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In this DataWorks Summit keynote session, CEO Praveen Kankariya explores the challenges in creating a unified view of enterprise data, an essential building block for information-driven decision making, and the advent of an AI-driven future.

A single version of truth continues to elude the enterprise due to multiple information sources functioning in silos. The confusion arising from hundreds of point solutions has added complexity that needs to be simplified.

Laying the foundation of your data-driven enterprise.

We enable you to focus on your business while we bring in the best available solutions and services to solve the data, AI and analytics puzzle.

Products


Automatic conversion of ETL and EDW to cloud

Out-of-the-box transformation accelerators for Teradata, Netezza, Ab Initio, Oracle and other legacy data warehouses

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Self-service data flow and analytics with StreamAnalytix

A visual IDE for data processing, and machine learning with Apache Spark

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Services


Cloud enablement and adoption

Leverage best practices and accelerators for cloud adoption, implementation and ROI

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Data Lake implementation and management for unified data access

Build a scalable, secure, fast and governed data lake with speed and agility

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Advanced analytics and AI

Build and deploy scalable data science and AI models across platforms leveraging multiple data sources

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We enabled

5x scope expansion and 10x cost reduction of insider threat detection

Fortune 200 bank


Used predictive analytics and machine learning

4x performance

10x faster app development

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Sunsetting Netezza using AWS and automated query translation to Apache Spark

Large mortgage company


Implemented a cloud-based big data warehouse

Benchmarked query performance

Saved time and cost over manual conversion

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Real-time customer 360 and context-driven applications for 5 million subscribers

Cable TV and telecom


Real-time call center monitoring

Real-time visualization

Context-driven applications across customer touch points

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50% faster conversion of Teradata ETL to Hadoop-based data lake architecture

Fortune 500 bank


Performed semantic validation

Converted 104 tables and 300 GB data

Performance tuning and cluster sizing

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