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Built using C language, PostgreSQL is the most popular choice of database from small web apps to enterprise systems. It runs as a multi-process system and follows ACID principles.
1 - PostgreSQL supports concurrent client connections independently. Each client connection to PostgreSQL creates a dedicated server process.
2 - The Postmaster Process is the main supervisor that manages all other PostgreSQL processes. It controls the entire database instance.
3 - Background workers run parallel processes when needed to handle specialized tasks.
4 - PostgreSQL shared memory is a central memory area containing multiple buffers such as Shared, WAL, Clog, and Temporary buffers. All components communicate through this shared memory.
5 - PostgreSQL also has several auxiliary processes such as:
- BG Writer: Manages background writing
- WAL Writer: Handles write-ahead logging
- Auto Vacuum: Maintains database cleanliness
- Checkpointer: Ensures data consistency
- Stats Collector: Gathers statistics
- System Logger: Manages Logging
- Archiver: Handles archiving
- Replication launcher: Manages replication
6 - PostgreSQL has different types of physical files for varied needs such as:
- Data Files: Stores actual database data
- WAL Files: Write-ahead log storage
- Archive Files: Backup and recovery data
- Log Files: System and error logs
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Here's your ultimate Excel Cheat Sheet tailored for Data Analysts. Save it, share it, and boost your productivity!
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Here’s a practical, code-first playbook for exploring numerical data 👇
How we approach EDA (with Python code + outputs):
- Basics: shape, dtypes, and missing values.
- Denoscriptives: mean/median, variance, and percentiles for quick sanity checks.
- Distributions: histograms, boxplots, density to spot skew and spread.
- Relationships: scatter plots and a correlation heatmap to find signals.
- Outliers: z-scores/IQR to flag anomalies worth investigating.
- Scaling: MinMax vs Z-score depending on the model and metric.
- Segments: groupby comparisons to surface patterns you miss in globals.
- Decisions: tie insights back to the question and next steps.
What this really means is: you get a repeatable workflow that turns raw numbers into clear hypotheses fast.
How we approach EDA (with Python code + outputs):
- Basics: shape, dtypes, and missing values.
- Denoscriptives: mean/median, variance, and percentiles for quick sanity checks.
- Distributions: histograms, boxplots, density to spot skew and spread.
- Relationships: scatter plots and a correlation heatmap to find signals.
- Outliers: z-scores/IQR to flag anomalies worth investigating.
- Scaling: MinMax vs Z-score depending on the model and metric.
- Segments: groupby comparisons to surface patterns you miss in globals.
- Decisions: tie insights back to the question and next steps.
What this really means is: you get a repeatable workflow that turns raw numbers into clear hypotheses fast.
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Ex_Files_Learning_Apache_Airflow.zip
88 KB
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