Some best practices using Azure Data Platform: 

Structured and Unstructured data required different storage and processing. Structured data is a fixed format data with schema, types, and relationships. It requires a lot of upfront planning and is equally difficult to modify afterwards. It is frequently used to store application data for online transaction processing. The semi-structured data is very flexible format with various models such as key/value, document, etc. The emphasis is on long-term flexibility and modifications and is best suited to dynamic applications such as social media. Media files, text files and office documents are most frequently used unstructured data. 

Azure storage accounts can store blobs, queues, files and blocks. The kind of storage account determines the supported storage services, performance tier, and pricing. Data can be replicated in the primary region to a secondary region. The access tier influences the pricing and access latency. The hot, cool or archive access tiering suits data ageing and supports lifecycle management. The Gen2 storage supports hierarchical namespace. Object data can be streamed globally, and at scale. Lower latency and higher throughput come with performance tiers. Replication and accessibility must be part of the design decisions. 

Companies that require relational databases have a variety of products and offerings from Azure. They can choose to directly host a database on Azure using a fully managed offering that supports common SQL Server features. Otherwise, they could choose to deploy a managed SQL Server instance. If they want even more parity and control with a traditional on-premises database, then they could create Azure SQL Server virtual machines which provide full control and access. Those three options are ordered in terms of trade-offs between cloud native and full control and access. Cloud native resources come with built-in backup, patching, and recovery and provide 99.995% availability guarantee. They also integrate with Azure Active Directory. Azure SQL managed instances are deployed on a managed virtual cluster that Microsoft manages. It provides a private IP address and support for most migrations to the cloud. The SQL purchasing models can be DTU-based for predictive and linear increase between compute and storage or vCore based for scattered and independent compute versus storage. vCore also supports Azure Hybrid Benefits which supports porting on-premises SQL Server licensing. The General Purpose Service Tier uses blob storage at about 5-10 ms latency, the Business Critical uses SSD at about 1-2 ms latency and 16TB database, and Hyperscale supports up to 100TB databases. Azure SQL virtual machines provide full control and access with relaxed limits. 

The Data Lake storage is ideal to store huge amounts of varied or unstructured data and is built on top of block blobs. It enables common analytical features and access and is especially useful to store large volumes of text with support for hierarchical namespaces. It is accessible by Hadoop services and supports a superset of POSIX for finer-grained access controls. Synapse Analytics combines data warehousing and big data analytics. It has tools for data integration from diverse sources and powers analytics with massively parallel processing. The resource pools can be SQL pool, Spark pool and Synapse supports pipelines for data movement and transformation using Azure Data Factory workflows and supports connectivity to CosmosDB for near real-time analytics. The models served by Synapse can be rich. Databricks are favored for Apache Spark-based big data and machine learning. Environments with Databricks can run SQL queries on a data lake, provide a collaborative workspace for working on big data pipelines and analytics and end-to-end integrations for ML experiments, model training and serving. The premium tier can help with management, security and monitoring with audit logs, notebooks, cluster, job RBAC, Azure AD passthrough and IP access lists. 

The right choices for cloud engineering can bring tremendous value to data engineering professionals.