In this post “Interactive Data Analysis with SQL Server using Jupyter Notebooks“, we will demonstrate how we can use Jupyter Notebooks for interactive data analysis with SQL Server. Jupyter notebooks are one of the most useful tools for any Data Scientist/Data Analyst. It supports 40+ programming languages and facilitates web-based interactive programming IDE. We can put comments, headings, codes, and output in one single document. This document maintains the context to the original data source which means we can re-execute the code whenever we need it. This feature facilitates Data scientists/Data analysts to play with the code during the presentations. Also, these notebooks are very handy in sharing and can be shared easily across the teams.
In the previous chapter, we discussed the T-SQL’s Insert statement. Now, in this chapter, we are going to discuss the T-SQL’s UPDATE command. We will discuss how we can use the T-SQL’s Update command with the help of examples. We will also discuss some best practices we need to follow while using the UPDATE command in SQL Server.
What is UPDATE command
UPDATE command is used to modify existing records in a SQL table. Depending on the need, we can update all or a few selected records in a table. Also, we can update the records in a table based on the records of other tables. However, in this case, we need to join all the tables in the FROM clause of the UPDATE statement. Let’s have a look at the syntax of the UPDATE statement which updates all rows of the table:
Syntax of UPDATE command in SQL… More
In this post, we are going to discuss how we can export SQL Server table data to an Excel file or to a CSV file using Python’s pandas library. Prior to SQL Server 2017, we could use one of the below methods to export data from SQL Server to Excel or CSV file:
- Create an SSIS package to export the data from SQL Server – This option can be a good choice if we want to reuse the export process again and again. Also, if we want to put moderate/complex transformations during data export, this option can be a better choice.
- Use SQL Server Import/export wizard – SQL Server provides in-built data export/import wizard which can be used in case we want to export data with no/minimal transformations.
- OS-based copy-paste functionality – We can simply copy the query output from SQL Server using Ctrl + C option and then open
Sometimes when we try to restart “SQL Server service” we might get an error “Windows could not stop the SQL Server (MSSQLSERVER) service on Local Computer” with error code and description “Error 1061: The service cannot accept control messages at this time“. In this post, “SQL Server – Error 1061: The service cannot accept control messages at this time”, we will discuss the workaround which can help us to fix this issue.
This error occurs when we try to restart the SQL Server service using SSMS object explorer or/and using the services console. Let’s try to restart the SQL Server service using object explorer and services console.
Restarting SQL Server services using Object explorer:
Restarting SQL Server using services console:
Below is the error screenshot:
Fix – Error 1061: The service cannot accept control messages at this time
To fix this issue, we can … More
Apache Spark is a very powerful general-purpose distributed computing framework. It provides a different kind of data abstractions like RDDs, DataFrames, and DataSets on top of the distributed collection of the data. Spark is highly scalable Big data processing engine which can run on a single cluster to thousands of clusters. To follow this exercise, we can install Spark on our local machine and can use Jupyter notebooks to write code in an interactive mode. In this post “Read and write data to SQL Server from Spark using pyspark“, we are going to demonstrate how we can use Apache Spark to read and write data to a SQL Server table.
Read SQL Server table to DataFrame using Spark SQL JDBC connector – pyspark
Spark SQL APIs can read data from any relational data source which supports JDBC driver. We can read the data of a SQL Server table … More
During the installation of SQL Server 2017(Or other versions), we can get an error “The RPC server is unavailable” at the very last step of the installation process while executing the action “DReplayControllerConfigAction_install_postmsi_Cpu64“. “The RPC server unavailable error” might also occur at the “Server Configuration” step during the installation process. However, typically this error occurs when we try to install SQL Server on a Remote/Virtual Machine.
Below is the screenshot of the RPC error you may get during the installation of SQL Server 2017 on a remote machine:
The above error message stops the installation process at the final step and when we click on “Retry” button, it keeps prompting the same error message again and again. If you do not get any appropriate solution, this solution might help you to resolve this RPC error which has occurred due to the domain name issue on the … More
Resampling time series data in SQL Server using Python’s pandas library
In this post, we are going to learn how we can use the power of Python in SQL Server 2017 to resample time series data using Python’s pandas library. Sometimes, we get the sample data (observations) at a different frequency (higher or lower) than the required frequency level. In such kind of scenarios, we need to modify the frequency of the given samples as per the frequency of the required outcome. Modifying the frequency of time series data using T-SQL query becomes a tedious task especially when we need to perform upsampling as we need to generate more rows than what we have in the sample dataset. The Python’s pandas module has in-built capabilities for frequency conversion. With the help of pandas resample method, we can increase or decrease the time series observation frequencies with only few lines of … More
In this post, we are going to learn how we can apply a conditional GROUP BY clause on a column, based on the values of another column. Assume that we have a table named tbl_EmpSaleDetail which contains the sales records for each employee. Let’s have a look at the table data.
In the above table, we have these columns:
EmpName – Stores the name of the employee
SaleDate – Date of sale
SaleAmount – Amount of the sale
IsActive – Indicates whether the employee is active or not.
Now, we need this output.
In this output, we can see that all the data of inactive employees have been aggregated to a single row labeled as “–Inactive Employees Sales–” (Highlighted in red). However, the sum of the sales of the active employees are aggregated individually. Before writing the conditional group by query, lets create the sample table … More
If we need to import data from an excel file into SQL Server, we can use these methods:
- SQL Server Import Export Wizard
- Create an SSIS package to read excel file and load data into a SQL Server table
- Use T-SQL OPENROWSET query
- Use the read_excel method of Python’s pandas library (Only available in SQL Server 2017 onwards)
In this post “Python use case – Import data from excel to sql server table – SQL Server 2017”, we are going to learn that how we can use the power of Python in SQL Server 2017 to read a given excel file in a SQL table directly. With the integration of Python in SQL Server 2017, we can use the pandas read_excel method to read a given excel file with lots of customizations in SQL Server.
Import zipped CSV file without unzipping it in SSIS using SQL Server 2017
SQL Server Integration Services (SSIS) is one of the most popular ETL tools. It has many built-in components which can be used in order to automate the enterprise ETL(Extract, Transform, and Load). Also, if we need a customized component which is not available in SSIS, we can simply create it by writing our own piece of code in C# using Script Task or Script Component.
In this post, we are going to explore that how we can read and load a zipped CSV file in SQL Server without unzipping it using SSIS along with SQL Server 2017. Reading a zipped file directly (without unzipping it) will save some time required in order to write the text file on the physical disk and then reading it from there. As of now, we don’t have any built-in component in … More