Manveer is co-founder of Zenith, where he helps technical companies like ClickHouse win in AI search and SEO. He was previously Director of Engineering at Confluent and led Growth Engineering at Dropbox.
Manveer Chawla
Co-founder @ Zenith AI Labs
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Resources by Manveer Chawla
Best real-time analytics database for star schema and fast joins (2026 guide)
Manveer Chawla • Last updated: Apr 8, 2026
Compare real-time analytics databases for star schema and fast cross-table joins. Learn what to test in 2026 and choose the right engine for your workload.
Managed Postgres for AI and Real-Time Apps
Manveer Chawla • Last updated: Apr 7, 2026
Learn how managed Postgres on NVMe with native ClickHouse CDC and pg_clickhouse eliminates the ETL tax and scales from OLTP to real-time analytics and AI apps
How to choose a database for real-time analytics in 2026
Manveer Chawla • Last updated: Mar 25, 2026
In 2026, the gap between "Data Warehouse" and "Real-Time Serving" has become the primary bottleneck for data teams. For the last decade, we've operated on a split model: transactional databases (OLTP) handled application state, and data warehouses (DWH) handled nightly reporting.
How to engineer cost-efficient open source observability with ClickHouse (ClickStack) - 2026 technical playbook
Manveer Chawla • Last updated: Jan 7, 2026
A technical guide for engineers to build a cost-efficient observability stack. Learn how to use ZSTD codecs, Materialized Views, and Tiered Storage with ClickHouse to reduce observability data footprint by 10x.
A practical guide to observability TCO and cost reduction
Manveer Chawla • Last updated: Nov 26, 2025
Tired of exploding observability bills? Learn how to calculate your true TCO and see how to cut architecture costs by 70-90% vs. ingest-based pricing.
The ultimate guide to Open Source Observability in 2026: From silos to stacks
Manveer Chawla • Last updated: Nov 10, 2025
Explore the top open source observability stacks for 2025. Compare ELK, LGTM, and unified observability solutions like ClickStack for cost, scale, and high-cardinality data.