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JavaScript Congress • Nov 24, 2021

Making Data Warehouses fast. A developer's story.

Adnan Rahic

Standard caching won't fix unpredictable data warehouse response lags. Learn how one developer used Cube's semantic API to unlock 200-millisecond queries without complex SQL tuning.

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#1 about 4 min

Navigating data warehouse performance for application developers

Grasping the challenges of massive analytical datasets is the first step to building responsive data applications.

#2 about 3 min

Understanding the fundamental differences between analytical and transactional processing

Defining data warehouses reveals why low-volume complex queries require entirely different architectures than traditional transactional databases.

#3 about 2 min

Utilizing materialized views and multi-dimensional cubes for scale

Bridging the gap between scanning vast amounts of data and handling higher query volumes requires pre-computed analytical results.

#4 about 4 min

Evaluating physical constraints and network limits on query latency

Achieving instantaneous application response times under network constraints requires understanding how data volume shapes subsecond query execution.

#5 about 4 min

Explaining query execution overhead and caching limitations in BigQuery

Serverless data warehouses struggle with subsecond latency due to query planning overhead and strict exact-match caching rules.

#6 about 3 min

Managing query concurrency and quota limits in cloud datasets

Hitting high parallel execution reveals project-level query caps that throttle active application requests and queue operations.

#7 about 4 min

Benchmarking cloud data warehouses under concurrent artificial user load

Uncached execution testing reveals hard concurrency ceilings and unchanged percentile latency distributions in serverless data structures.

#8 about 4 min

Implementing an analytics API layer with open source tools

Using a semantic layer to condense source data drastically reduces execution processing and retains high application concurrency.

#9 about 4 min

Configuring local development environments for automated schema generation

Connecting an open-source semantic layer to a cloud warehouse automates data schema generation via zero-code interfaces.

#10 about 5 min

Deploying pre-aggregations and materialized views in production systems

Scaling analytics requires dedicated infrastructure to pre-compute and store aggregated tables separately from raw database storage.

#11 about 4 min

Comparing benchmark outcomes between direct queries and semantic layers

Bypassing raw data scans with pre-aggregated structures yields massive throughput improvements and subsecond application responses.

#12 about 8 min

Integrating analytics APIs across small applications and mid-sized databases

Overcoming initial data scaling challenges requires timing the introduction of semantic cache layers regardless of underlying database size.

Matching moments

1:21 min

Summary of decoupling analytical compute and storage

Matthias Niehoff Matthias Niehoff · WWC Europe 2026

5:00 min

Q&A on analytical databases and market convergence

Andrey Abramov Andrey Abramov · WWC Europe 2026

7:52 min

Audience questions on database performance, deployments, and data migrations

Gregor Bauer Gregor Bauer · LIVE

2:36 min

Modern application stacks and real-time data requirements

Tim Faulkes · LIVE

3:58 min

Struggling with ungoverned data lakes and massive storage costs

Kateřina Ščavnická Kateřina Ščavnická · WWC 2025

8:01 min

Solving architectural challenges during rapid public cloud migrations

Liam Hurrel +1 · WWC 2021

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