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Organize: Data Organization

Organize: Data Organization

Overview of the Organize phase in the CORE framework

Organize: Data Organization

Section titled “Organize: Data Organization”

Organizing data effectively is crucial for scalable analytics. This phase covers both batch processing with data warehouses and real-time processing with stream layers.

Overview

Section titled “Overview”

The Organize phase transforms raw collected data into structured, accessible formats. It encompasses both historical data storage (warehouses) and real-time data processing (streams) to support different analytical needs.

Key Concepts

Section titled “Key Concepts”

Analytics Warehouse / Data Lake

Section titled “Analytics Warehouse / Data Lake”

Centralized storage for historical data:

  • Batch processing: Process large volumes of data efficiently
  • Schema flexibility: Support for structured and unstructured data
  • Cost optimization: Use appropriate storage tiers based on access patterns

Real-Time Stream Layer

Section titled “Real-Time Stream Layer”

Process data as it arrives:

  • Low latency: Sub-second processing for time-sensitive use cases
  • Event streaming: Handle high-volume event streams
  • Real-time analytics: Power dashboards and alerts

Outcomes

Section titled “Outcomes”
  • Data warehouse or data lake configured
  • Data pipeline architecture designed
  • Real-time stream processing set up (if needed)
  • Data transformation and ETL processes implemented
  • Data quality monitoring in place
  • Access controls and security configured

Artifacts

Section titled “Artifacts”
  • Data Warehouse Schema: Structure for storing historical data
  • ETL Pipelines: Processes for extracting, transforming, and loading data
  • Stream Processing Setup: Real-time data processing infrastructure
  • Data Catalog: Documentation of available datasets and schemas
  • Data Quality Reports: Monitoring and validation dashboards

Pitfalls

Section titled “Pitfalls”
  • Premature optimization: Over-engineering data structures before understanding usage patterns
  • Schema rigidity: Creating schemas that are too rigid to accommodate future needs
  • Ignoring real-time needs: Only focusing on batch processing when real-time is required
  • Poor data quality: Not implementing validation leads to downstream issues
  • Cost overruns: Not monitoring storage and compute costs can lead to unexpected expenses
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