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Uploaded byYogi Devendra Vyavahare
Introduction to Real-time data processing
The document discusses the principles of real-time data processing and contrasts it with batch processing, emphasizing the characteristics, use cases, and appropriate scenarios for each method. It highlights the significance of big data, defined as high-volume, high-velocity, and high-variety information that requires innovative processing techniques for enhanced decision-making. Current trends indicate a growing demand for real-time data insights, with a notable shift toward processing data in motion.
Introduction to Real-time data processing
- 1. Introduction to Real-time dataprocessing Yogi Devendra (yogidevendra@apache.org)
- 2. Agenda ● What isbig data? ● 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
- 3.
- 4. Definition : bigdata Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. [1] 4
- 5. Exploding sizes ofdatasets 5 ● 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
- 6.
- 7. 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 7
- 8. 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 8
- 9. Examples ● Data atrest ○ Finding stats 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 9
- 10.
- 11. Batch processing ● Problemstatement : ○ Process this entire data ○ give answer for X at the end. 11
- 12. Batch processing :Use-cases 12 ● Sales summary for the previous month[5] ● Model training for Spam emails
- 13. Batch processing :Characteristics 13 ● 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]
- 14.
- 15. Real time dataprocessing ● a.k.a. Stream processing ● Problem statement : ○ Process incoming stream of data ○ to give answer for X at this moment. 15
- 16. Stream processing :Use-cases ● e-commerce order processing ● Credit card fraud detection ● Label given email as : spam vs non- spam 16
- 17.
- 18. 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) 18
- 19. 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 19
- 20.
- 21.
- 22. Batch Vs Streaming panipuri ⇒ Streaming image ref [9] wada ⇒ batch image ref [8] 22
- 23.
- 24. Micro-batch ● Create batchof small size ● Process each micro-batch separately ● Example frameworks: Spark streaming pani puri ⇒ micro-batch image ref [10] 24
- 25. ● 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. 25 When to use : Batch Vs Streaming?
- 26. 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 26
- 27. 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. 27
- 28.
- 29. 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). 29
- 30.
- 31. Quiz : isthis Batch or Real-time? ● Queue for roller coaster ride image ref [11] ● Queue at the petrol pump image ref [12] 31
- 32. 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] 32
- 33.
- 34. Current trends ● Difficultyin splitting 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. 34
- 35.
- 36.
- 37. 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 37