how to speed up analytics queries
AI Search Visibility Analysis
Analyze how brands appear across multiple AI search platforms for a specific prompt
Total Mentions
Total number of times a brand appears
across all AI platforms for this prompt
Platform Presence
Number of AI platforms where the brand
was mentioned for this prompt
Linkbacks
Number of times brand website was
linked in AI responses
Sentiment
Overall emotional tone when brand is
mentioned (Positive/Neutral/Negative)
Brand Performance Across AI Platforms
| BRAND | TOTAL MENTIONS | PLATFORM PRESENCE | LINKBACKS | SENTIMENT | SCORE |
|---|---|---|---|---|---|
1ClickHouse | 0 | 0 | 95 | ||
2Amazon Redshift | 0 | 0 | 95 | ||
3Toucan | 0 | 1 | 95 | ||
4Datadog | 0 | 0 | 66 | ||
5Redis | 0 | 0 | 66 | ||
6New Relic | 0 | 0 | 66 | ||
7Apache Pinot | 0 | 0 | 66 | ||
8BigQuery | 0 | 0 | 66 | ||
9Memcached | 0 | 0 | 66 | ||
10Snowflake | 0 | 0 | 55 |
Strategic Insights & Recommendations
Dominant Brand
Amazon Redshift and BigQuery are consistently mentioned as leading columnar storage solutions for analytics optimization.
Platform Gap
ChatGPT provides more detailed technical implementation guidance while Perplexity offers a more structured comparison table format.
Link Opportunity
Both platforms reference specialized analytics databases and monitoring tools that could benefit from direct integration guides.
Key Takeaways for This Prompt
Columnar databases like ClickHouse, Redshift, and BigQuery significantly outperform traditional databases for analytics workloads.
Query optimization through proper indexing, avoiding SELECT *, and using materialized views can dramatically improve performance.
Caching strategies with Redis or Memcached reduce database load and improve response times for frequent queries.
Partitioning large tables and using EXPLAIN plans are essential for identifying and resolving query bottlenecks.
AI Search Engine Responses
Compare how different AI search engines respond to this query
ChatGPT
BRAND (8)
SUMMARY
ChatGPT provides comprehensive strategies for optimizing analytics queries including SQL optimization with EXPLAIN plans, strategic indexing, sargable queries, materialized views, caching layers like Redis, table partitioning, star schema design, columnar storage engines like Amazon Redshift and BigQuery, and continuous monitoring with tools like pgBadger and Datadog.
Perplexity
BRAND (4)
SUMMARY
Perplexity offers practical techniques for speeding up analytics queries including optimizing query writing by avoiding SELECT *, proper indexing and data partitioning, reducing data volume through filtering, using materialized views and caching, workload management, choosing columnar databases like ClickHouse, and analyzing query execution plans with EXPLAIN commands.
REFERENCES (7)
Google AIO
SUMMARY
No summary available.
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