Amazon Data / Trends
Large-scale Amazon scraping and trend analysis
Backend architecture & data pipelines
A growing dataset changes how a system should read, write and process information. I work on Python backends and data pipelines, from relational models to collection workers and search indexes. My Amazon Data and Liqsale work covers acquisition, normalization and bulk processing; the case pages explain the historical scope and implementation.
How I approach the work
Start with the workload
Identify the expensive path and inspect query plans, access patterns and batch sizes. Model relationships and transaction boundaries in PostgreSQL before deciding that another datastore is needed.
Give each data layer a clear purpose
Use Redis for suitable temporary or cached state with explicit TTL and invalidation rules. Use Elasticsearch when search and analytical access justify a separate index. Keep the source of truth and the cost of stale reads clear.
Process in bounded batches
Collection and enrichment workers need concurrency limits, retry handling and bulk storage operations. Separate HTTP, domain rules, persistence and provider adapters so a pipeline can evolve without coupling every layer.
A decision to make early
An extra cache or index improves some workloads and adds invalidation, synchronization and operational costs. I would choose it from measured bottlenecks and consistency requirements, rather than treating a larger stack as an improvement by itself.
Relevant work
Liqsale / Daily Snipes
From supplier spreadsheets to a marketplace
Onlihub
Technical ownership of a commerce platform
Tell me about your task
Describe the slow or unreliable operation, the current stack and approximate data volume. Existing measurements help; if there are none, identifying what to measure is the first useful step.
Discuss the task