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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Tarmoq:Programming, data science, ML - free courses by Big Data Specialist Hindiston31 693 Texnologiyalar & Aralashmalar9 381...

📈 Telegram kanali Data science/ML/AI analitikasi

Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 667 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 381-o'rinni va Hindiston mintaqasida 31 693-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 13 667 obunachiga ega bo‘ldi.

08 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 150 ga, so‘nggi 24 soatda esa 4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.97% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.27% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 1 089 marta ko‘riladi; birinchi sutkada odatda 310 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 09 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

13 667

Obunachilar

+424 soatlar

+437 kunlar

+15030 kunlar

1 089

Post ko'rishlar

~ 31024 soatlar

~ 45848 soatlar

7.97%

Muloqot nisbati

~ 1

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Adaptive Query Execution (AQE) in Apache Spark is a feature introduced to improve query performance dynamically at runtime, based on actual data statistics collected during execution. This makes Spark smarter and more efficient, especially when dealing with real-world messy data where planning ahead (at compile time) might be misleading. 🔍 Importance of AQE in Spark Runtime Optimization: AQE adapts the execution plan on the fly using real-time stats, fixing issues that static planning can't predict. Better Join Strategy: If Spark detects at runtime that one table is smaller than expected, it can switch to a broadcast join instead of a slower shuffle join. Improved Resource Usage: By optimizing stage sizes and join plans, AQE avoids unnecessary shuffling and memory usage, leading to faster execution and lower cost. 🪓 Handling Data Skew with AQE Data skew occurs when some partitions (e.g., specific keys) have much more data than others, slowing down those tasks. AQE handles this using: Skew Join Optimization: AQE detects skewed partitions and breaks them into smaller sub-partitions, allowing Spark to process them in parallel instead of waiting on one giant slow task. Automatic Repartitioning: It can dynamically adjust partition sizes for better load balancing, reducing the "straggler" effect from skew. 💡 Example: If a join key like customer_id = 12345 appears millions of times more than others, Spark can split just that key’s data into chunks, while keeping others untouched. This makes the whole join process more balanced and efficient. In summary, AQE improves performance, handles skew gracefully, and makes Spark queries more resilient and adaptive—especially useful in big, uneven datasets.

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