Amazon Athena is a fully managed, popular cloud service provided by Amazon to query structured data in S3. It allows users to write custom SQL queries to retrieve data from CSV or other file formats in S3. This is helpful if you want to query your static data at rest.
Being a pioneer in the cloud computing world, AWS defines Athena as “an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.”
Nowadays, within the SQL Server DBA community, it is extremely likely that we use, or at the very least have heard of, the famous stored procedure sp_WhoIsActive developed by Adam Machanic.
During my time as a DBA, I used the SP to immediately check what’s going on inside a particular SQL Server instance when it is getting all the “finger-pointing” that a particular application is running slow.
However, there are occasions where such issues become recurring and require a way to capture what’s going on to find a potential culprit. There are also scenarios where you have several instances serving as the backend for 3rd party applications. The Stored Procedure could work potentially well to find our culprits.
From the Customer Segmentation tutorial, you already know that Oracle Analytics provides several Machine Learning (ML) algorithms to solve the Supervised Learning and Unsupervised Learning problems with low-code required.
Prediction of Employee Attrition is one of such Supervised Learning problems. It is a popular use case that most organizations need to resolve to minimize the attrition rate. You train a model with a labeled dataset, and Employee is labeled Yes or No for Attrition in the dataset.
Machine Learning (ML) has come from a buzzword that is nice to have in your application to a must-have feature that works and adds value. Data scientists develop ML models in various ML Frameworks like TensorFlow, Scikit-learn, PyTorch, Azure ML, etc. Before ML.NET became available to all developers, adding the ML functionality to .NET applications required knowledge in some ML frameworks to build and train ML models. It also demanded additional time and effort to integrate that model into .NET applications.
The Common Table Expression aka CTE in SQL Server provides a temporary result set in T-SQL. You can refer to it within a SQL Select, SQL Insert, SQL Delete, or SQL Update statement.
The option is available from SQL Server 2005 onwards, helping the developers write complex and long queries involving many JOINs, aggregation, and data filtering. Usually, developers use subqueries for writing T-SQL codes, and SQL Server stores these CTE in memory temporally until the query execution finishes. Once the query is finished, it is removed from memory.
Nowadays, companies are moving their infrastructure from on-premise devices to the cloud. There are a few major cloud vendors like AWS, Azure, and GCP that dominate the market shares.
When applications are deployed to the cloud, more and more data is generated daily. However, the humongous amount of data generated each minute has no value until these data are properly analysed. This brings us to the rise of Data Visualization tools allowing us to visualize large data.
In my previous article about SQL Server System Databases, we learned about each system database that comes as part of SQL Server installation. The current article will focus on frequently faced issues around the tempdb database and how to resolve them correctly.
SQL Server is one of the most popular databases used in modern applications. Over the past few decades, it has established itself as a leading database provider that drives most of the applications in the Microsoft ecosystems. It provides unique features and easy integration with technologies.
In the last few years, JSON has positioned itself as a standard data exchange format between services, although XML is still widely used. In the SQL Server 2016, Microsoft implemented JSON support directly in the database engine, and the capabilities of data manipulation are increasing in each following version.
There are two primary methods to view the tables in MySQL:
In this article, we are going to explore them both.
