With the exponential growth in data, organizations rely on the limitless compute, storage, and analytical power of Azure to scale, stream, predict, and see their data. Analytics solutions turn volumes of data into useful business intelligence (BI), such as reports and visualizations, and inventive artificial intelligence (AI), such as forecasts based on machine learning.
Whether your organization is just starting to evaluate cloud-based analytics tools or is looking to expand your current implementation, Azure provides many options. The workflow starts with learning about common approaches and aligning processes and roles around a cloud mindset.
Data can be processed in batches or in real-time, on-premises or in the cloud, but the goal of any analytics solution is to make use of data at scale. Increasingly, organizations want to create a single source of truth for all the relational and nonrelational data being generated by people, machines, and the Internet of Things (IoT). It's common to use a big data architecture or an IoT architecture to transform raw data into a structured form, then move it to an analytical data store. This store becomes the single source of truth that can power a multitude of insightful analytics solutions.
Download a Visio file of this architecture.
Learn about analytics on Azure
If you're new to analytics on Azure, the best place to learn more is with Microsoft Learn, a free, online training platform. You'll find videos, tutorials, and hands-on learning for specific products and services, plus learning paths based on your job role, such as developer or data analyst.
Organizational readiness
If your organization is new to the cloud, the Cloud Adoption Framework can help you get started. This collection of documentation and best practices offers proven guidance from Microsoft designed to accelerate your cloud adoption journey. It also lists innovation tools to democratize data in Azure.
To help assure the quality of your analytics solution on Azure, we recommend following the Azure Well-Architected Framework. It provides prescriptive guidance for organizations seeking architectural excellence and discusses how to design, provision, and monitor cost-optimized Azure solutions.
Path to production
Knowing how to store your data is one of the first decisions you need to make in your journey to analytics on Azure. Then you can choose the best data analytics technology for your scenario.
To get started, consider the following example implementations:
Best practices
High-quality analytics start with robust, trustworthy data. At the highest level, information security practices help ensure that your data is protected in transit and at rest. Access to that data must also be trusted. Trustworthy data implies a design that implements:
At the platform level, the following big data best practices contribute to trustworthy analytics on Azure:
Orchestrate data ingestion using a data workflow or pipeline solution such as those supported by Azure Data Factory or Oozie.
Process data in place using a distributed data store, a big data approach that supports larger volumes of data and a greater variety of formats.
Scrub sensitive data early as part of the ingestion workflow to avoid storing it in your data lake.
Consider the total cost of the required Azure resources by balancing the per-unit cost of the compute nodes needed to the per-minute cost of using those nodes to complete a job.
Create a data lake that combines storage for files in multiple formats, whether structured, semi-structured, or unstructured. At Microsoft, we use Azure Data Lake Storage Gen2 as our single source of truth. For example, see BI solution architecture in the Center of Excellence.
Additional resources
Analytics is a broad category and covers a range of solutions. The following resources can help you discover more about Azure.
Hybrid
The vast majority of organizations need a hybrid approach to analytics because their data is hosted both on-premises and in the cloud. Organizations often extend on-premises data solutions to the cloud. To connect environments, organizations must choose a hybrid network architecture.
A hybrid approach might include mainframe and midrange systems as a data source for Azure solutions. For example, your organization may want to modernize mainframe and midrange data or provide mainframe access to Azure databases.
Example solutions
Here are a few sample implementations of analytics on Azure to consider:
AWS or Google Cloud professionals
These articles can help you ramp up quickly by comparing Azure analytics options to other cloud services: