SQL for Data Analysis

Learn SQL for querying security data across relational databases, flat files, and platforms like Spark, Drill, and ElasticSearch.

Overview

A solid understanding of SQL expands the number of data sources an analyst can access. SQL is not limited to traditional relational databases. Tools like Apache Drill, Spark, Flink, and ElasticSearch all support SQL-based querying, which means analysts who know SQL can work across log management platforms, data lakes, and structured file formats without learning a new query language for each.

This 16-hour course teaches analysts to leverage SQL for data analysis across multiple platforms. 50% of class time is instructor-led, and 50% is hands-on labs.

What You Will Learn

  • Execute foundational SQL queries effectively
  • Understand relational database architecture
  • Query non-relational datasets and flat files using SQL
  • Combine multiple datasets through join operations
  • Develop complex aggregate queries for data summarization

Who This Is For

Security analysts, threat hunters, and data analysts who work with structured data and want to expand their ability to query across diverse data sources. No prior SQL experience required.

SQL skills complement Python and data science capabilities. Students who complete this course benefit from combining it with Python Coding for Security Analysts and Applied Data Science & AI for Cybersecurity.

Topics covered

  • Foundational SQL query execution
  • Relational database architecture
  • Querying non-relational datasets and flat files with SQL
  • Combining datasets through join operations
  • Aggregate queries for data summarization
  • Querying across Spark, Flink, Drill, Cassandra, and ElasticSearch

Tools & technologies

SQLApache DrillJupyterCentaur VM

Frequently Asked Questions

Can I query log files directly with SQL using Apache Drill?
Yes. Apache Drill executes ANSI SQL against JSON, CSV, Parquet, Avro, log files, and many other formats without first loading them into a database. For security analysts, this means querying gzipped firewall logs, JSON-formatted DNS exports, or Parquet-compressed Zeek output with the same SQL skill, no ETL required.
Why learn SQL when Python and Pandas can do the same thing?
Pandas processes data in memory on a single machine. SQL query engines (Spark, Drill, ElasticSearch, Snowflake) push computation to where the data lives and scale to terabytes. For exploratory analysis on a few gigabytes, Pandas is easier; for production analytics on production volumes, SQL is the practical option. Most analysts end up using both.
What SQL window functions are useful for security analysis?
ROW_NUMBER, RANK, and LAG/LEAD for sequencing events per user or host (login chains, command sequences). SUM and AVG OVER for rolling time-window aggregations (logins per hour per user). PARTITION BY for per-entity statistics without explicit grouping. Window functions replace many of the brittle self-joins analysts write before they learn them.
How do I query Elasticsearch using SQL?
Elasticsearch ships with a SQL endpoint that translates SQL queries into the underlying query DSL. It supports SELECT, WHERE, GROUP BY, JOIN (limited), and aggregations. Useful for analysts comfortable with SQL who do not want to learn the query DSL for ad-hoc analysis. Not every Elasticsearch feature is exposed via SQL, but the core analytical features are.
What prerequisites do I need for the SQL for Data Analysis course?
None. The course starts with foundational query syntax and assumes no prior SQL experience. Basic comfort with computers and a willingness to type queries is sufficient. Analysts who already know SQL but want to query non-relational sources (Drill, Spark, ElasticSearch) often skip the early sessions and pick up the new platforms.

Interested in this course?

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