Continuous Integration and Continuous Deployment (CI/CD) – SQL Server Database testing using tSQLt – Part 4

In the previous posts, we have created a Continuous integration and a Conntinuous Deployment pipeline for a SQL Server database using the Azure DevOps server. Also, we have demonstrated how we can set up the cross-database dependency for a SQL Server database project in the Azure DevOps pipeline. Below are the links in case you […]

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Continuous Integration and Continuous Deployment (CI/CD) – SQL Server Database project dependency – Part 3

Previously, we have created an Azure DevOps Continuous Integration (CI) and Continuous deployment (CD) pipelines to independently deploy a SQL Server database project. However, in an enterprise data warehouse environment, the databases are mostly dependent on other databases. Because, in DWH, we access objects from multiple databases (like accessing the staging layer objects into the

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Continuous Integration and Continuous Deployment (CI/CD) – SQL Server Database CD – Part 2

In the previous post, we have created a CI (Continous Integration) pipeline for a SQL Server database project. For this demo, as like our previous demo, we will be using a SQL Server instance running on an on-prem machine along with a locally installed Azure DevOps Server. Please note that we are not using the

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Continuous Integration and Continuous Deployment (CI/CD) – SQL Server Database CI – Part 1

In this post, we are going to discuss how we can enable continuous integration and continuous deployment for a SQL Server Database project using Azure DevOps Server. For this demo, we will be using a SQL Server instance running on-prem along with a locally installed Azure DevOps Server. Continuous integration and continuous delivery (in short

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Cleanup historical data in Temporal table using Retention Policy

Temporal tables were introduced by Microsoft in SQL Server 2016. From SQL Server 2016 onwards, temporal tables can be used to capture DML changes on a table without writing a single line of code. We have already discussed temporal tables in multiple blogs which can help us to understand it in more detail: Temporal Table

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Audit created and modified by user in temporal table (without trigger)

Microsoft introduced Temporal tables in SQL Server 2016. Temporal tables are system versioned tables that have built-in support for row-level change tracking. We can create a new table as a temporal table or we can convert an existing table into a temporal table. The temporal tables keep the current data into the main table and

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An Introduction to mssql-cli – Cross platform Interactive command line tool for SQL Server

In this post, we are going to discuss a new cross-platform and interactive command-line query tool that can be used to communicate with Microsoft SQL Server. Unlike sqlcmd command-line utility, mssql-cli supports cross-platform and can be used on Windows, macOS, Linux, Ubuntu, Debian, CentOS, Fedora, and etc. This is an open-source tool and it supports

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Relationship between Binomial and Poisson distributions

In this post, we are going to discuss the Relationship between Binomial and Poisson distributions. We know that Poisson distribution is a limit of Binomial distribution for a large n (number of trials) and small p (independent probability for each trial) values. A large number of trials n with very small probability p indicates a

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Convert Jupyter notebooks to PDF

Jupyter lab is the next-generation web-based UI experience for Jupyter notebook users. It facilitates a tab-based programming interface that is highly extensible and interactive. It supports 40+ programming languages. We have already discussed how we can use Jupyter notebooks for interactive data analysis with SQL Server. With the help of Jupyter notebooks, we can keep

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Interactive Data Analysis with SQL Server using Jupyter Notebooks

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

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