Wide World Importers (WWI) is a wholesale novelty goods importer and distributor operating from the San Francisco bay area.

As a wholesaler, WWI's customers are mostly companies who resell to individuals. WWI sells to retail customers across the United States, including specialty stores, supermarkets, computing stores, tourist attraction shops, and some individuals. WWI sells to other wholesalers via a network of agents who promote the products on WWI's behalf. While all of WWI's customers are currently based in the United States, the company intends to expand into other countries.

WWI buys goods from suppliers, including novelty and toy manufacturers, and other novelty wholesalers. They stock the goods in their WWI warehouse and reorder from suppliers as needed to fulfill customer orders. They also purchase large volumes of packaging materials and sell these in smaller quantities as a convenience for the customers.

Recently WWI started to sell a variety of edible novelties such as chili chocolates. The company previously did not have to handle chilled items. To meet food handling requirements, they must monitor the temperature in their chiller room and any of their trucks that have chiller sections.

Wide World Importers is designing and implementing a Proof of Concept (PoC) for a unified data analytics platform. Their soft goal is to bring siloed teams to work together on a single platform.

In this lab, you will play the role of various persona: a data engineer, a business analyst, and a data scientist. The workspace is already set up to focus on some of the core development capabilities of Azure Synapse Analytics.

By the end of this lab, you will have performed a non-exhaustive list of operations that combine the strength of Big Data and SQL analytics into a single platform.

If you are using a hosted lab environment, please follow the steps below to get started:

1. Select the Lab Environment tab above the lab guide to copy the Azure credentials used for the lab. Make note of the UniqueId value. This value may be referenced at different points during the lab.

   

2. Select Lab Resources under Lab Environment to start the Virtual Machine (VM) provided for this lab. However, you do not need to use the VM to complete the lab. It is there for your convenience to make it easier to sign into Azure if you have an existing account and do not want to log out of it.

   

The diagram below provides a unified view of the exercises in the lab and their estimated times for completion.

In this exercise, you will explore data using the engine of your choice (SQL or Spark).

Understanding data through data exploration is one of the core challenges faced today by data engineers and data scientists as well. Depending on the data's underlying structure and the specific requirements of the exploration process, different data processing engines will offer varying degrees of performance, complexity, and flexibility.

In Azure Synapse Analytics, you have the possibility of using either the SQL Serverless engine, the big-data Spark engine, or both.

In this exercise, you will use a pipeline with parallel activities to bring data into the Data Lake, transform it, and load it into the Azure Synapse SQL Pool. You will also monitor the progress of the associated tasks.

Once data is properly understood and interpreted, moving it to the various destinations where processing steps occur is the next big task. Any modern data platform must provide a seamless experience for all the typical data wrangling actions like extractions, parsing, joining, standardizing, augmenting, cleansing, consolidating, and filtering.

Azure Synapse Analytics provides two significant categories of features - data flows and data orchestrations (implemented as pipelines). They cover the whole range of needs, from design and development to triggering, execution, and monitoring.

In this exercise, you will build a Power BI report in Azure Synapse Analytics.

The visual approach in data exploration, analysis, and interpretation is one of the essential tools for both technical users (data engineers, data scientists) and business users. Having a highly flexible and performant data presentation layer is a must for any modern data platform.

Azure Synapse Analytics integrates natively with Power BI, a proven and highly successful data presentation and exploration platform. The Power BI experience is available inside Synapse Studio.

In this exercise, you will try to understand customer details using a query and chart visualizations. You will also explore the performance of various queries.

SQL data warehouses have been for a long time the center of gravity in data platforms. Modern data warehouses are capable of providing high performance, distributed, and governed workloads, regardless of the data volumes at hand.

The Azure Synapse SQL Pools in Azure Synapse Analytics is the new incarnation of the former Azure SQL Data Warehouse. It provides all the modern SQL data warehousing features while benefiting from the advanced integration with all the other Synapse services.

In this exercise, you will leverage a model trained in Azure Databricks to make predictions using the T-SQL PREDICT statement in an Azure Synapse Analytics dedicated SQL pool.

Azure Synapse Analytics provides support for using trained models (in ONNX format) directly from dedicated SQL pools. What this means in practice, is that your data engineers can write T-SQL queries that use those models to make predictions against tabular data stored in a SQL Pool database table.

The models can be trained in Azure Databricks using Spark ML - the machine learning library included with Apache Spark. Models can also be trained using other approaches, including by using Azure Machine Learning automated ML. The main requirement is that the model format must be supported by ONNX.