Remove Automation Remove Data Ingestion Remove ETL
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What is Data Ingestion? Understanding the Basics

Pickl AI

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. This is where data ingestion comes in.

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ETL Process Explained: Essential Steps for Effective Data Management

Pickl AI

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
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Most Frequently Asked Azure Data Factory Interview Questions

Analytics Vidhya

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.

ETL 222
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A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

This involves a series of semi-automated or automated operations implemented through data engineering pipeline frameworks. Strengths: It offers parallel processing, flexibility, and built-in capabilities for various data tasks, including graph processing. Weaknesses: Steep learning curve, especially during initial setup.

ETL 130
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Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

AI News

Here are a few key reasons: The variety and volume of data will continue to grow, requiring the database to handle diverse data types—structured, unstructured, and semi-structured—at scale. Selecting a database that can manage such variety without complex ETL processes is important.

Big Data 237
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Introduction to Apache NiFi and Its Architecture

Pickl AI

Summary: Apache NiFi is a powerful open-source data ingestion platform design to automate data flow management between systems. Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation.

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Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

AI News

“If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui.