PySpark – Big Data Analysis in Databricks (PYSPARK1)

Databases, Data Analytics

Do you work with Excel, Power Query, SQL or Pandas but need to process gigabytes to terabytes of data? PySpark is the Python interface to Apache Spark, a big data engine for scalable, distributed processing that can handle datasets larger than one machine's memory and speed up analysis.

The workshop runs entirely in Databricks Community Edition in your browser - no local setup required. You'll learn the DataFrame API and Spark SQL, work with notebooks, clusters and data uploads, and apply familiar SQL skills to scale analyses to large datasets.

THIS TRAINING COURSE WILL HELP YOU:

  • Process large datasets using PySpark DataFrame API
  • Run SQL queries with Spark SQL and combine APIs
  • Read and write CSV, Parquet and Delta formats
  • Perform joins, aggregations, pivots and data transforms
  • Use Databricks notebooks, clusters and export results

WHO SHOULD ATTEND?

  • Data analysts using Excel, Power Query or SQL
  • Python users familiar with Pandas who need scale
  • BI developers working with large datasets
  • Anyone preparing data for analytics at scale

COURSE LOCATION AND AVAILABLE DATES



Choose whether to attend in person in our classroom or join online. You can select your preferred format during registration. Learn more about hybrid training.

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Course content:

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  • Getting started with Databricks
    1. What PySpark is and when to use it
    2. Creating an account in Databricks Community Edition
    3. Navigating the environment – workspace, notebooks, cluster
    4. Uploading data to Databricks
  • DataFrame – basic operations
    1. Creating DataFrames
    2. Schema and data types
    3. Selecting columns (select)
    4. Filtering rows (filter, where)
    5. Adding and transforming columns (withColumn)
  • Spark SQL
    1. Registering a DataFrame as a table (createTempView)
    2. Running SQL queries on data (spark.sql)
    3. Combining DataFrame API and SQL
    4. Using SQL functions in the DataFrame API
  • Data sources
    1. CSV files
    2. Parquet – optimal format for Spark
    3. JSON files
    4. Delta Lake (basics)
  • Data processing
    1. Column and type transformations
    2. Handling missing values (null)
    3. Joining tables (join)
    4. Combining datasets (union)
  • Data aggregation
    1. Grouping (groupBy)
    2. Aggregate functions (count, sum, avg, min, max)
    3. Multiple aggregations at once (agg)
    4. Pivot tables
  • Troubleshooting
    1. Reading PySpark error messages
    2. Common errors: data types, missing columns
    3. Data checks and debugging
  • Outputs and exporting data
    1. Saving to files (CSV, Parquet)
    2. Visualizations in Databricks
    3. Downloading results
Prerequisites:
Basic Python (variables, loops, functions); experience with SQL, Excel, Power Query or Pandas is advantageous.
Recommended previous course:
Python – Programming Basics (PYTH1)
Schedule:
2 days (9:00-17:00)
Price per person:
432.00 € ( 522.72 € incl. 21% VAT)

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