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Reference Architectures

  • Extended Relational
  • Non-Relational
  • Hybrid

Architectural Goals, Principles, and Considerations

  • Latency (near real time)
  • Reliability and fault tolerance
  • Availability
  • Scalability/Volume handling
  • Performance/speed
    • Goals and implementation - Oracle
      • Analyze and transform data in real-time
      • Optimize data structures for intended use
      • Use parallel processing
      • Increase hardware and memory
      • Database configuration and operations
      • Dedicate hardware sandboxes
      • Analyze data at rest, in-place
  • Throughput
  • Extensibility
  • Security
  • Cost/financial
  • Data quality
  • Skills availability
  • Backup and recovery
  • Locations and placement
  • Privacy and sensitive data
  • Disaster recovery
  • Schema on read vs schema on write
    • Bringing the analytical capabilities to the data, VS
    • Bringing the data to the analytical capabilities through staging, extracting, transforming and loading
  • Maturity Considerations - Oracle
    • Reference architecture
    • Development patterns
    • Operational processes
    • Governance structures and polices

Enterprise Big Data Architectural Components

  • Governance
    • Govern data quality
  • Operations, Infrastructure, and DevOps
  • Monitoring
  • Security and privacy
    • Authentication
    • Authorization
    • Accounting
    • Data protection
    • Compliance
  • Data Aquisition, Ingestion, and Integration
    • Messaging and message queues
    • ETL/ELT
    • Change data capture
    • FTP
    • API/ODBC
    • Replication
    • Bulk movement
    • Virtualization
    • Analytics types and options on ingestion - Oracle
      • Sensor-based real-time events
      • Near real-time transaction events
      • Real-time analytics
      • Near real time analytics
      • No immediate analytics
  • Data Processing
    • Batch and stream processing/computing (velocity)
      • Massive scaling and processing of multiple concurrent input streams
    • Parallel computing platform
      • Clusters or grids
      • Massively parallel processing (MPP)
      • High performance computing (HPC)
    • Options - Oracle
      • Leave it at the point of capture
      • Add minor transformations
      • ETL data to analytical platform
      • Export data to desktops
    • Fast data - Oracle
      • Streams
      • Events
      • Actions
  • Data Access
    • Querying
    • Real-time analytics
    • BI analytics
    • MapReduce analytics
  • Data Modeling and Structure
    • Star schema
    • Snowflake schema
  • Data Analysis, data mining, discovery, simulation, and optimization
    • Advanced analytics and modeling
    • Text and natural language analytics
    • Video and voice analytics
    • Geospatial analytics
    • Data visualization
    • Data mining
    • Where to do analysis - Oracle
      • At ingest – real time evaluation
      • In a raw data reservoir
      • In a discovery lab
      • In a data warehouse/mart
      • In BI reporting tools
      • In the public cloud
      • On premises
    • Data sets
    • Data science
    • Data discovery
    • In-place analytics
    • Faceted analytics
    • SQL analytics
  • Data Storage and Management
    • Data lake
    • Data warehouse (volume), aka enterprise information store
      • Centralized, integrated data store
      • Powers BI analytics, reporting, and drives actionable insights
      • Responsible for integrating data
      • Structured, prepared, and stored data optimized for
        • Analytical applications and decision support
        • Querying and reporting
        • Data mining
      • In-database analytics
      • Operational analytics
      • MPP engine
      • 'Deep analytical appliance' - IBM
    • Operational data store (ODS)
    • Database Systems and DBMS
      • Relational (RDBMS)
      • NoSQL
        • Real-time analytics and insights
      • NewSQL
      • Hybrid
    • Data marts
      • Data warehouse extracted data subsets oriented to specific business lines, departments or analytical applications
      • Can be a 'live' data mart
    • File systems (Non-distributed)
    • Distributed file systems (e.g., HDFS) and Hadoop (volume and variety)
      • Real-time and MapReduce analytics and insights
      • Deep analysis of petabytes of structured and unstructured data
    • In-memory
    • Data factory
    • Data Reservoir
    • Dedicated and ad-hoc
      • Discovery labs
      • Sandboxes
  • Data lifecycle management
    • Rule-based Data and Policy Tracking
    • Data compression
    • Data archiving
  • Deployment Choice
    • On-premise, aka traditional IT
    • In-cloud
      • Public cloud
      • Private cloud
    • Appliance
    • Managed services
  • Presentation, Analytics, and Applications (visibility)
    • Browser/web
    • Mobile
    • Desktop
    • Dashboards
    • Reports
    • Notifications and messaging
    • Scorecards
    • Charts and graphics
    • Visualization and discovery
    • Search
    • Alerting
    • EPM and BI applications
    • Recommendations

