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Columnar Storage and Query Optimization

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Columnar Storage and Query Optimization

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Gain insight into a topic and learn the fundamentals.
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Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain how data is stored, distinguish row vs columnar storage, and identify performance advantages of columnar formats

  • Work with Parquet and ORC formats, query columnar data using DuckDB, and compare storage performance against CSV

  • Read SQL execution plans using EXPLAIN, diagnose bottlenecks, and compare query performance across different engines

  • Apply optimization techniques including column pruning, filter pushdown, partitioning, and data skipping for analytics

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Recently updated!

April 2026

Assessments

19 assignmentsΒΉ

AI Graded see disclaimer
Taught in English

There are 5 modules in this course

Every data professional writes SQL queries β€” but few understand why some queries take seconds and others take minutes on the same data. The answer lies beneath the surface: in how data is stored, how query engines read that data, and how columnar formats like Parquet fundamentally change the game for analytics performance. This course gives you that understanding.

You will start from the foundations β€” how computers store and read data, how SQL operations access data internally, and what distinguishes row-based storage from columnar storage. From there, you will explore modern columnar formats (Parquet, ORC), work with DuckDB as your primary analytics query engine, and learn to read execution plans to diagnose exactly where queries slow down. Each concept is reinforced through hands-on demonstrations that you can follow along on your own setup. By the end of this course, you’ll be able to: - Explain how computers store data, distinguish between row-based and columnar storage, and identify when columnar formats provide a performance advantage. - Work with Parquet and ORC file formats, compare them to CSV, and query columnar data using DuckDB. - Read and interpret SQL query execution plans using EXPLAIN, and diagnose performance bottlenecks in analytical workloads. - Apply real-world query optimization techniques including column pruning, filter pushdown, partitioning, data skipping, and before-vs-after performance comparison. This course is designed for a diverse audience: Data Analysts who want to understand why their queries are slow, junior Data Engineers building foundational storage knowledge, BI Professionals moving into performance engineering or platform roles, and SQL Developers who want to go beyond writing queries to understanding how queries execute internally. Basic computer literacy is helpful. No prior SQL experience is required β€” though familiarity with basic statements will help you move faster. Stop guessing why queries are slow. Start understanding storage, execution, and optimization β€” and build the foundational skills every modern data team needs.

This module introduces how data is stored and organized in computer systems using files, tables, rows, and columns. It explains how SQL is used to access and manipulate data and how databases process read operations. The module also compares row-based and column-based storage to show how different storage models affect query performance.

What's included

13 videos6 readings5 assignments

13 videosβ€’Total 56 minutes
  • Course Introductionβ€’4 minutes
  • Core Data and Tabular Structure Overviewβ€’4 minutes
  • Foundations of Data Storage and Organizationβ€’6 minutes
  • Hands-On: Viewing CSV Data Using Spreadsheet Toolsβ€’5 minutes
  • Exploring the Purpose and use of SQLβ€’5 minutes
  • Internal Mechanisms of SQL Data Retrievalβ€’4 minutes
  • Hands-On: Running Basic SQL Queriesβ€’4 minutes
  • SQL Commands for Creating, Reading, Updating and Deleting Dataβ€’5 minutes
  • Comparing Read Heavy and Write Heavy Workloadsβ€’5 minutes
  • Hands-On : SQL CRUD Operationβ€’4 minutes
  • Understanding Row Based and Column Based Storageβ€’4 minutes
  • Hands-On: Evaluating Row and Column Storage Approachesβ€’3 minutes
  • Hands-On: How Storage Models Affect Query Executionβ€’4 minutes
6 readingsβ€’Total 57 minutes
  • Course Syllabusβ€’7 minutes
  • Design Principles of Data Organizationβ€’10 minutes
  • Core SQL Concepts for Data Analysis β€’10 minutes
  • Managing Data Operations in SQL Systems β€’10 minutes
  • Row and Column Storage Structuresβ€’10 minutes
  • Module Summary: Foundations of Data Storage and SQL for Analytics β€’10 minutes
5 assignmentsβ€’Total 39 minutes
  • Foundations of Data Storage and SQL for Analyticsβ€’15 minutes
  • Data Storage and Retrievalβ€’6 minutes
  • Introduction to SQL and Data Representationβ€’6 minutes
  • SQL Data Processing Concepts β€’6 minutes
  • Row vs. Column Storage Explainedβ€’6 minutes

This module explains how columnar storage is used in modern data systems and data warehouses for efficient analytics. It introduces common columnar formats and tools used in industry, and demonstrates how techniques like compression, data skipping, and partitioning improve query performance.

