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ACID? 🤔

In the world of data engineering and warehousing projects, the concept of ACID transactions is crucial to ensure data consistency and reliability. ACID transactions refer to a set of properties that guarantee database transactions are processed reliably and consistently. ACID stands for Atomicity , Consistency , Isolation , and Durability . Atomicity : This property ensures that a transaction is treated as a single, indivisible unit of work. Either the entire transaction completes successfully, or none of it does. If any part of the transaction fails, the entire transaction is rolled back, and the database is returned to its state before the transaction began. Consistency : This property ensures that the transaction leaves the database in a valid state. The database must enforce any constraints or rules set by the schema. For example, if a transaction tries to insert a record with a duplicate primary key, the database will reject the transaction and roll back any changes that have alre...
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Databricks: What is Databricks?

Data is the lifeline of any organization, and with the growing importance of data, companies have been looking for more effective ways to manage, store, and analyze their data. One of the most popular solutions that have emerged in recent years is Databricks. In this blog post, we'll take a closer look at what Databricks is, how it works, and why it has become so popular. What is Databricks? Databricks is a cloud-based platform that provides a unified environment for data engineering, data science, and machine learning. It was founded in 2013 by the creators of Apache Spark, a popular open-source big data processing framework. Databricks is built on top of Apache Spark and provides a managed version of Spark, along with other big data tools and services. Databricks provides a range of features that make it a powerful tool for managing and processing big data. These include: Unified Data Analytics Platform : Databricks provides a single platform for data engineering, data science, a...
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dbt (Data Build Tool) Overview: What is dbt?

If you're working with data, you've probably heard of the ETL process: Extract, Transform, Load. The ETL process is fundamental to data warehousing and analytics, but it can be challenging to implement and maintain. That's where dbt comes in. dbt, or Data Build Tool, is an open-source command-line tool that enables data analysts and engineers to transform, test, and document data using SQL. dbt was developed by Fishtown Analytics and has quickly become a popular tool in the data community. What is dbt used for? dbt is primarily used for building data pipelines and analytics systems. It allows data analysts and engineers to transform raw data into usable formats, test the transformed data to ensure accuracy, and document the entire process for future reference. One of the key benefits of dbt is that it uses SQL as its primary language. This makes it easy for data analysts and engineers to use the tool without having to learn a new programming language or framework. dbt can b...
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Gold Layer Explained

  Medallion Architecture is a data warehousing methodology that was introduced by Ralph Kimball. The architecture is designed to provide a flexible and scalable framework for data warehousing. In Medallion Architecture, data is stored in a series of layers, each layer providing a specific set of functions. One of the key layers in Medallion Architecture is the Gold Layer. In this blog post, we will take a detailed look at the Gold Layer in Medallion Architecture. What is the Gold Layer? The Gold Layer is the central layer in the Medallion Architecture. It is also known as the enterprise data warehouse layer. This layer contains the most important and trusted data in the data warehouse. The data in this layer is highly aggregated, cleansed, and integrated. The Gold Layer provides a single source of truth for the entire organization. The Gold Layer is designed to support decision-making processes at the enterprise level. The data in this layer is stored in a highly normalized format ...
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CETAS in Synapse Analytics

In Azure Synapse Analytics, creating external tables can be a powerful way to work with large volumes of data in various file formats without loading it into the data warehouse. The CREATE EXTERNAL TABLE AS SELECT (CETAS) command is a useful feature in Synapse Analytics that allows you to create external tables directly from SQL SELECT statements. In this blog post, we will explore how to use CETAS with the OpenRowset function to create external tables in Synapse Analytics. What is CREATE EXTERNAL TABLE AS SELECT (CETAS)? The CETAS command in Azure Synapse Analytics is a powerful feature that enables you to create an external table from the results of a SQL SELECT statement. With CETAS, you can create an external table directly from the results of a query, which can be useful for creating ad-hoc reports, running data transformations, or performing other operations on data outside of the data warehouse. CETAS can be used to create external tables in various file formats, including Parqu...
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Silver Layer Explained

The Silver Layer is an essential component of the Medallion data warehouse architecture, as it sits between the source systems and the analytical layer of the warehouse. The primary function of the Silver Layer is to prepare and cleanse data before it is loaded into the analytical layer. This is done to ensure data accuracy and consistency, making it suitable for analysis and decision-making. The Silver Layer is composed of three critical components: data integration , data quality , and data transformation . Data Integration involves collecting data from various sources and integrating it into a single dataset. This process involves extracting data from source systems, transforming it into a common format, and loading it into the Silver Layer. The goal of this component is to ensure that all data is in a consistent format, making it easier to analyze and report on. Data Quality is essential in any data warehouse. Poor data quality can lead to inaccurate reporting, flawed analyses, ...
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Data Wrangling in Azure

Data wrangling, also known as data cleaning or data preprocessing, is the process of transforming raw data into a format that is more suitable for analysis . This is an important step in any data-driven project, as it ensures that the data being analyzed is accurate, complete, and relevant to the problem at hand. Microsoft Azure provides a range of tools and services that can be used to perform data-wrangling tasks. In this blog post, we will provide an overview of what data wrangling is and how to do it in Azure. What is Data Wrangling? Data wrangling is the process of transforming raw data into a format that is more suitable for analysis. This involves several steps, including cleaning, transforming, and integrating data from various sources. Cleaning: This step involves removing any duplicate or irrelevant data, correcting any errors, and filling in missing values. Transforming: This step involves converting the data into a format that is  more suitable for analysis. ...
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