In this article, we will learn how to get started using PostgreSQL on Docker. PostgreSQL is one of the most popular open-source databases that is being used by a lot of developers. It is highly stable and has a large community that maintains and supports the database development lifecycle. PostgreSQL can be installed on any operating system like Windows, macOS, and Linux. With the rise in containerization technologies, PostgreSQL can now also be installed using Docker.
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In this article, we are going to discuss in detail what a spatial database is and the data type, POINT that is supported by PostgreSQL to perform geographic data analysis.
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In this article, we are going to talk about Power BI Incremental Refresh. Power BI is the cloud-based business intelligence tool developed by Microsoft. Using Power BI, users can create various kinds of modern reports and dashboards that provide insights and information of their data in a meaningful way. Power BI is a strong and independent BI tool that is available for both Windows desktop and also the cloud. In order to demonstrate the capabilities of Power BI Incremental Refresh, we will leverage Power BI Desktop to develop and author the data model and then publish it to the Power BI cloud where it will be refreshed incrementally.
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Working with Date and Time calculations is an integral part of any Data Analytics solution. Be it financial data or any data related to sales, dates always play a pivotal role in analyzing the trends and hence draw a pattern out from them. Some of the most commonly used KPIs that involve date and time calculations are monthly sales, quarterly web traffic, year-over-year growth etc. These KPIs help businesses understand and gauge their performance and also aids in making decisions necessary for their development. In this article, we are going to explore some of the important Date and Time functions that can be used in PostgreSQL and how they can be implemented henceforth.
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In this article, we are going to learn about the various DAX functions that are available for the summarization and aggregation of data. DAX, abbreviated as Data Analysis Expressions, is developed by Microsoft as a scripting language to interact with the Microsoft Business Intelligence stack, such as Power BI, SSAS Tabular, Power Pivot, etc. If you have prior experience working with Microsoft Excel, learning DAX Functions will not be too much of a hassle for you. One of the most popular tools in the BI market that uses DAX Functions is Power BI. Power BI can be used without using DAX, however, using DAX Functions in Power BI helps us to build powerful data models which also helps in performance optimization.
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In this article, we are going to use SQL Server to query data that resides in Amazon S3 buckets with the help of Amazon Athena. SQL Server is one of the most popular relational database management systems developed by Microsoft. SQL Server can be installed on-premises or on popular cloud services like Azure or AWS. For the purpose of this demonstration, we are going to use SQL Server that has been installed on-premises. Also, as a pre-requisite for this tutorial, we would need to have an AWS account valid and some knowledge about Amazon Athena.
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In this article, we are going to understand the various types of Power BI time calculations and how to implement these calculations. We will also understand the reason behind implementing such time-based calculations and the importance of these in the real world. The Power BI time calculations are also known as Time Intelligence functions that can be applied in DAX. There are a lot of functions available under Time Intelligence and you can read about it in detail from the official documentation from Microsoft.
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In this article, we are going to learn in detail about Azure Time Series Insights. Microsoft Azure is one of the leading cloud providers these days. With a lot of companies adopting or migrating to the cloud these days, it has become a usual trend to convert existing technologies into cloud-based services and consume them. This not only helps the companies to reduce their cost but also in turn allows them to focus on more business-related problems rather than concentrating on infrastructure costs.
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In this article, we are going to learn about the different types of MySQL Window Functions and how to use them as per different use cases. MySQL is one of the most commonly used databases in the software world today. Almost every other web application running uses MySQL as a database, either on-premise or on the cloud. It becomes extremely necessary for developers and database users to master their querying abilities such that they can query the databases as per the requirements and produce suitable results. In this article, we are going to focus on the MySQL Window Functions and how to use them with practical examples. An important point to note is that most of the MySQL Window Functions are applicable only to versions 8 or higher.
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In this article, we are going to learn how to trigger Function Apps from Queue Storage in Azure. Function Apps has been one of the most popular cloud services of Microsoft Azure. Function Apps allow users to write code in any language and then execute the code in the cloud. There is no infrastructure to be managed and hence is very flexible for writing and building applications on the go. Every Function App can be triggered in multiple ways, for example, by calling the function URL using an HTTP endpoint or from some other functions in Azure. In this article, we are going to trigger the Function App from Queue Storage in Azure and see how to pass a message from the queue to the Function App.
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In this article, we are going to talk about the Azure Queue Storage service using python as a programming language. As it goes by the name, Azure Queue Storage is a simple queue and messaging service in Azure that can be used to build queues and process messages in an asynchronous fashion. Queues help to decouple multiple systems and provide the middle layer in a distributed architecture. The Queue Storage service in Azure allows users to connect to and store messages up to 64 KB in size from anywhere in the world. It is exposed by using HTTP or HTTPS endpoints and can contain millions of messages that need to be processed.
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In this article, we are going to learn about the most popular Power BI Interview Questions being asked while applying for a Data Analyst role. Power BI is one of the most commonly used data visualization tools in the market. It is offered as a desktop tool using which you can author your reports and dashboards and then publish and share them with your users on the cloud. Power BI is also somewhat similar to Excel, so users who have prior experience with working in Excel can learn it quite easily.
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In this article, we are going to learn about working with Python in Power BI. Power BI has been adding a lot of features to it recently and one of the most essential was adding Python functionality to it. Users are now able to run Python scripts directly from within Power BI. Python is a great programming language, especially when it comes to the field of data analysis. Most of the machine learning frameworks and data science libraries are written in Python and there is a huge community of open-source programmers that maintain and promote the use of Python along with these libraries.
