What is a Data Cloud?
A data cloud is an integrated data management system that unifies all the data sources, data stores, and supporting data infrastructure in an enterprise. Most large organizations have complex IT infrastructures that can consist of multiple cloud service providers, on-premises resources, and legacy software. This can result in data that is redundant, poorly cataloged, and difficult to manage. A data cloud eliminates such silos and makes it easier for everyone in your organization to access and transform data into consumable insights.
What are the benefits of a data cloud?
A data cloud gives you an infrastructure for efficient data management across multiple systems at any scale. You can ensure data is available to anyone who needs it without compromising data integrity or security. We give some benefits of implementing a data cloud below.
Centralized management
Instead of being limited to isolated data infrastructure, your organization can use a data cloud to collect, process, store, and analyze data from a single unified platform. This ensures better governance and permission control for employees and cloud applications that access the stored data. Instead of administering access permission from multiple locations, your organization can regulate data usage policies from a unified point of control.
Greater mobility
A data cloud supports evolving business trends where data sharing extends beyond physical workspaces. It allows employees to access corporate information securely and efficiently from any part of the world. Your organization can move information between cloud data storage systems like distributed data lakes or data warehouses without being subjected to infrastructure lock-in. You have all the required data management tools to complete this process when you use data cloud providers like Amazon Web Services (AWS).
Better performance
A data cloud makes data exchange between different cloud storage solutions more efficient by using a common data sharing protocol. Cloud applications can access and analyze data without the complexity of additional data manipulation steps for system compatibility. Data cloud solutions also support various types of business data, including transactional and analytical data, without redundant modifications.
Enhanced security
A data cloud solution includes security technologies that can help your organization protect sensitive data in a cloud environment. Many organizations must meet regulatory compliance to protect customer privacy and prevent unauthorized access to stored sensitive information.
By consolidating data access to data stored in the cloud, your organization can apply data security policies and protectionary measures more effectively. For example, AWS Cloud security resources help businesses to automate security tasks and reduce human configuration errors.
Improved accessibility
Organizations use data clouds to break down silos and apply data to business processes as and when needed. Employees in different departments have access to shared datasets on a data cloud platform, which serves as a single source of truth. These employees can access both structured and unstructured data and use it for business intelligence analytics. This allows the entire organization to work cohesively and intelligently and be guided by the same information.
Why is a service level agreement important?
A service level agreement (SLA) is a critical component of an IT services contract that brings together all relevant information in one place. It outlines all agreed-upon metrics, services, and responsibilities, and it ensures both parties have a clear understanding of their requirements. An SLA protects both parties and ensures there are no misunderstandings in the event of a dispute. It clarifies the dispute resolution process in the event of missed obligations. Effective SLAs align to the technology or business objectives of the engagement.
What are the use cases of a data cloud?
We give some examples of common data cloud use cases below.
Cloud-centered application development
Developers build cloud-centered applications by performing the entire development lifecycle on the cloud. For example, they write codes, manage databases, and test and deploy the application on cloud-hosted platforms. A data cloud eases development by making it easier for developers to deal with data. It also puts applications closer to data, which is important for web applications that stream large amounts of real-time data.
Data sharing
Data sharing is important to improve efficiency and collaboration between employees. Similarly, access to shared data is also important for application users and commercial customers. Data cloud tools provide a frictionless data movement among parties that rely on timely information. A data cloud replaces the legacy data exchange process that requires several interoperating data storage modules that move information from one siloed storage to another.
Business analytics
You can use a data cloud to combine structured and semi-structured data for analysis as well as load it into the cloud database. Business analysts use a data cloud to discover actionable insights from various sources of data and improve business outcomes. On the other hand, data engineers solve the challenge of creating multiple nonstandard data pipelines in business analytics practices.
Backup and recovery
Businesses know the importance of an effective backup-and-recovery mechanism to ensure operational continuity. However, the exponential growth of data has made the task of moving data between storage on different platforms very challenging.
Instead, a data cloud platform offers a better recovery option by hosting all mission-critical workloads and backup storage within connected infrastructure. You can rely on backup systems and recover data quickly in case of outages. For example, organizations use AWS DataSync to back up data from on-premises resources to Amazon Simple Storage Service (Amazon S3).
How does a data cloud work?
Data sources and data architecture are two main components of a data cloud. It’s also important to know about cloud data platforms.
Data sources
Data sources are the original collections of data in their unprocessed form. Data can originate from multiple unrelated sources, such as email, social media, customer relationship management (CRM) logs, and sales transactions.
Data architecture
Data architecture describes the methods you can use to segregate and organize data on the cloud according to its intended usage. We give some common data architectures below.
Data lake
A data lake stores raw data. Raw data is unprocessed information that might come from the cloud, on-premises resources, or edge-computing devices.
Data warehouse
A data warehouse stores structured data that is meant for specific business purposes. Data warehouses provide readily available data for business intelligence and analytics.
Data lakehouse
A data lakehouse combines the cost efficiency of a data lake and the structural data management approach of a data warehouse. It also includes features such as machine learning and data analytics services that help organizations run business intelligence queries.
Data mesh
A data mesh is a decentralized data storage that allows your organization to scale data analytics. Instead of concentrating data management capabilities on a monolithic data storage, a data mesh distributes data ownership according to respective business domains.
Cloud data platforms
A cloud data platform helps organizations ingest data from on-premises storage into multi-cloud environments. It integrates different data architectures in a single self-managed portal that allows businesses to maximize the value of structured, semi-structured, and unstructured data. Instead of managing multiple data tools, your organization can use a cloud data platform to manage, govern, analyze, and secure business data effortlessly.
What are the challenges of implementing a data cloud?
While a data cloud empowers digital transformation, your organization might face some obstacles when shifting data to the cloud environment.
Data ingestion options
If your organization wants to shift from an on-premises environment to a data cloud, you have several options to choose from. You can transfer data through a direct connection, offline, or a combination of both. The question lies in which method is best suited for your business requirements.
AWS offers several methods of moving data from on premises to the cloud. For example, data centers use AWS File Gateway to expand on-premises storage to the AWS Cloud.
Data integrity
While moving data into the data cloud, your organization must ensure that data integrity is not compromised. This requires the IT team to verify that every data file moved to the cloud has the exact metadata and information as the original copy. This might require writing special programs to preserve metadata during data ingestion.
Technical expertise
Shifting data to the cloud requires data management expertise in the cloud domain. Your company might need to allocate additional resources to train existing IT teams or hire cloud specialists to migrate and manage data on a new platform. Supportive data cloud tools like AWS Glue help organizations ease into the transition by automatically orchestrating data workflow.
How can AWS support your data cloud implementation?
AWS provides the broadest selection of services that fit all your data cloud needs. Our services allow organizations of all sizes and industries to reinvent their business with data. AWS for Data helps you get all the insights and best practices you need to build a modern data strategy.
Explore AWS for Data resources across several categories:
- For analytics, AWS provides the broadest selection of analytics services that fit all your data analytics needs.
- For databases, AWS offers relational databases with unparalleled performance at 1/10th of the cost of enterprise grade commercial databases and eight purpose-built databases.
- For building a data-driven culture, AWS shows how to get more value out of your data through a combination of mindset, people, technology, and process.
- For artificial intelligence and machine learning (AI/ML), AWS meets you where you are in your AI/ML journey. AWS offers innovative services like Amazon SageMaker and pre-trained AI services to help you address common business problems.
Get started with data clouds on AWS by creating a free AWS account today.
