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Introduction to Real-Time Data Processing
The document discusses the differences between big data processing methods: batch processing and real-time data processing (streaming). It outlines use cases for each method, highlighting when to utilize batch processing for historical data analysis and real-time processing for immediate data analysis. Current trends indicate a growing demand for real-time insights, emphasizing the importance of stream processing in various applications.
Introduction to Real-Time Data Processing
- 1. Introduction to Real-time dataprocessing Yogi Devendra (yogidevendra@apache.org)
- 2. Agenda ●What is bigdata? ●Data at rest Vs Data in motion ●Batch processing Vs Real - time data processing (streaming) ●Examples ●When to use: Batch? Real-time? ●Current trends 2
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- 4. Exploding sizes ofdatasets 4 ●Google ○>100PB data everyday [3] ●Large Hydron collidor : ○150M sensors x 40M sample per sec x 600 M collisions per sec ○>500 exabytes per day [2] ○0.0001% of data is actually analysed
- 5. Data at restVs Data in motion ● At rest : ○ Dataset is fixed ○ a.k.a bounded [15] ● In motion : ○ continuously incoming data ○ a.k.a unbounded 5
- 6. Data at restVs Data in motion (continued) ●Generally Big data has velocity ○continuous data ●Difference lies in when are you analyzing your data? [5] ○after the event occurs ⇒ at rest ○as the event occurs ⇒ in motion 6
- 7. Examples ●Data at rest ○Findingstats about group in a closed room ○Analyzing sales data for last month to make strategic decisions ●Data in motion ○Finding stats about group in a marathon ○e-commerce order processing 7
- 8. Batch processing ●Problem statement: ○Process this entire data ○give answer for X at the end. 8
- 9. Batch processing :Use-cases 9 ● Sales summary for the previous month[5] ● Model training for Spam emails
- 10. Batch processing :Characteristics 10 ●Access to entire data ●Split decided at the launch time. ●Capable of doing complex analysis (e.g. Model training) [6] ●Optimize for Throughput (data processed per sec) ●Example frameworks : Map Reduce, Apache Spark [6]
- 11. Real time dataprocessing ● a.k.a. Stream processing ● Problem statement : ○ Process incoming stream of data ○ to give answer for X at this moment. 11
- 12. Stream processing :Use-cases ● e-commerce order processing ● Credit card fraud detection ● Label given email as : spam vs non- spam 12
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- 14. Stream processing :Characteristics ● Results for X are based on the current data ● Computes function on one record or smaller window. [6] ● Optimizations for latency (avg. time taken for a record) 14
- 15. Stream processing :Characteristics ●Need to complete computes in near real- time ●Computes something relatively simple e.g. Using pre-defined model to label a record. ●Example frameworks: Apache Apex, Apache storm 15
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- 17. Batch Vs Streaming panipuri ⇒ Streaming image ref [9] wada ⇒ batch image ref [8] 17
- 18. Micro-batch ●Create batch of smallsize ●Process each micro-batch separately ●Example frameworks: Spark streaming pani puri ⇒ micro-batch image ref [10] 18
- 19. ● Depends onuse-case ○Some are suitable for batch ○Some are suitable for streaming ○Some can be solved by any one ○Some might need combination of two. 19 When to use : Batch Vs Streaming?
- 20. When to use: Batch Vs Real time?(continued) ●Answers for current snapshot ⇒ Real-time ○Answers at the end ⇒ Open ●Complex calculations, multiple iterations over entire data ⇒ Batch ○Simple computations ⇒ Open ●Low latency requirements (< 1s) ⇒ Real- time 20
- 21. When to use: Batch Vs Real time?(continued) ●Each record can be processed independently ⇒ Open ○Independent processing not possible ⇒ Batch ● Depends on use-case ○Some use-cases can be solved by any one ○Some other might need combination of two. 21
- 22. Can one replacethe other? ●Batch processing is designed for ‘data at rest’. ‘data in motion’ becomes stale; if processed in batch mode. ●Real-time processing is designed for ‘data in motion’. But, can be used for ‘data at rest’ as well (in many cases). 22
- 23. Quiz : isthis Batch or Real-time? ●Queue for roller coaster ride image ref [11] ●Queue at the petrol pump image ref [12] 23
- 24. Quiz : isthis Batch or Real-time? ●Selecting relevant ad to show for requested page ●Courier dispatch from city A to B image ref [13] image ref [14] 24
- 25. Current trends ●Difficulty insplitting problems as Map Reduce : Alternative paradigms for expressing user intent . ●More and more use-cases demanding faster insight to data (near real-time) ●‘Data in motion’ is common. ●‘Real-time data processing’ getting traction. 25
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- 28. References 1. Big Data| Gartner IT Glossary http://www.gartner.com/it-glossary/big-data/ 2. Big Data | Wikipedia https://en.wikipedia.org/wiki/Big_data 3. Data size estimates | Follow the data https://followthedata.wordpress.com/2014/06/24/data-size-estimates/ 4. Data Never Sleeps 2.0 | Domo https://www.domo.com/blog/2014/04/data-never-sleeps-2-0/ 5. Data in motion vs. data at rest | Internap http://www.internap.com/2013/06/20/data-in-motion-vs-data-at-rest/ 6. Difference between batch processing and stream processing | Quora https://www.quora.com/What-are-the-differences-between-batch- processing-and-stream-processing-systems/answer/Sean-Owen?srid=O9ht 7. How FAST is Credit Card Fraud Detection | FICO http://www.fico.com/en/latest-thinking/infographic/how-fast-is-credit-card-fraud- detection 8. CULINARY TERMS | panjakhada http://panjakhada.com/the-basics/ 9. Crispy Chaat ... | grabhouse http://grabhouse.com/urbancocktail/11-crispy-chaat-joints-food-lovers-hyderabad/ 10. Paani puri stall | citiyshor http://www.cityshor.com/pune/food/street-food/camp/murali-paani-puri-stall/ 11. Great Inventions: The Roller Coaster | findingdulcinea http://www.findingdulcinea.com/features/science/innovations/great-inventions/the- roller-coaster.html 12. RIL petrol pump network | economictimes http://articles.economictimes.indiatimes.com/2015-05-24/news/62583419_1_petrol-and-diesel- fuel-retailing-ril 13. Publishers | Propellerads https://propellerads.com/publishers/ 14. Michael Bishop Couriers | Google plus https://plus.google.com/110684176517668223067 15. The world beyond batch: Streaming 101 http://radar.oreilly.com/2015/08/the-world-beyond-batch-streaming-101.html 16. How to Answer the Question http://www.clipartpanda.com/clipart_images/how-to-answer-the-question-46954146 17. Thank You http://www.planwallpaper.com/thank-you 28
Editor's Notes
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