Analytics Vidhya

DECEMBER 28, 2022

Source: [link] Introduction If you are familiar with databases, or data warehouses, you have probably heard the term “ETL.” As the amount of data at organizations grow, making use of that data in analytics to derive business insights grows as well. For the […].

ETL 218

ETL AWS Data Warehouse Data Science 218

Analytics Vidhya

MAY 16, 2022

Introduction on ETL Tools The amount of data being used or stored in today’s world is extremely huge. Many companies, organizations, and industries store the data and use it as per the requirement. While handling this huge amount of data, one has to […].

ETL 271

ETL Data Science Analytics Analytics 271

Analytics Vidhya

FEBRUARY 4, 2023

Introduction This article will explain the difference between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) when data transformation occurs. In ETL, data is extracted from multiple locations to meet the requirements of the target data file and then placed into the file.

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ETL Analytics Analytics Data Warehouse 298

Analytics Vidhya

SEPTEMBER 1, 2021

This means that the analyst must not only be able to build models, but also have sufficient data engineering skills to […]. The post Introduction to Data Engineering- ETL, Star Schema and Airflow appeared first on Analytics Vidhya.

ETL 295

ETL Data Engineer Data Engineering Data Engineering 295

Analytics Vidhya

JUNE 15, 2022

This article was published as a part of the Data Science Blogathon. Introduction ETL is the process that extracts the data from various data sources, transforms the collected data, and loads that data into a common data repository. Azure Data Factory […].

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ETL Data Pipeline Azure Data Science 270

Data Science Blog

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

Data Warehouse 259

Data Warehouse Azure SQL Database 259

Pickl AI

DECEMBER 15, 2024

Summary : This guide provides an in-depth look at the top data warehouse interview questions and answers essential for candidates in 2025. Covering key concepts, techniques, and best practices, it equips you with the knowledge needed to excel in interviews and demonstrates your expertise in data warehousing.

Data Warehouse 52

Data Warehouse ETL Database Data Quality 52

Analytics Vidhya

JULY 20, 2022

Introduction Data acclimates to countless shapes and sizes to complete its journey from a source to a destination. Be it a streaming job or a batch job, ETL and ELT are irreplaceable. Before designing an ETL job, choosing optimal, performant, and cost-efficient tools […].

Data Pipeline 361

Data Pipeline ETL Data Science Analytics 361

Data Science Blog

MAY 20, 2024

Enter AnalyticsCreator AnalyticsCreator, a powerful tool for data management, brings a new level of efficiency and reliability to the CI/CD process. It offers full BI-Stack Automation, from source to data warehouse through to frontend. It supports a holistic data model, allowing for rapid prototyping of various models.

Data Pipeline 262

Data Pipeline Data Warehouse Azure Data Lakes 262

IBM Data Science in Practice

JANUARY 13, 2025

By Santhosh Kumar Neerumalla , Niels Korschinsky & Christian Hoeboer Introduction This blogpost describes how to manage and orchestrate high volume Extract-Transform-Load (ETL) loads using a serverless process based on Code Engine. The source data is unstructured JSON, while the target is a structured, relational database.

ETL 100

ETL Data Pipeline Database Data Warehouse 100

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

ETL 126

ETL Hadoop Data Warehouse Data Pipeline 126

Analytics Vidhya

MAY 16, 2022

Introduction on ETL Tools The amount of data being used or stored in today’s world is extremely huge. Many companies, organizations, and industries store the data and use it as per the requirement. While handling this huge amount of data, one has to […].

ETL 143

ETL Data Science Analytics Analytics 143

Precisely

JANUARY 9, 2025

The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. In the beginning, there was a data warehouse The data warehouse (DW) was an approach to data architecture and structured data management that really hit its stride in the early 1990s.

Data Warehouse 119

Data Warehouse Hadoop Data Lakes Data Governance 119

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

Azure 283

Azure ETL Analytics Analytics 283

Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

Data Engineer 195

Data Engineer Data Engineering Data Engineering Data Engineering 195

Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. What is ETL? Let’s break down each step: 1.

ETL 105

ETL Data Quality Data Warehouse Analytics 105

Dataconomy

SEPTEMBER 4, 2023

The modern corporate world is more data-driven, and companies are always looking for new methods to make use of the vast data at their disposal. Cloud analytics is one example of a new technology that has changed the game. What is cloud analytics? How does cloud analytics work?

