You need to store a large amount of data. How can you do it efficiently?

Use Cloud Storage Services: Utilize cloud storage services like Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage to store large amounts of data. Scalability: Leverage the scalability features of cloud services to adjust your storage capacity based on evolving needs.

Cloud computing enables customers to use infrastructure and applications via internet of third parties without installing and maintaining them on premises. It's used on-demand access, hosted at remote data centre managed by a cloud service provider (CSP), cloud utilization provides: - Lower IT cost - Improve agility and time-to-value - Scale more easily and cost-effectively Cloud computing services as SaaS, PaaS, IaaS are the main ones under high utilization, nevertheless FaaS (Function as a Service) allows developers to execute portions of application code in response to different events.

Nowadays, data collection has become a frequent practice in companies and, as a result, concerns about data security and the way in which this data is stored are increasing. When talking about Bigdata there are some interesting options that differ from each other, such as: •Data Lake •Data Hubs • Data Warehouse Data Lake is a form of storage that is centralized and allows the storage, processing and protection of structured or unstructured data, storing the collected data in its original format. Data Hubs is a simple solution For structured data from multiple sources, which is very useful when companies aim to share data efficiently. Data Warehouse is a central repository for structured data, fundamental in Business Intelligence.

Nowadays companies are moving to cloud storage as their first choice. Cloud solutions such as AWS, azure are easily available, have high customisation and provide multiple options to cater to organization needs and usage. For example in AWS non frequent data can be stored in Glacier storage which has low storage cost but high retrieval cost while frequently used data can be stored in S3 which has exactly opposite monetary structure. This gives organization effective cost saving tool whole handling large data storage and retrieval

First step would be to understand the characteristics of the data. Its structure, classification, data type, freshness, access patterns etc. Based on these you can decide on the best storage platform, retrieval patterns, security considerations, compression and archival methodology. Encryption at rest and transit is recommended for better data security. Cloud storage is no brainer.

Adding to the encryption algorithms (symmetric and asymmetric) - Data should be encrypted while at rest, in transit, and in use. Customers need to maintain full control over security keys and hardware security module. - At Rest: information stored on the cloud network ensures that even if the data is lost, stolen or mistakenly shared, the contents are virtually useless without encryption key. - At Transit: usually automatically encrypted through HTTPS protocol, which adds a security sockets layer (SSL) to the standard IP protocol.

Anything that involves storing and processing huge volumes of data at high-speed and requires storage or computing capacity is moving at many organizations to cloud services. Major public cloud providers offer Disaster-Recovery-as-a-Service (DRaaS) to protect data against system failures. The expansion of Big data analytics, IoT, machine learning and AI, cloud offers the on-demand end-user self-service that keeps operations tasks, as spinning up development and test servers, to avoid development bottlenecks to run data analysis, code development and managing opplications. If you are in cloud industry - Focus on infrastructure, security and latency tools, to focus on user needs and time to serve.