Description

Introduction

Data Analytics & Aggregations Using q is a hands-on, performance-focused training program designed to help professionals analyze large-scale datasets efficiently using q, the powerful query language of kdb+.

This course dives deep into high-performance data analysis, grouped aggregations, time-series analytics, and real-time data exploration. Participants will learn how to write concise and expressive q queries to manipulate, summarize, and extract insights from massive datasets — a critical skill in industries such as financial services, telecom, IoT, and high-frequency trading.

By the end of the program, learners will confidently build advanced analytical queries, optimize aggregations, and perform scalable analytics on structured and time-series data using q.

Prerequisites

1. Basic understanding of databases and SQL concepts
2. Familiarity with data types and tabular data structures
3. Introductory knowledge of q or completion of q Fundamentals training
4. Understanding of time-series data

Table of Contents

Module 1: Refresher on q Essentials

1. q Data Types and Lists
2. Dictionaries and Tables
3. Keyed Tables
4. q-SQL Syntax Overview
5. Functional vs q-SQL Queries

Module 2: Data Retrieval & Filtering

1. Select Statements in q
2. Where Clauses and Conditional Filtering
3. Column Selection and Aliasing
4. Sorting and Limiting Results
5. Handling Nulls and Missing Data

Module 3: Aggregations in q

1. Basic Aggregation Functions (sum, avg, count, min, max)
2. Multi-Column Aggregations
3. Grouped Aggregations Using by
4. Conditional Aggregations
5. Aggregating on Keyed Tables

Module 4: Advanced Grouping & Analytics

1. Nested Aggregations
2. Multi-Level Grouping
3. Pivoting Data
4. Distinct and Unique Operations
5. Ranking and Top-N Calculations

Module 5: Time-Series Analytics

1. Time-Based Grouping
2. Windowed Aggregations
3. Moving Averages and Rolling Metrics
4. End-of-Day (EOD) Calculations
5. As-Of Joins for Time-Series

Module 6: Joins & Aggregated Data

1. Inner, Left, and As-Of Joins
2. Aggregating After Joins
3. Joining Large Datasets Efficiently
4. Performance Considerations

Module 7: Performance Optimization

1. Vectorized Operations in q
2. Efficient Use of each, over, and scan
3. Memory Optimization Techniques
4. Avoiding Common Performance Pitfalls
5. Query Benchmarking

Module 8: Real-World Analytical Use Cases

1. Trade & Market Data Analysis
2. Customer Transaction Analytics
3. KPI Dashboard Calculations
4. Risk & Exposure Aggregations
5. Streaming Data Aggregation Concepts

Module 9: Capstone Hands-On Project

1. Data Exploration
2. Designing Aggregation Logic
3. Performance Optimization
4. Analytical Reporting Output