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Smart Data Collective

MAY 31, 2022

Data activation is a new and exciting way that businesses can think of their data. It’s more than just data that provides the information necessary to make wise, data-driven decisions. It’s more than just allowing access to data warehouses that were becoming dangerously close to data silos.

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ETL Data Silos Data Warehouse Big Data 142

Dataversity

DECEMBER 7, 2020

Data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations who seek to empower more and better data-driven decisions and actions throughout their enterprises. These groups want to expand their user base for data discovery, BI, and analytics so that their business […].

Data Warehouse 90

Data Warehouse Business Intelligence Business Intelligence Analytics 90

Data Science Dojo

SEPTEMBER 6, 2023

M aintaining the security and governance of data within a data warehouse is of utmost importance. Data Security: A Multi-layered Approach In data warehousing, data security is not a single barrier but a well-constructed series of layers, each contributing to protecting valuable information.

Data Warehouse 195

Data Warehouse Data Governance Data Quality ETL 195

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. In this article, we’ll focus on a data lake vs. data warehouse.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Women in Big Data

NOVEMBER 27, 2024

A data warehouse is a centralized repository designed to store and manage vast amounts of structured and semi-structured data from multiple sources, facilitating efficient reporting and analysis. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

Analytics Vidhya

FEBRUARY 27, 2023

Delta Lake allows businesses to access and break new data down in real time. Delta Lake is an open-source warehouse layer designed to run on top of data lakes analogous to […] The post A Comprehensive Guide on Delta Lake appeared first on Analytics Vidhya.

Data Lakes 315

Data Lakes Business Intelligence Business Intelligence Analytics 315

Pickl AI

OCTOBER 17, 2024

Summary: This article explores the significance of ETL Data in Data Management. It highlights key components of the ETL process, best practices for efficiency, and future trends like AI integration and real-time processing, ensuring organisations can leverage their data effectively for strategic decision-making.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

Data Science Blog

JULY 20, 2024

Es bietet vollständige Automatisierung des BI-Stacks und unterstützt ein breites Spektrum an Data Warehouses, analytischen Datenbanken und Frontends. Automatisierung: Erstellt SQL-Code, DACPAC-Dateien, SSIS-Pakete, Data Factory-ARM-Vorlagen und XMLA-Dateien. Data Lakes: Unterstützt MS Azure Blob Storage.

Azure 130

Azure SQL Power BI Data Lakes 130

Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. Introduction The ETL process is crucial in modern data management.

ETL 52

ETL Data Warehouse SQL Data Quality 52

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

phData

FEBRUARY 21, 2025

In this post, we will be particularly interested in the impact that cloud computing left on the modern data warehouse. We will explore the different options for data warehousing and how you can leverage this information to make the right decisions for your organization. Understanding the Basics What is a Data Warehouse?

Data Warehouse 52

Data Warehouse Cloud Data ETL Cloud Computing 52

The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

ETL 59

ETL Data Pipeline ML ML 59

Smart Data Collective

OCTOBER 17, 2022

Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination. Destination. Processing.

Data Pipeline 145

Data Pipeline Data Warehouse ETL Exploratory Data Analysis 145

Pickl AI

OCTOBER 15, 2024

Summary: Selecting the right ETL platform is vital for efficient data integration. Consider your business needs, compare features, and evaluate costs to enhance data accuracy and operational efficiency. Introduction In today’s data-driven world, businesses rely heavily on ETL platforms to streamline data integration processes.

ETL 52

ETL Azure AWS Data Governance 52

phData

MARCH 14, 2024

In the data analytics processes, choosing the right tools is crucial for ensuring efficiency and scalability. Two popular players in this area are Alteryx Designer and Matillion ETL , both offering strong solutions for handling data workflows with Snowflake Data Cloud integration.

ETL 52

ETL SQL Data Warehouse Data Pipeline 52

Dataversity

FEBRUARY 1, 2021

Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […]. The post Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 3 appeared first on DATAVERSITY.

Data Warehouse 59

Data Warehouse Business Intelligence Business Intelligence Data Profiling 59

Dataversity

JANUARY 11, 2021

Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their user base for […]. The post Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 2 appeared first on DATAVERSITY.

Data Warehouse 59

Data Warehouse Business Intelligence Business Intelligence Data Profiling 59

Flipboard

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data.

SQL 160

SQL AWS Data Lakes AI 160

ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

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Data Lakes Data Warehouse Database Big Data 98