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![Big Data Architectures
Pros and Cons
• Pros
– Incorporate low value and social data in analysis
– Increase analysis reach to non-structured data
– Correlate across data sources on the same platform
– Very strong in their sweet spots.
– Efficiency in terms of
• data movement volume,
• scale
• fault tolerance and
• responsiveness.
• Cons
– Not relational. Gives up on some of the relational advantages.
• Joins
• Aggregations etc.
– Little standards – Non portable solutions
– Less support with end-user tools and applications [ though growing ]
– Not a replacement to DW but just an extension to it.
– Incompatible with different classes of use-cases. Have sweet spots.
– Heterogeneous setup in Development and Operations.](https://image.slidesharecdn.com/bigdata-130315182240-phpapp01/75/Bigdata-8-2048.jpg)
More Related Content
Bigdata
- 1. Big Data in 10 What’s real and what’s fluff Abhishek Pamecha Mar-2013
- 2. What is BigData • It is all about data – But not about “how much” – But about correlations and increased reach
- 3. BigData Architecture It influencesor changes your • Data source choices • Data storing choices • Data analyzing/mining approaches It helps • Address highly focused use cases • Correlate more data sources • address scale and fault tolerance issues
- 4. Caution! BigData is nota “substitute” for existing warehousing practices. It complements existing practices.
- 5. Architectures – Datasources • Traditional DW • BigData adds – Production DB – Log files – Dictionaries – Social graphs – ETL/ELT pipelines – Streaming data – External Data marts
- 6. Architectures – DataStorage • Traditional DW • BigData adds – Production DB – Distributed file storage • Flatten hierarchies • Resolved references – Distributed hash maps – Columnar representations – ROLAP or MOLAP databases • Star schema – Graph data bases • Materialized views • Virtual data marts – Document collections • Partitioned tables – Still relational – Other NoSQL variants
- 7. Architectures – Analyticapproaches • Traditional DW • BigData adds – Production DB – Distributed file storage • Flatten hierarchies • Map reduce frameworks and chaining • Resolved references – Pre-generate results – Distributed hash maps • Single key predominant – ROLAP databases • Star schema – Multidimensional queries – Columnar representations • Materialized views • Extracts select columns per row – adhoc explorations on subsets • Still relational – Graph data bases • Virtual data marts • Navigate links – adhoc explorations on subsets • Partitioned tables – Document collections • Simplified schemas – Other NoSQL approaches • Stream pattern matching and pipelining
- 8. Big Data Architectures Pros and Cons • Pros – Incorporate low value and social data in analysis – Increase analysis reach to non-structured data – Correlate across data sources on the same platform – Very strong in their sweet spots. – Efficiency in terms of • data movement volume, • scale • fault tolerance and • responsiveness. • Cons – Not relational. Gives up on some of the relational advantages. • Joins • Aggregations etc. – Little standards – Non portable solutions – Less support with end-user tools and applications [ though growing ] – Not a replacement to DW but just an extension to it. – Incompatible with different classes of use-cases. Have sweet spots. – Heterogeneous setup in Development and Operations.
- 9. Challenges • Architectural – “Big” data management – Data consistency – Read heavy or write heavy – Scaling – Distributed deployment • Functional – data quality – Problem set choice • Organizational – Data backed decisions – Going overboard – SLAs and operations management – Data Privacy
- 10.