Cloud Scale Analytics

Cloud-scale analytics (CSA) is an on-cloud unified scalable, repeatable framework for building modern data platforms with data governance, business intelligence, advanced analytics, machine learning, and data visualization. It is designed to scale to handle any volume, variety, and velocity of data.

Cloud scale analytics can help deploy data landing zones, data lake houses, data mesh, or data fabric, depending on customer needs and preferences.

Organizations are harnessing the massive volumes of data generated by databases, IoT devices, social media, and other data sources to gain actionable insights into their areas of interest: demand patterns, churn predictions, process optimization, customer behaviour, and preferences.

Cloud-scale analytics aims to empower organizations by:

  • Serving data to their employees and customers as a product
  • Enforcing data governance and security to ensure compliance with regulations and standards.
  • Empowering teams to focus on innovations and business outcomes.

Reference Architecture

Figure 1 Cloud Scale Analytics Reference Architecture

Figure 2 Data Landing Zone Reference Architecture

Translab CSA Services

Translab’s cloud-scale analytics experts can help you and your organization at every step of your journey

  1. Developing the right strategy for implementing CSA within an organization and helping your organization become data-driven.
  2. Building the data platform incorporating key design considerations like IAM, policies, and DR.
  3. Bringing data governance and security to your analytics through data catalog, master data management, data quality, metadata, data access management, data privacy, etc.
  4. Managing, optimizing, and innovating your CSA to provide more bang for buck

Translab Customer Success Stories

• A global bank that migrated its legacy data warehouse to a cloud-scale analytics platform, reducing its operational costs by 50%, improving its query performance by 10x, and enabling real-time analytics for fraud detection and risk management.

• A regional insurance company that implemented a data lake house architecture, integrating its structured and unstructured data sources in a single platform. This enabled the company to perform advanced analytics on customer behavior, claims processing, and product development, resulting in increased customer satisfaction and retention.

• A fintech startup that adopted a data mesh approach, decentralizing its data ownership and governance across different domains. This enabled the startup to accelerate its innovation and agility, launching new products and features faster than its competitors.