Explanation
Linked services are much like connection strings, which define the connection information needed for Data Factory to connect to external resources.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/enable-customer-managed-key
https://docs.microsoft.com/en-us/azure/data-factory/concepts-linked-services
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

Explanation

Table Description automatically generated
Box 1: DimEvent
Box 2: DimChannel
Box 3: FactEvents
Fact tables store observations or events, and can be sales orders, stock balances, exchange rates, temperatures, etc Reference:
https://docs.microsoft.com/en-us/power-bi/guidance/star-schema

Explanation
According to the information I found on the web, two options that you should include in the solution to minimize how long it takes to perform queries and joins on non-partitioned tables are:
Z-Ordering: This is a technique to colocate related information in the same set of files. This co-locality is automatically used by Delta Lake in data-skipping algorithms. This behavior dramatically reduces the amount of data that Delta Lake on Azure Databricks needs to read123.
Apache Spark caching: This is a feature that allows you to cache data in memory or on disk for faster access. Caching can improve the performance of repeated queries and joins on the same data. You can cache Delta tables using the CACHE TABLE or CACHE LAZY commands.
To minimize the time it takes to perform queries against non-partitioned tables and joins on non-partitioned columns in Delta Lake on Azure Databricks, the following options should be included in the solution:
A: Z-Ordering: Z-Ordering improves query performance by co-locating data that share the same column values in the same physical partitions. This reduces the need for shuffling data across nodes during query execution. By using Z-Ordering, you can avoid full table scans and reduce the amount of data processed.
B: Apache Spark caching: Caching data in memory can improve query performance by reducing the amount of data read from disk. This helps to speed up subsequent queries that need to access the same data. When you cache a table, the data is read from the data source and stored in memory. Subsequent queries can then read the data from memory, which is much faster than reading it from disk.
References:
Delta Lake on Databricks: https://docs.databricks.com/delta/index.html
Best Practices for Delta Lake on
Databricks: https://databricks.com/blog/2020/05/14/best-practices-for-delta-lake-on-databricks.html

Explanation
Graphical user interface, text, application Description automatically generated

Explanation
Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar format.
Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance.
It is especially good for queries that read particular columns from a "wide" (with many columns) table since only needed columns are read, and IO is minimized.
Reference: https://www.clairvoyant.ai/blog/big-data-file-formats