Azure Data Lake Storage is specifically designed to be the initial place data is ingested into Azure, before being further processed and moved into a more organized and structured data storage such as Azure SQL Database or Azure Cosmos DB for further analysis. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/learn/modules/examine-components-of-modern-data-warehouse/3-explore-azure-data-services-warehousing

Yes, Azure Data Factory supports many different internal and external data sources for it's Copy Data activity, including Redshift. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipelines-activities

Azure Data Studio is a modern data development environment that offers all of those features. In the Azure Portal, you can run some basic queries against your database, but won't get access to those featuers. sqlcmd is a command line tool. Microsoft Visual Studio offers a lot of those features for code development, and not specifically for SQL Server data development. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/sql/azure-data-studio/what-is-azure-data-studio?view=sql-server-ver15

Paginated reports are great when you need all of the details in one place. Instead of printing thousands of rows on the screen, you paginate them which makes it easier to navigate. It can also be printed. Charts and graphs are more likely to appear on a dashboard, where data needs to be summarized. And I would not suggest creating a report that spans 100,000s of pages because no one will every read that. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/power-bi/paginated-reports/paginated-reports-report-builder-power-bi

Azure Databricks is a data analytics platform optimized for the Microsoft Azure cloud services platform. Azure Databricks offers three environments for developing data intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning. Databricks Data Science & Engineering provides an interactive workspace that enables collaboration between data engineers, data scientists, and machine learning engineers. For a big data pipeline, the data (raw or structured) is ingested into Azure through Azure Data Factory in batches, or streamed near real-time using Apache Kafka, Event Hub, or IoT Hub. This data lands in a data lake for long term persisted storage, in Azure Blob Storage or Azure Data Lake Storage. As part of your analytics workflow, use Azure Databricks to read data from multiple data sources and turn it into breakthrough insights using Spark. Refer to Microsoft Doc:

https://docs.microsoft.com/en-us/azure/databricks/scenarios/what-is-azure-databricks

Streaming data is processed as it arrives.

Starting a SQL Database in Azure costs nothing up front ($0), you simply pay for the ongoing per hour cost of running the database.

Azure File Storage can support Premium storage on premium disks, or transaction optimized storage, hot and cool options as well. Refer to Microsoft Doc:

https://azure.microsoft.com/en-us/pricing/details/storage/files/

Descriptive analytics helps answers questions about what has happened, based on historical data.

Key-value data is designed for quick storage and retrieval of data based on a key. It's not good for doing searches across multiple keys, or examining the contents of multiple values to find the relevant ones. Refer to Microsoft Doc:

https://docs.microsoft.com/en-gb/azure/architecture/data-guide/big-data/non-relational-data