OQIMLI PROTSESSORLAR,REAL VAQT REJIMIDA MA'LUMOTLARNI QAYTA ISHLASH
Keywords:
Kalit so'zlar: oqimli protsessorlar,ma'lumotlarni real vaqt rejimida qayta ishlash,oqim (stream),oqim qayta ishlash dasturlari,oqim qayta ishlash dasturiy platformalari,apache kafka,apache flink, apache storm, spark streaming, reaktivlik, shkalalanish, kechikishlarni minimallashtirish, ma'lumotlar oqimi modellariAbstract
Ushbu maqola oqimli protsessorlar (stream processors) va ularning real vaqt rejimida ma'lumotlarni qayta ishlashdagi ahamiyatini tahlil qiladi. Oqimli protsessorlar yordamida ma'lumotlar oqimi doimiy ravishda kuzatiladi va tahlil qilinadi, bu esa katta hajmdagi ma'lumotlar bilan ishlashda katta afzalliklar beradi. Maqolada oqimli qayta ishlashning asosiy kontseptlari, afzalliklari va qo'llanilishi haqida batafsil ma'lumot beriladi. Shuningdek, texnik detallar, jumladan, ma'lumotlar oqimi modellari, qayta ishlash semantikalari va turg'unlik boshqaruvi kabi masalalar ham ko'rib chiqiladi. Oqimli protsessorlarning moliyaviy soha, sog'liqni saqlash, ijtimoiy tarmoqlar va telekom sohalaridagi qo'llanilishi haqida misollar keltirilgan. Ushbu maqola oqimli protsessorlar va ularning zamonaviy texnologiyalar bilan integratsiyasiga qiziquvchilar uchun mo'ljallangan.
References
Foydalanilgan Adabiyotlar:
Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: A Distributed Messaging System for Log Processing. Proceedings of the NetDB, Athens, Greece.
Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., & Tzoumas, K. (2015). Apache Flink: Stream and Batch Processing in a Single Engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering.
Toshniwal, A., Taneja, S., Shukla, A., Ramasamy, K., Rao, S., & Jha, G. (2014). Storm @ Twitter. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, New York, NY, USA.
Zaharia, M., Das, T., Li, H., Hunter, T., Shenker, S., & Stoica, I. (2013). Discretized Streams: Fault-Tolerant Streaming Computation at Scale. Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, New York, NY, USA.
Garg, R. (2020). Real-Time Analytics with Storm and Kafka: Real-Time Analytics on Streaming Data with Storm and Kafka. Packt Publishing Ltd.
Kleppmann, M. (2017). Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O'Reilly Media.
Munshi, A. (2018). Advanced Analytics with Spark: Patterns for Learning from Data at Scale. O'Reilly Media.
Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014). A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. Journal of Grid Computing.