Bantuan Proyek Big Data dan Optimalisasi Performa

@kamran2012

This looks like a great fit, Saya akan membantu desain arsitektur lakehouse, optimasi format kolumnar (Parquet/ORC), serta benchmarking performa — mencakup partitioning, clustering, compression, dan query performance tuning menggunakan Spark dan Trino. Satu hal yang akan saya prioritaskan: membandingkan Delta Lake vs Apache Iceberg secara langsung pada dataset Anda — mengukur latency, throughput, bytes scanned, dan storage footprint di setiap format. Hasil ini akan memberikan dasar eksperimental yang kuat untuk dokumentasi riset Anda, bukan sekadar rekomendasi teoretis. Questions: 1) Apakah data sudah tersedia di object storage (S3/GCS), atau perlu pipeline ingestion terlebih dahulu? 2) Apakah target output berupa laporan teknis internal atau publikasi ilmiah dengan metodologi formal? Ready to start whenever you are. Kamran

@mohjib092

Saya memiliki pengalaman dalam Data Engineering, Big Data Processing, dan optimasi performa menggunakan Apache Spark, Delta Lake, Iceberg, serta engine query seperti Trino dan Spark SQL. Saya dapat membantu mulai dari desain arsitektur lakehouse, optimasi storage dan query, benchmarking, analisis metrik performa, hingga dokumentasi teknis yang terstruktur untuk kebutuhan implementasi maupun penelitian. Regards, Muhammad Jibran Ahmed

@techsty2014

Hi there, Kami akan membangun arsitektur lakehouse, mengoptimasi format kolumnar (Parquet/ORC), dan menjalankan benchmarking performa secara menyeluruh untuk proyek penelitian Anda. Pendekatan kami: partitioning dan clustering dirancang berdasarkan pola query utama, bukan sekadar volume data. Ini memangkas bytes scanned secara signifikan dan mempercepat latency. Kami juga akan menyusun dokumentasi teknis lengkap beserta analisis hasil pengujian. A couple of quick things to confirm: 1) Apakah infrastruktur sudah tersedia (cloud atau on-premise), atau perlu kami rekomendasikan? 2) Apakah output akhir ditujukan untuk publikasi ilmiah, sehingga perlu mengikuti format metodologi tertentu? The number quoted here is a starting estimate. The exact cost and timeline will be confirmed after we go through the full scope together. Looking forward to talking through the details. Faizan

@vishal1145

Your lakehouse architecture will fail at scale if you don't implement proper Z-ordering and file compaction strategies. I've seen Delta Lake deployments where query times degraded from 2 seconds to 45 seconds after 6 months because of small file proliferation. Before designing the optimization strategy, I need clarity on two things: What's your current data volume and daily ingestion rate (TB/day)? Are you running on Databricks, EMR, or self-managed Spark clusters? Here's the architectural approach: - APACHE SPARK + DELTA LAKE: Implement adaptive query execution with dynamic partition pruning and optimize shuffle operations to reduce stage execution time by 60-70%. - PARQUET OPTIMIZATION: Design column pruning strategies and implement predicate pushdown with bloom filters to minimize bytes scanned from 500GB to under 50GB per query. - LAKEHOUSE ARCHITECTURE: Build a medallion architecture (bronze/silver/gold layers) with automated compaction jobs and vacuum operations to maintain optimal file sizes between 128MB-1GB. - PERFORMANCE BENCHMARKING: Set up TPC-DS or custom benchmark suites measuring query latency, CPU utilization, and I/O throughput across different compression codecs (Snappy vs ZSTD). - TRINO/PRESTO TUNING: Configure memory allocation, join distribution strategies, and cost-based optimizer settings to handle complex analytical queries on petabyte-scale datasets. I've architected similar lakehouse systems for 4 research projects that processed 50TB+ daily with sub-3-second query response times. I'll also deliver comprehensive technical documentation in both English and Bahasa Indonesia covering methodology, experimental results, and optimization recommendations suitable for academic publication. Let's schedule a technical discussion to review your current data pipeline bottlenecks before finalizing the implementation roadmap.

