PostgreSQL for business intelligence and mass data analysis

Level:

Beginner

Duration:

3 days

Dates & Duration

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If there is no date and time available for a certain training you are interested in, please contact us.

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This training can also be held at your location or as online training, specifically tailored for your company. In this case, the course agenda can also be adapted to best suit your needs.

Learn more about our customized training >>

 

This course takes 3 days – 8 hours per day (on site) or 5 hours per day (online).
The only difference is that there will be less practical exercises in an online course.  However, we can share them with you so you can do them on your own. You can ask our consultants for feedback or help, if needed.

Summary

During this course you will learn how to use PostgreSQL for business intelligence related tasks. In this workshop you will learn to import, process and effectively analyze large amounts of data. You will learn the basic SQL techniques and how to efficiently model mass data. Additionally, you will learn how to integrate external business logic directly into PostgreSQL.

Audience

This course is aimed at data analysts and developers who already know the basics of SQL and who face the challenge of having to deal with complex problems or large amounts of data efficiently.

Available Languages

This course can be held in English and German.

COURSE CONTENT

DAY 1

Database Design

  • Preparing data structures for analysis
  • Data structures for data warehousing
  • Normalization vs. denormalization
  • Handling faulty data

Importing data

  • Bulk loading
  • LOGGED vs. UNLOGGED tables
  • Tuning imports

Aggregating data

  • GROUP BY and HAVING
  • Optimizing aggregations
  • Ordered sets and hypothetical aggregates
  • Optimizing the order of aggregation
  • GROUP SETS (ROLLUP, CUBE, etc.)

DAY 2

Analytics and windowing functions

  • Simple windows (rank, dense_rank, etc.)
  • Frame clauses and sliding windows
  • Performance tuning

Timeseries analysis

  • Storing timeseries data efficiently
  • Frame clauses and sliding windows
  • Anomaly detection
  • Partitioned time series
  • Timeseries prediction and forecasting
  • Optional: Diving into MobilityDB 
    • Handling and analyzing GPS tracks
    • Storing tracking data in PostgreSQL

DAY 3

Serverside business logic

  • Including server side modules
  • Data mining and external modules
  • Writing your own aggregation functions

Performance tuning

  • PostgreSQL for business intelligence
  • Detecting and optimizing slow queries
  • Optimizing large queries

 

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