Introduction
Daily operations and interactions with customers create a constant flow of data. This world of big data is growing steadily, and so is the need to store, process, and analyze the data in a timely and cost-efficient way. Big data requires large amounts of scalable storage space. Because huge volumes of data flow in at high velocity from various sources, the ability to identify and respond to meaningful events is key. Additionally, data is generated in various formats: structured/semi-structured data and free text, as well as images and videos. In order to find correlations between these different data flows, businesses invest significant time and money into parsing, processing, and storing this data. A robust end-to-end data analytics system that can manage your huge, complex data and run advanced analytics is essential to make data-driven business decisions. What tool can help you manage this vast array of data types, work flows, and visualizations?
Azure Data Explorer is a fully managed, high-performance, big data analytics platform. Azure Data Explorer can take all this varied data, and then ingest, process, and store it. You can use Azure Data Explorer for near real-time queries and advanced analytics, as well as for more advanced features such as geospatial analytics, alerting, dashboarding, and business analytics.
Example scenario
Imagine you work at a clothing company that is a large chain of brick-and-mortar stores that's expanding into e-commerce. You're about to launch your end of year sale targeting several international audiences. You want to see how your campaign impacts sales, inventory, and logistics. You have a large volume of data flowing in different formats, and need to figure out a way to make sense of this data and use it to make good business decisions.
Different divisions across the company are going to use the collected data to inform their strategic and day-to-day decisions on operations, marketing, and customer relations. They plan to use Azure Data Explorer to ingest various data types into a single collection comprised of:
- structured data, such as internal operations systems.
- semi-structured data, such as marketing clickstream data.
- unstructured data, such as social media feeds.
Then each division can use data analysis and visualization to make data-driven decisions about the campaign.
What will we be doing?
Analyzing the capabilities of Azure Data Explorer to help you decide when to use it:
- What are the strengths of Azure Data Explorer and the Kusto Query Language?
- How do you work with the service?
- What types of data can you analyze and where can the data come from?
- How can you organize, display, or make the results of your queries actionable?
What is the main goal?
By the end of this session, you're able to decide whether Azure Data Explorer is a good choice to help you make sense of your big data.