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What is cloud analytics?
Cloud analytics involves both using data stored in the cloud for analytic processes and leveraging the fast computing power of the cloud for faster analytics. Historically, organizations have stored their data in on-premise data centers. These physical servers limited the amount of data that organizations could keep, cost large amounts of money to operate, and consumed vast amounts of time when performing any kind of data-related process.
As data volume exploded and big data became paramount to success, organizations needed greater data storage capabilities and faster insights. With the rise of the cloud, organizations could access powerful remote servers through the internet. These cloud data storage centers — including cloud data warehouses and cloud data lakes — could consolidate data from numerous sources, keep that data secure, and transform the way data was delivered and used. Instead of expensive physical servers, organizations could leverage the cloud as a fast, scalable, and cost-efficient way to store and process data, enabling cloud-based analytic processes that were not previously possible.
What are the different types of cloud analytics?
Cloud-based data analysis can span any or all aspects of an analytic workflow, from data access to visualization, and typically involve multiple analytic tools — any of which could utilize the storage and compute power of the cloud for scalability and real-time insights.
For example, an organization could host its data using Snowflake, a popular cloud data storage platform; manipulate that data and build analytic models using Alteryx, a low-code, no-code Analytics Automation platform; and finally, use a visualization and business intelligence solution like Tableau to create interactive dashboards and share those insights with stakeholders.
When it comes to the cloud itself, there are three variations organizations can use for their cloud infrastructures.
A public cloud is a computing service and infrastructure that third parties operate and offer to the public through the internet. They are usually free, but sometimes, users may be able to pay for additional features and storage.
Private clouds have all the same benefits as a public cloud but offer greater control and security. Private clouds may be either a cloud service that third parties provide through the internet or a private internal network of physical, on-premise servers. These are typically much more expensive than public clouds.
A hybrid cloud structure employs both public and private clouds depending on the use case. For example, organizations may use a public cloud to store and access non-sensitive data at a low cost while using a private cloud for storing and accessing highly-sensitive information.
How do cloud analytics work?
While any step of the data analytics process can leverage the cloud for data storage and cloud computing, there are two core mechanisms that the cloud enables, ETL (extract-transform-load) and pushdown processing.
Before the cloud, organizations relied on ETL, or extract-transform-load, to store data and build data pipelines. ETL is essentially the process of taking data from siloed, legacy sources and loading that data into a data warehouse in a specified format.
The problem was that this data had narrowly-defined use cases and was typically reserved for IT. But with the rise of big data and the need for real-time insights, every part of the organization now needs data to answer key business questions and make data-driven decisions, but this requires accessing data quickly and in different formats.
ETL doesn’t easily facilitate multiple use cases or fast data retrieval or data processing. While the underlying methods behind ETL will always be important (taking data from legacy systems and loading it into a data repository for transformation), the cloud has changed the order in which this process can be done, allowing for much greater optimization.
ELT (extract-load-transform) uses the computing power of the cloud to handle the “T,” or transformation part of ELT with a process called pushdown processing. Pushdown processing is where workloads are “pushed” to cloud data warehouses. These data analytics workflows can run much faster inside a cloud data warehouse rather than on a physical computer.
Pushdown processing greatly decreases processing times (usually up to 90%) and dramatically shrinks processing costs. By loading data into a cloud data warehouse or cloud data lake before transforming it, organizations can also use data for multiple purposes rather than losing critical data from previous transformations.
Key benefits of cloud analytics and how they drive growth
Everyone in an organization should be able to access and utilize data and analytics, regardless of department, role, skillset, or location. Cloud analytics solutions play a pivotal role in making this democratization possible. As more organizations harness the power of the cloud across their data analytic processes, they can make data-driven business decisions. Here are some of the key benefits organizations can expect as they adopt and grow their usage of cloud-based analytics:
Democratized access to data and analytics automation
Instead of relying on IT and creating bottlenecks around analytic processes and data sets, business users across the organizations can gain self-service, real-time access to data and use that data for multiple purposes to drive growth.
A centralized data repository
Organizations can take disparate data from legacy or siloed sources and store it in a central location for easy governance, security, and accessibility.
Dramatically reduced costs
The scalability of the cloud gives organizations the flexibility to only pay for the storage they need. As the business grows, so can its cloud usage. In addition, users can run data processes at scale, bypassing traditional costs associated with on-premise analytics.
Faster time to value
Using traditional methods, analyzing large data sets could take hours. With the power of cloud computing, users can analyze these same data sets in minutes, allowing for seamless use of data and greater business decisions.
Transform your insights with Alteryx Designer Cloud
Alteryx Analytics Cloud is a cloud analytics solution that delivers powerful, easy-to-use data analytic capabilities to everyone in the business and allows for collaboration across teams to support end-to-end enterprise analytics solutions. This unified, enterprise-ready solution provides automated data preparation & analytics, approachable machine learning, and automated insights with interactive data stories.
Key Features of Alteryx Analytics Cloud
Self-Service For Everyone
- Allow everyone to participate in the data analytics process regardless of job role, skill level, or department with an intuitive drag-and-drop user experience and analytic apps
- Enable users to communicate and collaborate to support cross-departmental, end-to-end analytical business initiatives
- Upskill employees with built-in recommendations, tips & tricks, best practices, and templates shared by the Alteryx community and its 370,000+ experts and everyday users
- Empower everyone to transform data into business insights with self-service, cloud-native data engineering, analytics, and data science from the comfort of your web browser.
- Combine, prepare, and enrich disparate data sources for analytics
- Use analytics, geospatial, and approachable machine learning to become more predictive and anticipate future demands
- Distribute insights generated from AI (artificial intelligence) across the organization to fuel data-driven decision-making
- Deploy a flexible, scalable, and open enterprise-grade platform that integrates seamlessly into your existing data analytics architecture with best-in-class security and governance
- Connect to a wide range of data sets across cloud-based and on-premise sources, extensible with REST and JDBC frameworks
- Accelerate user onboarding with on-demand, self-service provisioning
- Adapt to evolving business needs with simplified, flexible licensing and packaging