Accelerate Time to Real Insights by Building a Cloud Analytics Stack

Leading companies are bypassing traditional approaches to BI and reporting in favor of leveraging new technologies that accelerate time to insight and do more with less. They’re growing their businesses and outpacing the competition by building modern cloud analytics technology stacks that empower everyone to harness data in advanced and unprecedented ways.

Digitally Transform Your Business with the Limitless Scale and Flexibility of Analytics in the Cloud

Organizations today have more applications that generate more data than ever before, but traditional analytics solutions prevent teams from unlocking its full value.

Harnessing the automation and elasticity of cloud services eliminates mundane system maintenance so data teams can focus on innovative and influential data projects. For business users hungry for on-demand answers to open-ended questions, cloud-based analytics delivers true self-service ad hoc data exploration that fuels business outcomes.

Why Traditional Data Analytics Stacks Approaches Hold Businesses Back

Focus on pre-configured reporting dashboards
Scalability and performance challenges with data sets with billions of rows
Require SQL or other specialized coding skills to ask questions of the data
Require data to be extracted or copied out for detailed analysis
Risk of data fragmentation and proliferation
Expensive and resource-intensive
Accumulate technical debt to make adding new data and use cases more difficult

Why a Modern Cloud Data Analytics System Fuels Success

Agile exploratory ad hoc analysis
Leverage the virtually unlimited scale, speed, and concurrency of your cloud platform
No-code spreadsheet user experience for users of all skill levels
All data remains securely in the cloud data platform
Centralized governance with pass through permissions to cloud data platform
Analytics-as-a-service with near-zero setup and maintenance
Flexible foundation for future expansion and growth without lock-in

“Adopting a modern cloud-based analytics stack was a game-changer for PAYLOAD. It allows us to harness the full, unbridled power of our data, and the results we’ve seen from a business velocity perspective speak for themselves.”

Chris Lambert CEO, PAYLOAD

Components of the Cloud Data Analytics Stack

The modern cloud data analytics stack consists of three layered technologies and cloud-based services that collect, store, and analyze data. Together, these tools allow organizations to unlock the full value of their data and fuel smarter decision-making for all.

Data Sources

Data must be collected and integrated across applications, databases, files, and more so it can be easily accessed, modeled, and holistically analyzed. The data sources vary depending on the business and analytics use cases, but they generally include some combination of:

Popular SaaS applications: Salesforce, HubSpot, Netsuite, Marketo, Google Analytics, Facebook Ads, Amazon S3, Microsoft Dynamics, etc.
Traditional on-premises applications and databases: Oracle, SAP, SQLserver, Excel, etc.
New data sources: Click streams, log files, smart devices, social media, etc.

Layer 1: Data Pipeline

The modern data pipeline is a SaaS solution that automatically connects and normalizes data from across sources, preparing it for storage and querying using analysis-ready schemas.

Key considerations when selecting a data pipeline solution include:

Offers out-of-the-box connectivity to popular data sources, SaaS applications, and more.
Does the heavy-lifting with integrations, pre-built modeling tools, and data building tools (dbt).
Keeps data fresh, automatically checking for and updating any changes made.

Examples include: Fivetran, Matillion

Layer 2: Cloud Data Platform

Cloud Data Platform AKA a Cloud Data Warehouse as a service (DWaaS) is the centralized repository for all of an organization’s data, offering the elastic infrastructure, unlimited scale, cost-effective risk mitigation, security management, and other cloud-specific benefits traditional on-prem warehouses do not support.

Key considerations when selecting a DWaaS solution include:

Allows you to store, transform, model, and combine any kind of data including structured, semi-structured, or unstructured (e.g. JSON).
Offers near-infinite scalability at the speed and power needed to keep data safe, current, and complete.
Saves engineering resources by offering a fully managed and maintained solution.

Examples include: Snowflake Data Cloud, Amazon Redshift, and Google BigQuery.

Layer 3: Cloud-native Analytics

Cloud-native analytics tools give everyone the ability to directly query live data from the cloud data platform down to row-level detail — no manual SQL or proprietary coding required — while maintaining strict data governance. To prepare and model your data, the best practices for modeling and building datasets in Sigma can be found here.

Key considerations when selecting a cloud analytics solution include:

Connects directly to the cloud data platform in minutes to start doing analytics in real-time – no copies or extracts required.
Operates at cloud scale and speed – no slowing down or crashing with large data sets over billions of rows.
Offers an easy-to-use interface with functionalities and governance that allow team members to reuse and repurpose analyses for easier collaboration.