Box 1: Azure Data Lake Storage Gen2
Azure Data Explorer integrates with Azure Blob Storage and Azure Data Lake Storage (Gen1 and Gen2), providing fast, cached, and indexed access to data stored in external storage. You can analyze and query data without prior ingestion into Azure Data Explorer. You can also query across ingested and uningested external data simultaneously.
Azure Data Lake Storage is optimized storage for big data analytics workloads.
Use cases: Batch, interactive, streaming analytics and machine learning data such as log files, IoT data, click streams, large datasets
Box 2: Azure SQL Database Hyperscale
Azure SQL Database Hyperscale is optimized for OLTP and high throughput analytics workloads with storage up to 100TB.
A Hyperscale database supports up to 100 TB of data and provides high throughput and performance, as well as rapid scaling to adapt to the workload requirements. Connectivity, query processing, database engine features, etc. work like any other database in Azure SQL Database.
Hyperscale is a multi-tiered architecture with caching at multiple levels. Effective IOPS will depend on the workload.
Compare to:
General purpose: 500 IOPS per vCore with 7,000 maximum IOPS
Business critical: 5,000 IOPS with 200,000 maximum IOPS
Incorrect:
* Azure Synapse Analytics Dedicated SQL pool.
Max database size: 240 TB -
A maximum of 128 concurrent queries will execute and remaining queries will be queued.
Reference:
https://docs.microsoft.com/en-us/azure/data-explorer/data-lake-query-data
https://docs.microsoft.com/en-us/azure/azure-sql/database/service-tier-hyperscale
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-service-capacity-limits