@InoovativeThink

Hi, I read through your project for Bantuan Proyek Big Data dan Optimalisasi Performa. You need help with bantuan proyek big data dan optimalisasi performa. I can build and deliver this efficiently based on the job requirements. This fits well with my experience in Excel. I can take what you outlined and turn it into a clean final delivery, with attention to Excel and the details in your brief. I will keep the implementation clean, practical, and aligned with your requested scope. Send me the details and I can start immediately.

@alluankit01

Halo, saya bisa membantu proyek Big Data dan optimalisasi performa Anda, khususnya pada desain lakehouse, optimasi storage, query tuning, benchmarking, dan dokumentasi teknis untuk kebutuhan riset maupun implementasi sistem analitik skala besar. Solusi terbaik adalah memulai dari review arsitektur dan workload saat ini, lalu menyusun eksperimen terukur untuk membandingkan format penyimpanan seperti Parquet/ORC, strategi compression, partitioning, clustering, data layout, serta performa query menggunakan Spark SQL, Trino/Presto, Delta Lake atau Apache Iceberg. Saya terbiasa dengan Apache Spark, data engineering, data lakehouse architecture, pipeline data, performance tuning, benchmarking, analisis latency, throughput, storage footprint, bytes scanned, CPU/memory usage, serta penyusunan laporan teknis yang rapi dan mudah dipahami. Deliverables dapat mencakup: * Review dan rekomendasi arsitektur data * Optimasi format dan layout penyimpanan * Query performance tuning * Benchmarking dan hasil eksperimen * Analisis metrik performa * Dokumentasi teknis dan ringkasan temuan * Rekomendasi untuk implementasi lanjutan Saya dapat bekerja secara terstruktur, komunikatif, dan mendukung kebutuhan riset/eksperimen dengan pendekatan yang reproducible. Best regards Ankit

@robertb297

Hi, I read your project description regarding the research and implementation of a large-scale data analytics system. With 8+ years of experience in Data Engineering and Big Data optimization, I’m confident I can support both the research and practical implementation aspects of your project. For the past ~8 years I have been working with one of the largest telecommunications companies in the Netherlands, where I designed and managed large-scale data platforms. In one of the key projects, I implemented and optimized a Hadoop-based cluster with around 140 physical nodes, managing and processing approximately 3 TB of data with highly optimized storage and query performance. My experience closely matches the areas you mentioned: • Data Lakehouse architecture using technologies such as Apache Spark, Delta Lake, and Apache Iceberg • Optimization of columnar storage formats like Parquet and ORC (compression strategies, partitioning, clustering, and data layout optimization) • Query performance tuning using Spark SQL and Trino/Presto • Large-scale data pipeline design and performance optimization • Benchmarking and performance analysis using metrics such as latency, throughput, bytes scanned, CPU utilization, and memory usage I also have strong experience in analyzing system behavior through benchmarking experiments and translating those results into clear technical documentation and recommendations. For collaboration, I usually structure work in phases: Architecture review and system design Benchmarking experiments (storage formats, partitioning strategies, compression) Performance analysis and optimization Documentation of results and recommendations I am available for medium‑term collaboration and would be happy to discuss your research objectives and the scale of the data platform you are planning. Best regards Robert B

@toqeer74

The complexity of optimizing large-scale data storage and analytics demands a robust architecture and advanced methodologies. Implementing a data lakehouse model can drastically improve both storage efficiency and query performance. Leveraging tools like Apache Spark and Delta Lake will facilitate effective data manipulation and architecture design. With extensive experience in performance benchmarking, I can deliver thorough metrics analyses covering latency, throughput, and resource utilization. The expected timeframe for the initial deliverable is 30 days. Do you have a specific timeline for the first milestone?

@jabulanix

We've just completed a similar project with outstanding results in Big Data Performance Optimization. We can seamlessly assist in achieving your goal through a professional process, leveraging our expertise in Apache Spark, Delta Lake, Trino, and more. I am currently offering discounted pricing on Freelancer.com to build my profile. Remember, if the final work doesn't meet the agreed scope, payment is not required. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, Jabu.