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In this article, we are going to learn about implementing and managing security in Power BI Workspaces. Power BI is one of the leading data visualization tools that is used by a plethora of companies around the globe. Power BI has risen to prominence in recent years due to the ease of use and familiarity of concepts with Microsoft Excel, which helped a lot of analysts tutor themselves and learn to work in Power BI. Power BI is now available in both, desktop and the cloud and users can choose to start with anyone. However, the desktop version of Power BI, known as the Power BI Desktop is used mainly for creating simple and complex data models within the Power BI environment and then published to the cloud, known as the Power BI Service.
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In this article, we are going to explore how to embed Power BI reports in Jupyter notebooks. Power BI is one of the most popular data visualization tools in the market that is being heavily used by Data Analysts to visualize existing data and to discover patterns lying in the data. Power BI provides a desktop application to author the reports and it can be deployed to the Power BI Service and consumed by users using a web browser.
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In this article, we are going to talk about the various analytic functions that are supported by the SQL Server Database Engine. As it goes by the name, these are some special functions using which we can execute analytic queries on the dataset and obtain useful results. In comparison to standard SQL queries, sometimes it becomes necessary for the Data Analysts to deep dive into the data more and obtain insights from analytic perspectives. This article will discuss all the analytic functions supported by SQL Server with some details.
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Microsoft Azure has been a leading cloud service provider over the past few years. In this article, we are going to look into an overview of various cognitive services offered by Azure. In simple English, the word ‘Cognitive’ means to involve in intellectual activities, such as thinking or reasoning. Similarly, Azure Cognitive Services are also related to intelligence related to the computer and the way humans behave with these computers. For example, these services allow humans to interact with the machines as if they have the intellectual ability to talk, think and respond to events accordingly.
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This article is a part of the series Learn NoSQL in Azure where we will learn about the DocumentDB SQL API in detail. Azure Cosmos DB is a serverless NoSQL database service provided by Azure that is highly available, globally distributed, and responds within the minimum latency. Due to these features, it is a perfect fit for building modern-day applications using the DocumentDB or SQL API of Azure Cosmos DB.
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This article is a part of the series – Learn NoSQL in Azure where we explore Azure Cosmos DB as a part of the non-relational database system used widely for a variety of applications. Azure Cosmos DB is a part of Microsoft’s serverless databases on Azure which is highly scalable and distributed across all locations that run on Azure. It is offered as a platform as a service (PAAS) from Azure and you can develop databases that have a very high throughput and very low latency. Using Azure Cosmos DB, customers can replicate their data across multiple locations across the globe and also across multiple locations within the same region. This makes Cosmos DB a highly available database service with almost 99.999% availability for reads and writes for multi-region modes and almost 99.99% availability for single-region modes.
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In this article, we are going to learn Azure Cosmos DB. This article is a part of the series Learn NoSQL in Azure, where we will explore all the different types of non-relational databases that are supported in Azure at the moment. Azure is one of the most popular public cloud platforms that has a big market share all over the world. Cosmos DB is a part of the Databases section in Azure that allows customers to create and use NoSQL or non-relational databases and consume these at scale. You can leverage Cosmos DB to build highly scalable and robust cloud-based applications that support modern big data workloads. Let us understand more about what a NoSQL database is all about and how it is different from a relational database. Although this article focuses on the NoSQL related to Azure, it is to be known that other open-source projects support NoSQL databases like Apache Cassandra, etc. However, these topics are out of the scope of this article and we will focus on Azure mostly.
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In this article, we are going to learn about PGAdmin, a PostgreSQL management tool. As you are aware SQL Server Management Studio (SSMS) and the MySQL Workbench are the GUI management tools for SQL Server and MySQL respectively. Similarly, in order to manage the Postgres database and its services, PGAdmin is used. PGAdmin is a web-based GUI tool used to interact with the Postgres database sessions, both locally and remote servers as well. You can use PGAdmin to perform any sort of database administration required for a Postgres database.
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Spatial data deals with data related to geography. In this article, we are going to understand the various concepts related to geographic or spatial data and how PostgreSQL can be leveraged as a database to store such geographic information. As you might be already aware, PostgreSQL is a popular and widely used open-source relational database management system that can handle production workloads very easily. With the availability of the cloud, you can quickly spin up instances of Postgres on major public cloud providers like AWS, Azure, GCP, etc.
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In this article, I am going to talk about using regular expressions in a Postgres database. Regular Expressions, also known as RegEx are pattern matching criteria that can filter data based on the pattern. It is heavily used to match string values to a specific pattern and then filter the results based on the condition. From a beginner’s perspective, these regular expressions can seem to be quite complex in the first, however, as you will start using these on a daily basis, you will come to the underlying logic, and then you can start writing your own RegEx statements.
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In this article, we are going to understand what the Power BI Dataflows is all about and how we can get started by building dataflows in Power BI Service. A dataflow can simply be considered as an extract transform and load pipeline that can be used to connect to source data, transform the data by applying business rules, and then finally preparing the data to be available to visualize. In a general data architecture, a dedicated ETL tool is used to prepare and transform the data, which is then loaded into a data warehouse. Power BI was used to connect to this data warehouse and visualize the data from here on.
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In this article, I am going to discuss in-depth setting up docker and running a SQL Server Docker image. SQL Server, as you might already be aware that it is one of the most popular relational database technologies in today’s world. SQL Server is widely used in various applications that support transactional and analytical workloads. Docker, on the other hand, is a containerization technology using which you can bundle your applications within a container and distribute them. Docker helps users to build applications independent of the underlying operating system. We will learn more about Docker and using the SQL Server Docker image in this article.
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