See the explanation below.
Explanation
Create a general-purpose v2 storage account, which provides access to all of the Azure Storage services: blobs, files, queues, tables, and disks.
1. On the Azure portal menu, select All services. In the list of resources, type Storage Accounts. As you begin typing, the list filters based on your input. Select Storage Accounts.
2. On the Storage Accounts window that appears, choose Add.
3. Select the subscription in which to create the storage account.
4. Under the Resource group field, select Create new. Enter the name for your new resource group, as shown in the following image.

5. Next, enter the name account10543936 for your storage account.
6. Select a location for your storage account, or use the default location.
7. Leave these fields set to their default values:
Deployment model: Resource Manager
Performance: Standard
Account kind: StorageV2 (general-purpose v2)
Replication: Read-access geo-redundant storage (RA-GRS)
Access tier: Hot
8. Select Review + Create to review your storage account settings and create the account.
9. Select Create.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/storage-account-create

Explanation

Step 1: Deploy the Azure Databricks service
Create an Azure Databricks workspace by setting up an Azure Databricks Service.
Step 2: Deploy a Spark cluster and then attach the required libraries to the cluster.
To create a Spark cluster in Databricks, in the Azure portal, go to the Databricks workspace that you created, and then select Launch Workspace.
Attach libraries to Spark cluster: you use the Twitter APIs to send tweets to Event Hubs. You also use the Apache Spark Event Hubs connector to read and write data into Azure Event Hubs. To use these APIs as part of your cluster, add them as libraries to Azure Databricks and associate them with your Spark cluster.
Step 3: Create and configure a Notebook that consumes the streaming data.
You create a notebook named ReadTweetsFromEventhub in Databricks workspace.
ReadTweetsFromEventHub is a consumer notebook you use to read the tweets from Event Hubs.
References:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-stream-from-eventhubs

Explanation:
Execution results and diagnostics: Azure Storage
Job launcher and tracker: Job Service
Job metadata and state: Control database
The Job database is used for defining jobs and tracking the status and history of job executions. The Job database is also used to store agent metadata, logs, results, job definitions, and also contains many useful stored procedures, and other database objects, for creating, running, and managing jobs using T-SQL.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-job-automation-overview

Explanation

Box 1: CSV
A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. A CSV file stores tabular data (numbers and text) in plain text. Each line of the file is a data record.
JSON and AVRO are not formatted as one record per row.
Box 2: "type":"Microsoft.ServiceBus/EventHub",
Properties include "EventHubName"
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-inputs
https://en.wikipedia.org/wiki/Comma-separated_values

Explanation
The following query returns the transaction log size on each distribution. If one of the log files is reaching 160 GB, you should consider scaling up your instance or limiting your transaction size.
-- Transaction log size
SELECT
instance_name as distribution_db,
cntr_value*1.0/1048576 as log_file_size_used_GB,
pdw_node_id
FROM sys.dm_pdw_nodes_os_performance_counters
WHERE
instance_name like 'Distribution_%'
AND counter_name = 'Log File(s) Used Size (KB)'
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-manage-monitor