Converged data architectures: data warehouse or data lake?
Posted: Tue Jan 21, 2025 6:14 am
What is the best strategy when opting for converged data architectures ? The answer is: data integration .
Integration of convergent data architectures
The crisis triggered by COVID-19 acted as a stimulus for digital transformation. In a very short time, advances were developed at an unimaginable speed: telemedicine, remote banking, online shopping and virtual learning made companies evolve years in just a couple of months during the pandemic.
Today we know that data is the path to profitability , and as leaders and technology solutions update and modernize , so do legacy, disconnected enterprise data architectures.
The goal is to move toward a unified analytics environment, recognizing that there can no longer be limitations in current data architectures and going one step beyond the choice between a data warehouse and a data lake.
In this scenario, the cloud plays a leading role, responsible for austria phone number lead having facilitated a new transition towards the integration of convergent data architectures .
Enterprises don’t have to make a decision between a data warehouse or a data lake , or even set up separate but equal entities in the cloud, as storage in this space includes all types of structured, unstructured, and semi-structured data, and provides the scalability needed to manage large volumes of data.
As the worlds of data warehouse and data lake collide no matter what field they are in, one thing is clear: digital transformation is only possible with a modern, automated approach to data integration .
Traditional ways of manually setting up and managing data pipelines and ETL scripts have become outdated, slow and error-prone. Today, modern businesses need to integrate their data volumes, no matter where they come from.
What processes and actions drive the unification of converged data architectures?
Data warehouse and data lake have converged to form a new, richer data architecture , according to the report “ Building the Unified Data Warehouse and Data Lake ” issued by TDWI in 2021.
The research, which included 220 data analytics professionals, shows that data warehouses and data lakes are coming together to drive new practices based on valuable data, based on an evolved and expanding architecture.
The advantages and processes driven by the integration of converged data architectures are the following:
Data silos are consolidated, providing a better foundation for analyzing new and traditional data.
By integrating technologies, companies can implement tools (data pipelines, data catalogs, business glossaries, etc.) and data areas (data governance, master data management, metadata management, etc.).
AI powers data integration, fostering automation and improved governance.
Modern software tools support practices and help extend data governance capabilities across the enterprise.
Greater business value is obtained from data.
Data analysis is expanding into more advanced forms, such as machine learning and AI.
Qlik Data Integration to unify converged data architectures
Qlik Data Integration is Qlik’s leading platform that unifies converged data architectures by automating data warehouses and data lakes, from real-time data ingestion to creating, cataloging and publishing data sets ready for analysis.
In a context where enterprise data architectures are in a state of flux, Qlik Data Integration has key functionalities to adapt to the demands of today's companies . This allows the planning and execution of any project with ease.
Integration of convergent data architectures
The crisis triggered by COVID-19 acted as a stimulus for digital transformation. In a very short time, advances were developed at an unimaginable speed: telemedicine, remote banking, online shopping and virtual learning made companies evolve years in just a couple of months during the pandemic.
Today we know that data is the path to profitability , and as leaders and technology solutions update and modernize , so do legacy, disconnected enterprise data architectures.
The goal is to move toward a unified analytics environment, recognizing that there can no longer be limitations in current data architectures and going one step beyond the choice between a data warehouse and a data lake.
In this scenario, the cloud plays a leading role, responsible for austria phone number lead having facilitated a new transition towards the integration of convergent data architectures .
Enterprises don’t have to make a decision between a data warehouse or a data lake , or even set up separate but equal entities in the cloud, as storage in this space includes all types of structured, unstructured, and semi-structured data, and provides the scalability needed to manage large volumes of data.
As the worlds of data warehouse and data lake collide no matter what field they are in, one thing is clear: digital transformation is only possible with a modern, automated approach to data integration .
Traditional ways of manually setting up and managing data pipelines and ETL scripts have become outdated, slow and error-prone. Today, modern businesses need to integrate their data volumes, no matter where they come from.
What processes and actions drive the unification of converged data architectures?
Data warehouse and data lake have converged to form a new, richer data architecture , according to the report “ Building the Unified Data Warehouse and Data Lake ” issued by TDWI in 2021.
The research, which included 220 data analytics professionals, shows that data warehouses and data lakes are coming together to drive new practices based on valuable data, based on an evolved and expanding architecture.
The advantages and processes driven by the integration of converged data architectures are the following:
Data silos are consolidated, providing a better foundation for analyzing new and traditional data.
By integrating technologies, companies can implement tools (data pipelines, data catalogs, business glossaries, etc.) and data areas (data governance, master data management, metadata management, etc.).
AI powers data integration, fostering automation and improved governance.
Modern software tools support practices and help extend data governance capabilities across the enterprise.
Greater business value is obtained from data.
Data analysis is expanding into more advanced forms, such as machine learning and AI.
Qlik Data Integration to unify converged data architectures
Qlik Data Integration is Qlik’s leading platform that unifies converged data architectures by automating data warehouses and data lakes, from real-time data ingestion to creating, cataloging and publishing data sets ready for analysis.
In a context where enterprise data architectures are in a state of flux, Qlik Data Integration has key functionalities to adapt to the demands of today's companies . This allows the planning and execution of any project with ease.