Introduction
A client organisation is initiating the development of an advanced data storage system utilizing Amazon Simple Storage Service (Amazon S3) to create a comprehensive data lake. This system will serve as the backbone for integrating various data sources and enabling sophisticated data analysis capabilities.
High Level Data Lake Storage System Requirements
Data Ingestion Requirements
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Real-Time IoT Data Collection:
- Develop a real-time data ingestion mechanism for IoT sensor data.
- Ensure the ingestion process supports high-velocity and high-volume data streams.
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Historical Data Integration:
- Implement a batch ingestion process for importing historical data from existing databases.
- Design the system to maintain data integrity and optimize for large-scale data transfer.
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Third-Party Data Enrichment:
- Establish a protocol for ingesting supplemental data from third-party sources.
- Ensure compatibility and seamless integration with internal data structures.
Data Processing and Transformation Requirements
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Data Cleaning and Transformation:
- Design a data transformation pipeline that cleanses, normalizes, and enriches the raw data.
- Utilize technologies that are compatible with Apache Hadoop ecosystems to align with current team expertise.
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Scalable Data Processing Solutions:
- Leverage cloud-based data processing services that can scale with the growth of data volume.
- Prioritize services that offer interoperability with Hadoop-based tools and minimize the need for additional training.
Data Analysis and Visualization Requirements
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Analytical Dashboards:
- Develop interactive dashboards that provide visual representations of data insights.
- Ensure dashboards are user-friendly and can be customized to highlight key performance indicators.
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Compatibility with Analytical Tools:
- Ensure the data lake is compatible with common analytical and business intelligence tools.
- Provide support for both batch and real-time analytics.
General Requirements
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Cost-Effectiveness:
- Implement a solution that provides optimal cost-to-performance ratio.
- Monitor and optimize resource usage to manage operational costs.
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Security and Compliance:
- Adhere to industry-standard security practices to protect data at rest and in transit.
- Ensure the system complies with relevant data protection regulations.
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System Scalability and Reliability:
- Design the architecture to support scaling up or down based on demand.
- Ensure high availability and fault tolerance of the data ingestion and processing services.
Sample Solution - AWS Based Data Processing Architecture (Draft)
Download Draw.io Source File
AWS Services Utilized
- API Gateway -> Lambda
- AWS Data Pipeline -> Amazon S3
- AWS AppFlow
- AWS Glue
- Amazon EMR
- Amazon QuickSight
Data Processing Stages
- Ingesting Raw Data
- Store Raw Data
- Transform and Refine Data
- Big Data Analysis
- Visualizations For Insights