Azure SQL Database is a fully managed Platform-as-a-Service (PaaS) relational database. It handles patching, backups, and high availability automatically, minimizing administrative overhead. It maintains a relational structure compatible with existing SQL Server applications and offers predictable performance through reserved compute tiers. While SQL Managed Instance is also PaaS, it’s designed for larger-scale migrations with near 100% compatibility; for a standard inventory app, SQL Database is the simpler, more direct fit.

Azure Event Hubs is a big data streaming platform and event ingestion service. It is built to handle massive throughput of millions of events per second with low latency, making it the ideal first stop for telemetry and event data from distributed sources. It can durably capture the stream and publish it to multiple consumers, such as real-time analytics services (Azure Stream Analytics) and long-term storage (Azure Data Lake), enabling both real-time and batch processing scenarios.

Session consistency guarantees read-your-own-writes, monotonic reads, and monotonic writes within the context of a single client session. It’s the default and most commonly used model, offering a good balance of performance and predictability for user-centric applications. Strong consistency, the highest level, guarantees that a read returns the most recent version of an item, acknowledged by all replicas globally, which impacts latency. The core difference is the scope of the guarantee: session (per client) vs. strong (global).

Azure Cosmos DB is a multi-model database service that natively supports key-value and document data models via its Core (SQL) and Table APIs. It is designed for flexible schema, global distribution, and elastic scalability. While Azure Table Storage is a key-value store, it is a more basic, single-region service. Cosmos DB’s Table API offers a premium feature set (global distribution, SLA-backed latency) on the same key-value model, making it the more comprehensive managed solution for a critical application like a financial portal.

Azure Synapse Analytics (formerly SQL Data Warehouse) is a limitless analytics service that brings together big data and data warehousing. Its core architectural feature is the decoupling of compute (dedicated SQL pools) from storage (using Azure Data Lake). This allows you to scale compute resources independently and on-demand to handle unpredictable, highly concurrent query workloads against massive datasets, and then pause the compute to save costs when not in use.

The Archive tier offers the lowest storage cost for data that is rarely accessed and has flexible retrieval latency (hours). An immutable storage policy (using time-based retention or legal hold) ensures that the blobs become non-erasable and non-modifiable for a specified interval, which is a direct requirement for regulatory compliance. Combining the Archive tier for cost with an immutable policy for data integrity is the prescribed solution for long-term legal holds.

A Linked Service in Azure Data Factory is essentially a connection string. It defines the connection information needed for the service to connect to an external data store (like Azure SQL Database, Amazon S3, or an on-premises SQL Server) or a compute resource (like Azure HDInsight or Azure Databricks). Datasets and activities within pipelines reference these linked services to know where to source from or sink data to.

Azure Cache for Redis is a fully managed, in-memory data store based on the open-source Redis. It is specifically designed to provide sub-millisecond data access, making it perfect for caching session state to improve application performance and for real-time leaderboards where low latency is critical. Its data structures (like sorted sets) are ideal for ranking scenarios, and it supports replication across Azure regions for global accessibility.

Azure Databricks is an Apache Spark-based analytics platform optimized for Azure. It provides a collaborative, serverless workspace where data scientists and engineers can use notebooks (in Python, Scala, R, SQL) to perform ETL, exploratory data analysis, and machine learning directly on data stored in Azure Data Lake Storage Gen2 or Blob Storage. Its tight integration with Spark makes it the go-to choice for processing large-scale raw data files with open-source libraries.

Azure SQL Database is a fully managed Platform-as-a-Service (PaaS) relational database. It handles patching, backups, and high availability automatically, minimizing administrative overhead. It maintains a relational structure compatible with existing SQL Server applications and offers predictable performance through reserved compute tiers. While SQL Managed Instance is also PaaS, it’s designed for larger-scale migrations with near 100% compatibility; for a standard inventory app, SQL Database is the simpler, more direct fit.0