Data-Centric Architecture: Reasons to Adopt
- Data as a Service (DaaS) Software Marketing & Analytics
Data-Centric Architecture: Reasons to Adopt
Data-centric architecture forms a key modernization strategy gaining immense traction today. By treating data as the epicenter of IT environments versus applications, it unlocks immense agility, insights, and cost efficiencies. This article outlines key business motivations driving adoption of the data-first paradigm powering digital leaders.
Automating Data Access and Insights
Data-centricity entails creating unified data fabrics where information gets stored centrally, then leveraged across the enterprise stack through open access. This automation of data availability, transforms analytics deriving actionable insights continuously.
Instead of lengthy pipelining, analysts directly query central repositories. And pretrained models trigger automated enrichments like segmentation, forecasting, and sentiment analysis on latest data.
Achieving Agility Across Initiatives
With data fabrics offering a single source of truth, new IT initiatives integrate easily regardless of systems, locations or formats. Teams combine data sets on-demand gaining flexibility.
New apps connect to existing data pools without dependencies. Microservices access common data grids as needed through standard APIs avoiding duplication. Agility at scale gets unlocked for versatile experimentation.
Optimizing TCO Through Consolidation
Transitioning siloed data and stacks into unified fabrics optimizes total cost of operations substantially. Duplicated analytic pipelines get retired through cross-functional data availability.
Storage, network and infrastructure costs lower running converged environments, while skills centralization raises ROI of expensive data science resources via cross-leveraging.
Top Data Fabric & Lakehouse Platforms
- AWS Lake Formation – Fully managed service simplifying data lake builds, data prep and access control.
- Databricks – Unified analytics platform to ingest, process, explore and analyze data on one platform.
- Snowflake – Scales storage and compute separately creating flexible data warehouse capacity.
- Dremio – Data lake engine helping analysts self-serve insights through SQL without copying data.
- Starburst – Accelerates analytics on data anywhere – cloud storage, warehouses, lakes and more.
Key Takeaway
Data-centric architecture centers environments around trustworthy distributed data networks versus applications. This elevates analytics automation, IT agility at scale and TCO efficiencies unlocking outsized business impact from data. The paradigm becomes imperative as data volumes and user needs balloon globally.
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Featured Data-Centric SaaS Solutions:
