Data Modernization
Modernization of the data warehouse includes technical practices, platforms, and tool types, as well as data analysis and governance to support data-driven business goals.
Modernization encompasses more transformative changes to applications, infrastructure, data, and business processes to prepare for and take advantage of the latest technologies.
Data Challenges
Data is big and multi-format
Data is vast and diverse in format—huge volumes generated rapidly. Managing and analyzing varied data types require specialized tools and approaches.
Data requires more than SQL
Data work evolves—no single approach. ML, languages, real-time processing for innovation. Data engineers rise for urgent, diverse task connections.
Data should universal
Data spans all—employees, customers, partners, suppliers. A mission-critical, scalable, high-performance landscape beyond organizational bounds.
The Advantage of
Managing Your Data
Data Modernization Services
Big Data Migration
Data migration creates a competitive advantage by giving the ability to meet growing user demands for digital services and convenient interactions. Consolidate legacy systems to new applications dataset.
Data Warehouses
Achieve scalable computing and distributed storage capabilities with separated storage. To face growing data volumes improving the extensibility, stability, operability, performance, and resource utilization.
Data Analytics
Enable organizations to make more-informed business decisions. Analytics enables organizations to respond quickly to emerging market trends and gain a competitive edge over business rivals with actionable information
Business Intelligence
Combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make data-driven decisions. prioritize flexible analysis and speed to insight.
Data modernization is the process of updating and improving an organization’s data systems and infrastructure in order to make the data more accessible, accurate, and useful. This can involve a variety of activities, such as converting legacy data into modern formats, implementing new data management technologies, and improving data governance processes. The goal of data modernization is to enable organizations to better utilize their data for decision-making and to support new business initiatives.
Modern data systems and technologies make it easier for users to access the data they need, when they need it, from any device or location. This can help improve productivity and enable users to make better decisions based on the most up-to-date information. Data modernization can help improve the accuracy and completeness of an organization’s data, which is critical for making reliable business decisions. With modern data systems and technologies, Data can be more flexible and scalable than legacy systems, which can enable organizations to quickly respond to changing business needs and opportunities.
Platform for Data
Data Warehouse
BigQuery, Cloud BigTable, Cloud SQL, Cloud Firestore, Cloud Spanner
Data Processing
DataFlow, Data Fusion, Data Prep, DataProc, VertexAI
Prediction & Insight
GKE, Looker, Data Studio
Data Visualization
Looker Studio, Tableau,
Machine Learning
VertexAI
Pipeline Management
WorkFlows, Cloud Composer, Data Catalog, Cloud Run
Your Journey to Data-Modernization
Data Discovery
Focus on understanding the high-level data issues and goals, and brainstorm ideas about how to drive a better data architecture that satisfies business and application demands now and in the future.
Data Architecture
Design the overall architecture, including which clouds, technologies, and platforms to be used. Develop pipelines to ingest, transform and store data. implement the modern data architecture.
DataOps and Reporting
Define your data models, build dashboards with real-time refreshes and automate reporting. create predictable delivery and change management of data, data models and related artifacts.
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