective and scalable manner. By using cloud-based data storage and processing solutions, companies can avoid the high costs and maintenance required for on-premises infrastructure. Additionally, Cloud Data Analytics allows for flexible, real-time processing, which can enable organizations to quickly respond to changing business needs. Furthermore, using cloud analytics solutions makes it easier for organizations to collaborate and share data across different teams and locations. Moreover, cloud providers offer a variety of storage options that can support the needs of big data projects. Let’s check how cloud complements big data and advanced analytics requirements.

  • Scale: Cloud storage can easily scale to meet the growing storage needs of big data projects, without the need for organizations to invest in additional hardware. Besides that, it also scale to meet the growing computational needs of big data projects for analytics and visualation without the need for organizations to invest in additional hardware.
  • Cost-effectiveness: Cloud storage is generally less expensive than on-premises storage options, making it a cost-effective solution for storing large amounts of data. Cloud can be more cost-effective than on-premises analytics solutions, as organizations only pay for the resources they use.
  • Durability: Cloud storage is designed to be highly durable, meaning that data is stored across multiple servers and locations to ensure that it is not lost in case of a hardware failure.
  • Data warehousing: Cloud providers offer data warehousing services, which can be used to store large amounts of structured data and run analytical queries on it in a cost-effective way. 
  • Object storage: Cloud providers offer object storage, which can be used to store unstructured data like videos, photos, and log files.
  • Data archiving: Cloud providers offer data archiving services, which can be used to store infrequently accessed data at a lower cost than active data.
  • Multi-region replication: Cloud providers offer option to store data in multiple geographic regions, this can be used for compliance, disaster recovery or low-latency access.
  • Advanced analytics: Cloud providers offer a range of advanced analytics services, such as machine learning and artificial intelligence, which can be used to extract insights from big data in sophisticated ways.
  • Real-time data processing: Cloud providers offer services for real-time data processing and analytics, which allow organizations to gain insights from their data as it is generated.
  • No infrastructure maintenance: By using cloud-based analytics, organizations can avoid the need to maintain and update their own analytical infrastructure, which can be time-consuming and expensive.
  • Multi-cloud and hybrid-cloud capabilities : Cloud providers offer the option of multi-cloud or hybrid-cloud architecture, which allows organizations to use multiple cloud providers for their analytics and data processing needs, and also allows to work with data that is on-premises.
  • Serverless computing: Cloud providers also offer serverless computing solutions, like AWS Lambda and Azure Functions, that enable to process and analyze data without provisioning or managing infrastructure. This makes it easy to build and run applications and services in response to events, and automatically scaling to the volume of incoming requests.
  • Cloud-based data processing: Services like AWS Elastic MapReduce, Azure HDInsight, and Google Cloud Dataflow allow organizations to perform large-scale data processing in the cloud. These services provide a platform for running big data processing frameworks like Apache Hadoop and Apache Spark, which enable organizations to process and analyze data in real-time.
  • Cloud-based visualization: Several cloud providers also offer visualization tools that allow organizations to create interactive dashboards and reports. These tools, such as Amazon QuickSight, Microsoft Power BI, and Google Data Studio, allow businesses to easily visualize their data in an intuitive way, without the need for specialized technical skills.

Conclusion

There are many more benefits of using cloud and cloud based products and services to store data and perform advanced analytics for varied business needs. Cloud-based analytics solutions are scalable and cost-effective and offers significant flexibility that traditional on-premises infrastructure cannot provide.

 

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