What is the performance optimization available for the SQL server production server?

here are several performance optimization options available for a SQL Server production server. Here are a few examples:

Indexing: Indexes can be used to improve the performance of queries by providing a faster way to retrieve data from tables.

Query optimization: You can use tools such as the SQL Server Query Optimizer to analyze and optimize the performance of queries.

Partitioning: You can use partitioning to divide a large table into smaller, more manageable pieces, which can improve the performance of queries and maintenance tasks.

Data compression: You can use data compression to reduce the size of data in tables and indexes, which can improve performance by reducing the amount of I/O required to access the data.

Hardware upgrades: Upgrading the hardware on the server, such as the processor, memory, and storage, can improve performance.

Load balancing: You can use load balancing to distribute the workload across multiple servers, which can improve performance.

Monitoring and tuning: Regularly monitoring the performance of the server and tuning the configuration and settings as needed can help to optimize performance.

By implementing these optimization options, you can improve the performance of a SQL Server production server.

What are performance options in azure data factory copy activity ?

In Azure Data Factory, the Copy Activity is used to copy data from a source data store to a destination data store. There are several options that you can use to optimize the performance of the Copy Activity. These options include:

Data partitioning: You can use data partitioning to divide the data into smaller chunks, which can be processed in parallel to improve performance.

Parallel copy: You can use parallel copy to copy data from the source to the destination in parallel, which can improve performance.

Batch size: You can specify the batch size for the copy operation, which determines the number of rows that are processed in each batch. Increasing the batch size can improve performance, but it can also increase the risk of errors.

Concurrency: You can specify the number of concurrent copy operations that can be performed. Increasing the concurrency can improve performance, but it can also increase the risk of errors.

Data compression: You can use data compression to reduce the size of the data being transferred, which can improve performance.

Data caching: You can use data caching to store a copy of the data in memory, which can improve performance by reducing the number of read operations required.

Custom connectors: You can use custom connectors to optimize the copy operation for specific data stores or scenarios.

By using these options, you can optimize the performance of the Copy Activity in Azure Data Factory.

Azure Data Architect Interview Questions

Py4JJavaError : An error occurred while calling o536.json. : Operation failed: "This request is not authorized to perform this operation.", 403, HEAD, https://storageaccount.dfs.core.windows.net/container/?upn=false&action=getAccessControl&timeout=90

Py4JJavaError : An error occurred while calling o536.json. : Operation failed: "This request is not authorized to perform this operation.", 403, HEAD, https://storageaccount.dfs.core.windows.net/container/?upn=false&action=getAccessControl&timeout=90

If we try to access data from ADLS gen2 without the "Storage Blob Data Contributor" role on the storage account, they will receive the error message: Operation failed: "This request is not authorized to perform this operation.",403.

Once the storage account is created, select Access control (IAM) from the left navigation. Then assign the following roles or ensure they are already assigned. Assign yourself to the Storage Blob Data Owner role on the Storage Account.