Data Centric World

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...

  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 ...

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...

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, ...

  Azure Synapse Analytics is a powerful tool for working with big data, and one of the key features of this platform is its ability to quickly and easily load data from a variety of sources. In this blog post, we will explore the different data loading methods available in Azure Synapse Analytics, along with examples of how to use each one. Azure Data Factory : Azure Data Factory is a fully managed data integration service that allows you to create, schedule, and manage data pipelines. With Azure Data Factory, you can easily move data from a variety of sources, such as flat files, databases, and cloud storage, into Azure Synapse Analytics. Example: {     "name": "AzureDataFactoryPipeline",     "properties": {         "activities": [             {                 "name": "CopyFromBlobToSynapse",                 "type": "Copy",   ...

Data transformation is a crucial step in the data processing pipeline and Azure Synapse provides several methods to perform data transformation tasks. In this blog post, we will discuss some of the most commonly used data transformation methods in Azure Synapse with code examples. Mapping Data Flow: Mapping Data Flow allows you to define data transformation tasks by creating a flow of data between source and destination datasets. You can use built-in transformation tasks such as filtering, aggregation, and joining data. Example: {     "name": "ExampleDataFlow",     "properties": {         "activities": [             {                 "name": "Source",                 "type": "Source",                 "policy": {                     "timeout": "7.00:00...