Scalable Cloud-Native Data Lake for Enterprise-Wide Analytics

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Scalable Cloud-Native Data Lake for Enterprise-Wide Analytics

SiriusOne built a cloud-native data lake for a multinational enterprise, enabling real-time analytics, automated data ingestion, and AI-driven insights. The solution improved data management, cut costs, and empowered decision-makers with actionable intelligence.
Tech Stack: AWS (S3, Glue, Athena, Redshift, Kinesis), Python, Apache Spark
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Client & Project Overview:

A global enterprise struggled with data silos and inefficient analytics due to its reliance on traditional data warehouses. Their fragmented data infrastructure hindered cross-functional collaboration, and their teams lacked real-time insights for strategic decision-making. They required a scalable, cost-effective, and AI-driven cloud-native data lake that could:

  • Unify data across multiple business units to create a single source of truth.
  • Process structured and unstructured data seamlessly, enabling advanced analytics.
  • Reduce storage and processing costs while scaling dynamically with business needs.
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Business Challenge:

  • Data silos across departments - Inconsistent storage prevented collaboration and cross-functional insights.
  • Slow and expensive analytics - Traditional warehouses led to high query execution times and cost inefficiencies.
  • Limited real-time insights - Delays in data ingestion hindered decision-making in critical operations.
  • Scalability issues - Their existing data infrastructure couldn’t adapt to growing business demands.

Solution:

To address these challenges, SiriusOne designed and deployed a high-performance cloud-native data lake that centralized data storage, automated ingestion pipelines, and provided real-time analytics capabilities. The solution was built using AWS S3, Glue, Athena, Redshift, and Kinesis, ensuring seamless integration, cost optimization, and scalability.

Step 1: Unified Data Storage & Intelligent Data Ingestion

  • AWS S3 as the Data Lake Foundation – Implemented a multi-tiered storage architecture, allowing data to be stored in raw, processed, and curated formats.
  • Automated ETL Pipelines with AWS Glue – Designed serverless data pipelines that cleaned, transformed, and cataloged data for quick retrieval and querying.
  • Hybrid Data Ingestion – Enabled real-time streaming via AWS Kinesis and batch processing with AWS Glue, ensuring instant and historical data availability.

Step 2: Real-Time Analytics & AI-Driven Insights

  • Redshift Spectrum for Large-Scale Analytics – Deployed Amazon Redshift for high-speed analytical processing, enabling petabyte-scale querying.
  • Athena for Ad-Hoc Queries – Integrated Amazon Athena for on-demand, serverless queries, eliminating the need for provisioned infrastructure.
  • AI-Driven Decision Support – Leveraged AWS SageMaker to analyze historical trends, predict business outcomes, and provide intelligent recommendations.

Step 3: Cost Optimization & Scalable Architecture

  • Tiered Storage Optimization – Implemented S3 Intelligent-Tiering, reducing storage costs by up to 40% through automated data lifecycle management.
  • Partitioning & Compression – Used Parquet & ORC file formats, improving query speed and reducing storage footprint.
  • Serverless Data Access & Dashboards – Designed API-based data access layers using AWS Lambda, enabling self-service analytics for business users.

Results:

  • 50% faster query execution, enabling real-time business intelligence.
  • 40% lower storage and compute costs, leveraging automated cloud optimizations.
  • Unified enterprise-wide data lake, breaking down silos and enabling cross-functional analytics.
  • AI-powered decision-making, providing predictive insights for strategic growth.

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