Streaming Data Platform for AI Business

The Streaming Data Platform is a cloud-native solution that enables real-time data analytics and serves as a foundational service for AI. This well-architected platform accelerates time-to-market and mitigates technology risks.

Challenges & Trends

Organizations using legacy data infrastructure find it challenging to capture and analyze data at scale in real-time amidst massive growth. They are looking to migrate their outdated data solutions to cloud-native data platforms to enable real-time data analytics.

Organizations seeking to leverage AI lack the essential infrastructure and data platform. They are looking for a real-time data analytics solution that provides immediate value and serves as a platform for innovation. There is a need for a readily available solution with high customization capabilities.

 

High Customization vs. Ready to Go? Get Both

80%

  • Certified by cloud providers - AWS, Azure, GCE or Kubernetes (on-prem)
  • Data Security foundation (Role-based authentication, data encryption, data anonymization, PII Data removal)
  • Data quality, timeliness and completeness monitoring infrastructure
  • Data Governance infrastructure
  • CI/CD for data pipelines
  • Cloud agnostic distribution on Apache Kafka or cloud-native version on Amazon Kinesis and Azure Analytics
20%

customizable for a
competitive edge

  • Data Pipelines architecture, implementation and optimization
  • Migration of legacy data pipelines to the cloud
  • Integration with various data sources and consumers
  • “How-to” training for engineers and managers

Implementation Journey

What You Pursue Our Solution What You Get
Hadoop Migration & Re-architecture Streaming Data Platform DWH experience:

  • Ad-hoc analytics API
  • Reporting API
ESB (Tibco, Informatica) migration Baked into your organization in 5-6 months to drive your business performance Streaming experience:

  • Change Data Capture
  • Consistent processing and enrichment
DWH & ETL migration to DWH with enough capacity Cheap metadata-rich Data Lake
Data Lake Foundation Foundation for Enterprise ML/AI
ML/AI Initiatives

Use Cases

Streaming Data Platform is designed to be implemented for the following initiatives:

  • Plug-n-play solution for processing and storing data streams in AWS
  • Migration of Hadoop based on-prem platform to AWS native streaming services
  • Migration of legacy Enterprise Service Bus or Data Integration architectures to AWS native platform
  • Rearchitecture of Data Warehouse workloads to handle growing data volume, velocity as well as to provide capabilities for real-time analytics
  • Rearchitecture of slow, inconsistent and always-out-of-date data marts in existing Data Lake or Data Warehouse
  • Rearchitecture of duplicated and disjointed realtime and batch pipelines

Note: The platform’s architecture is standardized to support any business use case. For the demo purposes, a canonical Adtech use case is implemented. The code is available on GitHub.

Benefits

Cloud-Native Solution

Converts expensive, outdated infrastructure into an efficient cloud platform

Streaming-First Architecture

Powers and provides governance for a data lake ecosystem

Unified Architecture

Reference architecture for data ingestion, processing, machine learning and business applications

Foundation for AI Solutions

Makes data available for training and serving machine learning models in realtime and always consistent data streams

Read Case Study

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