Enterprise Big Data Components

Big Data Processing Key Functional Capabilities - IBM

  • Data ingestion
    • Optimize the process of loading data in the data store to support time-sensitive analytic goals.
  • Search and survey
    • Secure federated navigation and discovery across all enterprise content.
  • Data transformation
    • Convert data values from source system and format to destination system and format.
  • Analytics
    • Discover and communicate meaningful patterns in data.
  • Actionable decisions
    • Make repeatable, real-time decisions about organizational policies and business rules.
  • Discover and explore
    • Discover, navigate, and visualize vast amounts of structured and unstructured information across many enterprise systems and data repositories.
  • Reporting, dashboards, and visualizations
    • Provide reports, analysis, dashboards, and scorecards to help support the way that people think and work.
  • Provisioning
    • Deploy and orchestrate on-premises and off-premises components of a big data ecosystem.
  • Monitoring and service management
    • Conduct end-to-end monitoring of services in the data center and the underlying infrastructure.
  • Security and trust
    • Detect, prevent, and otherwise address system breaches in the big data ecosystem.
  • Collaborate and share

Big data and analytics architecture on cloud - IBM

  • Analytics-as-a-service
    • Consumes both data at rest and in motion
    • Applies analytical algorithms
    • Provides
      • Dashboards
      • Reports
      • Visualizations
      • Insights
      • Predictive modeling
    • Abstracts away all complexity of data collection, storage, and cleansing
  • Data-as-a-service
    • Data-at-rest-service
    • Data-in-motion-service
  • NoSQL tools (Hive, Pig, BigSQL, ...)
  • EMR clusters (Hadoop, Cassandra, MongoDB, ...) and Traditional DW
  • Big data file system (HDFS, CFS, GPFS, S3, ...)
  • Infrastructure & Appliances (Baremetal or IaaS) and object storage

The Oracle Enterprise Architecture Development Process (OADP)

  • Designed to be a flexible and a “just-in-time” architecture development approach
  • Key Steps
    • Establish Business Context and Scope
    • Establish an Architecture Vision
    • Assess the Current State
    • Establish Future State and Economic Model
    • Develop a Strategic Roadmap
    • Establish Governance over the Architecture

Data Storage Functions

  • Staging
    • Temporary storage
    • Used for cleaning, integration and transformation routines
  • Data management
    • Long-time managed storage
    • Clean and integrated data
  • Sandboxing
    • Temporary data stores
    • Used by people, groups, and departments
    • Experimentation with data, processing, and analysis techniques
  • Application optimized storage
    • Example usage = data mart
  • Archive and raw data archive
    • Raw, processed, and transformed data

The 7 V's of Big Data

  • Volume - Scale of data
  • Variety - Different forms of data
  • Velocity - Analysis of streaming data
  • Veracity - Overall quality and correctness of the data
    • Garbage in, garbage out
    • Assess the truthfulness and accuracy of the data as well as identify missing or incomplete information
  • Visibility/Visualization
  • Value
  • Variability

Data types and sources

  • Structured
    • Transactions
    • Master and reference
  • Unstructured
    • Text
    • Image
    • Video
    • Audio
    • Social
  • Semi-structured
    • Machine generated
  • Data storage (databases)
  • Sensors
  • Events
  • Parquet
  • RFID tags
  • Instore WiFi logs
  • Machine Logs
    • Application
    • Events
    • Server
    • CDRs
    • Clickstream
  • Text, including documents, emails, scanned documents, records, ...
  • Social networks
  • Public web
  • Geo-location/geospatial
  • Feeds
  • Machine generated
  • Clickstream
  • Software
  • Media
    • Images
    • Video
    • Audio
  • Business applications
    • OLTP - Online transaction processing
    • ERP - Enterprise resource planning
    • CRM - Customer relationship management
    • SCM - Supply chain management
    • HR
    • Product/Project management
  • Online chat
  • Merchant listings
  • DMP - Data management platform (advertising/marketing)
  • CDR - Call detail records
  • Surveys, questionnaires, binary questions, and sentiment
  • Billing data
  • Product catalog
  • Network data
  • Subscriber data
  • Staffing
  • Inventory
  • POS and transactional
  • eCommerce transactions
  • Biometrics
  • Mobile devices
  • Weather data
  • Traffic pattern data
  • Mobile devices
  • Surveillance

