SQL performance tuning is a difficult task, especially when working with large amounts of data, where even slight changes can have a significant (positive or negative) influence on performance. A Database Administrator (DBA) will be in charge of most SQL performance adjustments in mid-sized and large businesses. However, believe me when we say that there are plenty of developers that have to perform DBA-like jobs.

The MySQL database is a popular option for storing data. It's powerful, reliable, and easy to use. However, it can be challenging to work with if you don't have the right tools. Luckily, Python has an API for MySQL that makes working with this database simple. Connecting to MySQL with Python code is a great way to build a rich set of data through programming and to create database content quickly.

Stitch is pleased to announce the availability of Microsoft SQL Server as a destination. MS SQL Server joins nine other data destinations (including Microsoft Azure Synapse) that Stitch supports to help execute all your data modeling and analysis projects. Stitch customers can immediately benefit from the new destination, which supports both Azure SQL Server and standard SQL Server editions reaching as far back as SQL Server 2012.

As a company’s data assets grow, the need for cloud computing increases in tandem. For keeping pace with this growth, Snowflake stands above the rest. What makes Snowflake so special? This cloud-agnostic platform takes the best of traditional database technology and combines it with modern cloud computing to drive the agility and innovation companies need to remain competitive. It features on-the-fly scaling, flexible clustering options, and the capability to hold several petabytes of information.

As one of the most popular relational database management systems, MYSQL gets a lot of use from a variety of applications. With the popularity of Python in web-based applications, it's essential that MySQL and Python can communicate with one another. Read on to learn more about MySQL and to discover three ways to integrate it with Python.

A good database schema is indispensable for making data warehouses work. Get it wrong, and you’ll be in a world of hurt.

Follow these tips to ensure that your database schema delivers maximum utility for data warehouse users, data analysts, and data scientists.

The post pandemic period is one wherein being tech-savvy has become an absolute necessity. Today, business-houses have got to store huge amounts of data while simultaneously analyzing it. And although spreadsheets could combat this problem to an extent, they’re not a dependable solution in the long run. This is why the need for a versatile database management tool has risen, which can have all your information and ideas just a click away.

Data and information are the main currency of the 21st century. When managing applications, we have to deal with reams of data and store it for future use, either in our file system, in the memory of the application or in designated data management apps, also known as databases. Traditionally, databases have been local applications that required installation, management and maintenance.

Heroku Postgres databases are powerful tools for managing and analyzing data. You can run large-scale data processes for your business off of Heroku Postgres. While management is made much simpler with Heroku, the insight of a skilled developer and data analyst when manipulating databases makes a big difference in the outcomes. This is especially true when migrating a database. Here are five things you need to know about migrating Heroku Postgres databases.