Data Science · Advanced level

Advanced level · Module 3: Working with Larger and Spatial Data · Lesson 4 of 4

Big Data Concepts

Understand when data truly needs distributed tools and what those tools do.

In this lesson you will

  • Explain distributed processing concepts
  • Judge when big-data tools are justified

Key concepts

distributed processingSparkcloud data warehousescost and complexity

The lesson

When data no longer fits on one machine even with efficient methods, distributed systems such as Apache Spark split work across many computers. Cloud data warehouses offer similar scale through SQL.

These tools add cost, complexity and dependence on reliable internet. Many 'big data' problems are solved on one laptop with Parquet and DuckDB.

Choose tools by data size, growth, team skills and budget, not fashion.

Step by step

  1. Estimate the size of three data scenarios.
  2. Decide which need distributed tools.
  3. Read a simple Spark example and identify the parallel steps.
  4. Write a tool recommendation.

Practice activity

Recommend tools for three provided data scenarios.

Expected result: Justified recommendations that avoid unnecessary complexity.

Check your understanding

  1. When are distributed tools needed?

    Show answer

    When data and processing truly exceed what one machine can handle efficiently.

  2. Name a cost of big-data platforms.

    Show answer

    For example complexity, cloud fees or the need for reliable connectivity.

Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.