What's included

12 videos4 readings4 assignments

12 videosβ€’Total 49 minutes
  • Columnar storage in Modern Data Systemsβ€’4 minutes
  • Columnar Storage Formats: Parquet and ORCβ€’4 minutes
  • Columnar Systems vs. File Based Storage Architectures β€’4 minutes
  • Hands-On : Exploring Columnar Parquet Files Using Query Engineβ€’3 minutes
  • Hands-On : Comparing CSV vs. Columnar File Sizeβ€’3 minutes
  • Role of Columnar Storage in Modern Data Warehousesβ€’4 minutes
  • Modern Columnar Systems in Open Source and Cloudβ€’5 minutes
  • Hands-On: Querying Data in Columnar Storage Systemsβ€’4 minutes
  • Impact of Compression on Query Performanceβ€’5 minutes
  • Data Skipping , Metadata, and Partitioning Techniquesβ€’4 minutes
  • Hands-On: The Role of Filters in Efficient Data Accessβ€’4 minutes
  • Hands-On: Partitioned vs. Non-Partitioned Queriesβ€’5 minutes
4 readingsβ€’Total 40 minutes
  • Columnar Storage Architecture for Analytical Systemsβ€’10 minutes
  • Scalable Columnar Data Platforms and Ecosystemsβ€’10 minutes
  • Performance Optimization in Columnar Data Platformsβ€’10 minutes
  • Module Summary: Columnar Storage in Modern Industry Systemsβ€’10 minutes
4 assignmentsβ€’Total 33 minutes
  • Columnar Storage in Modern Industry Systemsβ€’15 minutes
  • Data Storage Formats and Structuresβ€’6 minutes
  • Columnar Data Processing with DuckDBβ€’6 minutes
  • Efficiency Gains with Columnar Storageβ€’6 minutes

This module introduces query engines and SQL tools used by analysts and engineers to process data. It explains how SQL queries are executed internally and how query plans represent the steps a system takes to run a query. The module also compares different query engines to understand why some systems perform faster for analytical workloads.

What's included

9 videos4 readings4 assignments

9 videosβ€’Total 39 minutes
  • Role of Query Engineβ€’4 minutes
  • Hands-On: Running SQL Queries in DuckDBβ€’4 minutes
  • Execution Lifecycle of SQL Queriesβ€’3 minutes
  • Query Plansβ€’5 minutes
  • Hands-On: Analyzing Query Execution Flowβ€’5 minutes
  • Hands-On: Interpreting Query Execution Plansβ€’3 minutes
  • Performance Differences Across Query Enginesβ€’5 minutes
  • Hands-On: Storage Format and Query Performance Analysisβ€’4 minutes
  • Hands-On: Cross-Engine SQL Query Comparisonβ€’5 minutes
4 readingsβ€’Total 40 minutes
  • Architectural Components of Analytical Query System β€’10 minutes
  • Query Processing Techniques in Analytical Databasesβ€’10 minutes
  • System Design Factors in Query Engine Performance β€’10 minutes
  • Module Summary: Query Engines and SQL Processing Systemsβ€’10 minutes
4 assignmentsβ€’Total 33 minutes
  • Query Engines and SQL Processing Systemsβ€’15 minutes
  • SQL Execution with in Query Enginesβ€’6 minutes
  • Query Execution and Query Plansβ€’6 minutes
  • Analytical Query Systemβ€’6 minutes

This module explains why query optimization is important for improving data processing efficiency and reducing slow query performance. It introduces practical SQL optimization techniques such as filtering, column pruning, and efficient aggregations. The module also demonstrates real-world optimization workflows using industry tools to compare query performance before and after optimization.

