PHPMem v2.0.1

Version
1.6.45
Uptime
17 days 2 hours 24 minutes 39 seconds

Memory

Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB

Keys

Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0

Connections

Current
14 / 1 024 max
Total
194 954
Rejected
0
llm:852ad9e1da684f4a13c91406ad308403e54c2c57ceef5fe28def6ecb82646e14
TTL 6 days 41 minutes 20 seconds Size 2,21KB Export
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### 1.1 Business Data Landscape This dataset captures Indian startup funding and investor activity for January through May 2022. It is organized as five monthly tables, one per month, from "Indian Startups _ Funding Investors Data January 2022" through "Indian Startups _ Funding Investors Data May 2022". Together they hold 446 rows. Each table is classified as a dimension, which suggests each is a descriptive record of funded companies and investors rather than a transactional log. The structure is a consistent monthly series, so the core entities are startups, investors, and funding events observed over time. ### 1.2 Key Data Assets | Table | Role | Rows | % of Total | Size | |-------|------|-----:|----------:|-----:| | Indian Startups _ Funding Investors Data January 2022 | dimension | 115 | 25.8% | 0.1 MB | | Indian Startups _ Funding Investors Data March 2022 | dimension | 98 | 22.0% | 0.1 MB | | Indian Startups _ Funding Investors Data February 2022 | dimension | 96 | 21.5% | 0.1 MB | | Indian Startups _ Funding Investors Data April 2022 | dimension | 95 | 21.3% | 0.1 MB | | Indian Startups _ Funding Investors Data May 2022 | dimension | 42 | 9.4% | 0.0 MB | Each table is a monthly snapshot of funding activity, so no single table dominates. The first four months each hold 21% to 26% of rows, and January is the largest at 25.8%. May is the smallest at 9.4% (42 rows), which may reflect a partial month of coverage, though the profiling evidence cannot confirm that. ### 1.3 Data Integration Posture No relationships were detected among the five tables, and no validated joins exist. This is expected for a monthly partitioned series: the tables share a common purpose but are not linked by keys. The headline scores (overall 100%, completeness 100%, referential integrity 100%) show the data is clean as delivered. For cross-month analysis such as trend comparisons, the practical next step is to stack the monthly tables into one unified view, not to build joins. That would let leaders compare January through May activity directly, and later months could be added as new tables or appended rows.