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Big Data Technologies

Analyze Data at Enterprise Scale

Organizations increasingly manage datasets too large, fast-moving, distributed, or diverse for conventional analytical approaches.

Explore the 5 resource centers:
  1. 1. Data Analytics Guides
  2. 2. BI & Visualization Tools
  3. 3. Data Science Insights
  4. 4. Big Data Technologies
  5. 5. Datasets & Resources
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Analyze Data at Enterprise Scale

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Analyze Data at Enterprise Scale

Organizations increasingly manage datasets too large, fast-moving, distributed, or diverse for conventional analytical approaches.

The IBACTP® Big Data Technologies Center introduces the infrastructure and architectural concepts used to process data at scale.

UNDERSTANDING BIG DATA

Big Data is commonly characterized by several dimensions.

Volume

Large quantities of data.

Velocity

Data generated and processed rapidly.

Variety

Structured, semi-structured, and unstructured information.

Veracity

Quality and reliability.

Value

The ability to convert data into meaningful outcomes.

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DATA WAREHOUSES

A data warehouse provides structured analytical data optimized for reporting and decision support.

Common characteristics include:

  • Curated datasets
  • Historical information
  • Business-oriented models
  • SQL analytics
  • Reporting
  • Governance
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DATA LAKES

Data lakes store large amounts of data in flexible forms.

They may support:

  • Raw data
  • Semi-structured data
  • Logs
  • Images
  • Documents
  • Machine-learning datasets
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LAKEHOUSE ARCHITECTURES

Lakehouse approaches attempt to combine the flexibility of data lakes with the management and analytical capabilities traditionally associated with warehouses.

APACHE SPARK

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Distributed Analytics at Scale

Apache Spark provides an open-source engine for large-scale data processing.

Its current documentation includes APIs and guidance for areas such as:

  • Spark SQL
  • DataFrames
  • Structured Streaming
  • Machine learning
  • Distributed processing

Spark can support workloads involving:

  • Batch analytics
  • Streaming
  • ETL
  • Machine learning
  • Large-scale transformations

EXPLORE APACHE SPARK

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DISTRIBUTED COMPUTING

Large analytical workloads can be divided across multiple computing resources.

Key concepts include:

  • Clusters
  • Nodes
  • Parallel processing
  • Partitioning
  • Distributed storage
  • Fault tolerance
  • Scalability
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DATA PIPELINES

A data pipeline moves information through stages such as:

  • SOURCE
  • INGESTION
  • VALIDATION
  • TRANSFORMATION
  • STORAGE
  • ANALYSIS
  • SERVING
  • MONITORING

ETL & ELT

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ETL

  • Extract
  • Transform
  • Load

Data is transformed before loading into the target analytical environment.

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ELT

  • Extract
  • Load
  • Transform

Raw information is loaded before transformation.

Cloud data platforms have increased the use of ELT architectures.

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STREAMING ANALYTICS

Some decisions require information to be processed continuously.

Applications include:

  • Fraud detection
  • IoT monitoring
  • Cybersecurity
  • Financial transactions
  • Manufacturing
  • Logistics
  • Website analytics
More in this section
CLOUD DATA PLATFORMS Read this

Modern analytical architectures increasingly use cloud services for:

  • Storage
  • Warehousing
  • Streaming
  • Processing
  • Machine learning
  • Governance
  • BI

Cloud adoption can improve scalability, but also creates considerations around:

  • Security
  • Privacy
  • Data residency
  • Cost
  • Vendor dependence
  • Governance
DATA ENGINEERING Read this

Data engineering provides the infrastructure that makes analytics possible.

Professionals may work with:

  • SQL
  • Python
  • APIs
  • Pipelines
  • Distributed computing
  • Warehouses
  • Lakes
  • Streaming systems
  • Orchestration
  • Data quality
  • Metadata
DATA GOVERNANCE Read this

Scaling analytics requires governance.

Organizations should define:

  • Data ownership
  • Data stewardship
  • Definitions
  • Quality standards
  • Classification
  • Access
  • Retention
  • Privacy
  • Security
  • Lineage

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