What's included

9 videos4 readings4 assignments

9 videosβ€’Total 36 minutes
  • Optimizing SQL Query Performanceβ€’5 minutes
  • Impact of Query Performance on Business Operationsβ€’4 minutes
  • Hands-On: Evaluating Query Inefficienciesβ€’3 minutes
  • Performance Optimization Strategies for Columnar SQL Databasesβ€’4 minutes
  • Hands-On: Bad SQL vs Optimized SQL Queriesβ€’4 minutes
  • Hands-On: The Role of Filters in Efficient Data Accesβ€’4 minutes
  • Query Optimization Pipeline: An End to End Perspectiveβ€’3 minutes
  • Real World Application of Analytics Optimization Techniquesβ€’6 minutes
  • Hands-On: Comparing Query Performance Before and After Optimizationβ€’3 minutes
4 readingsβ€’Total 40 minutes
  • Managing Query Performance in Analytic Systemsβ€’10 minutes
  • Efficient Query Design in Analytical Systemsβ€’10 minutes
  • Strategies for Sustaining Query Performanceβ€’10 minutes
  • Module Summary: Query Optimization Concepts and Best Practicesβ€’10 minutes
4 assignmentsβ€’Total 33 minutes
  • Query Optimization Concepts and Best Practicesβ€’15 minutes
  • Principles of Query Optimization and Its Improtance β€’6 minutes
  • SQL Query Optimization Strategiesβ€’6 minutes
  • Real World Query Optimization in Industry Systemβ€’6 minutes

This module consolidates key concepts from data storage, SQL querying, query execution, and optimization. It evaluates understanding through structured assessments and practical query analysis scenarios. It serves as a final checkpoint to assess readiness for real-world data querying and performance optimization tasks.

What's included

1 video1 reading2 assignments

1 videoβ€’Total 4 minutes
  • Course Summaryβ€’4 minutes
1 readingβ€’Total 30 minutes
  • Practice Project: Designing Fast Data Systems for Analyticsβ€’30 minutes
2 assignmentsβ€’Total 60 minutes
  • Final Course Assessment : Columnar Storage and Query Optimizationβ€’30 minutes
  • Optimizing the Data Core – Columnar Storage and Query Performanceβ€’30 minutes

Instructor

Edureka
203 Coursesβ€’185,724 learners

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Frequently asked questions

Columnar storage organizes data by columns rather than rows. For analytical queries that typically scan a few columns across millions of rows, columnar formats like Parquet and ORC dramatically reduce the amount of data read from disk β€” enabling faster queries, better compression, and lower compute costs. This course teaches you exactly how and why.

The primary hands-on tool is DuckDB β€” a fast, in-process analytical query engine that requires minimal setup. You will also work with Parquet files, CSV data, and SQL query plan tools (EXPLAIN). No complex installation or cloud accounts are needed.

Yes. This course is built around a follow-along, demonstration-driven learning model. Each concept is taught through step-by-step video demonstrations using DuckDB and Parquet that you can replicate on your own machine. DuckDB runs locally with minimal setup, so you can pause, rewind, and practice alongside each demo at your own pace.

Basic SQL familiarity (SELECT, FROM, WHERE) is helpful but not strictly required. Module 1 begins with SQL fundamentals β€” what SQL is, CRUD operations, and how READ operations work internally β€” before progressing to advanced query optimization. The course is accessible for beginners while still valuable for experienced SQL users.

DuckDB is a modern, open-source analytical query engine designed for fast, in-process SQL analytics. It runs locally without a server, reads Parquet files natively, and provides query plan visibility through EXPLAIN β€” making it ideal for learning storage and optimization concepts without infrastructure overhead.

A query execution plan shows the step-by-step strategy a query engine uses to retrieve your data β€” including scan operations, filter application, join methods, and aggregations. In Module 3, you will use EXPLAIN to view execution plans, interpret them conceptually, and diagnose where queries slow down.

Both Parquet and ORC are columnar file formats designed for analytics. Parquet is more widely adopted in cloud and open-source ecosystems (Spark, Hive, DuckDB), while ORC is optimized for Hive-based environments. This course covers both formats and helps you understand when to use each.

You will learn column pruning (reading only needed columns), filter pushdown (applying filters early), partitioning (organizing data for faster scans), data skipping (avoiding irrelevant data blocks), and aggregation optimization. Module 4 includes before-vs-after comparisons so you can measure the real performance impact of each technique.

Data Analysts who want to understand query performance, junior Data Engineers building storage foundations, BI Professionals moving into platform or performance roles, and SQL Developers who want to go beyond syntax to understand execution internals. If slow queries frustrate you, this course is for you.

Storage and query optimization are foundational skills for Data Engineering, Analytics Engineering, BI Development, Database Engineering, and Performance Engineering roles.

Basic computer literacy is sufficient. SQL is taught from fundamentals in Module 1. No prior experience with DuckDB, Parquet, or query optimization is required. Familiarity with basic SELECT statements will help you move faster but is not mandatory.

Yes. Upon completing all graded assessments and the final course assessment, you will earn a Coursera Course Certificate from Edureka that you can add to your LinkedIn profile, resume, or CV.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

ΒΉ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.