Data Scraping Encyclopedia>Data Scraping Noun> Modern Data Stack (MDS)

Modern Data Stack (MDS) | Web Scraping Tool | ScrapeStorm

2025-12-30 17:10:28

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Abstract：Modern Data Stack (MDS) refers to a collective term for modern data analysis and utilization platforms built on cloud-native technologies. ScrapeStormFree Download

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Introduction

Modern Data Stack (MDS) refers to a collective term for modern data analysis and utilization platforms built on cloud-native technologies. At its core is a cloud data warehouse, characterized by a loosely coupled suite of SaaS tools covering data collection (ELT), transformation, analysis, visualization, and utilization. Unlike traditional on-premises data warehouses (DWH) or ETL-centric architectures, MDS prioritizes scalability, flexibility, and rapid deployment, aiming to extend data-driven decision-making across the entire organization.

Applicable Scene

MDS is applicable to enterprises seeking to quickly establish cloud-based data analysis platforms, as well as startups to large-scale organizations experiencing rapid growth in data volume and analytical needs alongside business expansion. Its use cases include user behavior analysis in SaaS and web services, marketing effectiveness measurement, KPI analysis for product improvement, and operational visualization through BI dashboards. It enables not only data engineers but also analysts and business units to directly handle data.

Pros: MDS ensures high scalability and flexibility by combining specialized tools such as Snowflake, BigQuery (cloud data warehouses), Fivetran, dbt, Looker, and Tableau around a cloud DWH core. The ELT approach allows rapid accumulation of raw data, strong adaptability to schema changes and new data sources, and minimal infrastructure management overhead. Additionally, the loose coupling between tools facilitates easy replacement in response to technological evolution, accelerating development speed and promoting democratization of analysis.

Cons: On the other hand, as MDS is based on combining multiple SaaS tools, poor overall design may lead to tool proliferation and cost increases. Responsibility boundaries between vendors can be unclear, making fault isolation challenging. Furthermore, without organizational design for data governance, security, and permission management, excessive analytical freedom may result in weak control, necessitating a certain level of data architecture design capability.