Big Data Architecture Patterns

  • Polyglot
  • Lambda
  • Kappa
  • IOT-A
    • Message Queue/Stream Processing (MQ/SP) block
      • Buffer data
        • Processing speed
        • Throughput handling of downstream components
        • Micro-batching can increase ingestion rate into downstream components
      • Process and filter data
        • Cleaning and removal
        • Stream processing
          • Continuous queries
          • Aggregates
          • Counts
          • Real-time machine learning/AI
      • Output
        • Real-time
        • Ingest data into downstream blocks (DB and/or DFS)
      • Example technologies
    • Database (DB) block
      • Provides granular, structured, low-latency access to the data
      • Typically NoSQL
        • MongoDB
        • Cassandra
        • HBase
      • Output
        • Interactive ad-hoc querying
          • Data store API (e.g., HBase, MongoDB, ...)
          • Standard SQL interface
      • Example technologies
    • Distributed File System (DFS) block
      • Batch jobs over entire dataset
        • Aggregations
        • Reporting
        • Integration across data sources
          • E.g., with unstructured data
      • Long term storage (archiving)
      • Example technologies

IoT Solution Components

  • Connected devices
  • Support for a variety of workload types
  • Data management
  • Data modeling and schemas
    • Device metadata
  • Data streams
    • Composed of data records flowing through the system
  • Processing and output
    • Native and processed raw data support
      • Input data typically time-series
    • Real-time stream
    • Interactive querying
    • Output generated in batches
    • Data transformations
    • Aggregations and computation
    • Data integration and enrichment
  • Data movement and storage
    • One or more data stores
  • Leverage big data approach
    • Scale out techniques and storage on commodity hardware
      • Historical data/references (volume)
    • Schema-on-read (e.g., data lake)
    • Community defined interfaces
    • Many different data formats and non-relational sensor data (variety)
    • High rate data generation and handling via data streams in IoT context (velocity)
  • Analytics
  • APIs/SDKs
  • Applications and presentation

Big Data and IoT Tech Stacks

  • SACK
    • Spark - Digest
    • Akka - Ingest
    • Cassandra
    • Kafka
  • SMACK
    • Spark
    • Mesos
    • Akka
    • Cassandra
    • Kafka

Hadoop Benefits

  • Built on the shared nothing principle
    • Each node is independent and self-sufficient
  • Ability to store any and all data types relatively cheap
  • Ability to process any and all data quickly and relatively cheap
  • Vast community, ecosystem, and pluggable architecture
  • Scalable, flexible, computational model

Data Processing and access methods/patterns

  • Batch
    • Process batches of data on regular time intervals, e.g., hourly, daily, overnight, etc.
    • Aka, MapReduce on Hadoop
  • Real-time
    • Monitor and react in real time
    • Key-value data stores, such as NoSQL, allow for high performance, index-based retrieval - Oracle
    • Real-time MapReduce and processing (e.g., Spark)
  • Streaming
    • Stream ingestion
    • Near Real-Time (NRT) Event Processing with External Context
    • NRT Event Partitioned Processing
    • Complex Topology for Aggregations or ML
  • Interactive/ad-hoc querying
    • Data analysts reviewing data
  • Online
  • Search
  • In-memory

Offline vs Online Learning

Coming soon...

General References

Big Data Best Practices - Oracle

  • Align Big Data with Specific Business Goals
  • Ease Skills Shortage with Standards and Governance
  • Optimize Knowledge Transfer with a Center of Excellence
  • Top Payoff is Aligning Unstructured with Structured Data
  • Plan Your Discovery Lab for Performance
  • Align with the Cloud Operating Model

Architecture Principles - Oracle

  • Accommodate All Forms of Data
  • Consistent Information and Object Model
  • Integrated Analysis
  • Insight to Action

IBM Data Governance Council Maturity Model

  • Organizational Structures & Awareness
  • Stewardship
  • Policy
  • Value Creation
  • Data Risk Management & Compliance
  • Information Security & Privacy
  • Data Architecture
  • Data Quality Management
  • Classification & Metadata
  • Information Lifecycle Management
  • Audit Information, Logging & Reporting

Diagrams

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Courtesty of DataZoomers

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Courtesty of Guru99