Data too big for one machine? Process it all. Master Hadoop and Spark for large-scale data processing.
Hands-on with distributed computing — the tools that crunch terabytes across clusters.
Process data across many machines.
Fast, large-scale data processing.
A core skill for data engineers.
Big Data with Hadoop & Spark teaches you to process data too large for a single machine. You'll understand the Hadoop ecosystem (HDFS and the big-data landscape), then focus hands-on on Apache Spark — the fast, in-memory engine for large-scale processing — using PySpark for data transformation, SQL and analytics across distributed clusters.
Python & SQL! Comfort with Python and SQL is recommended. This is an advanced, applied course for aspiring data engineers.
Earn the "Big Data Engineer" badge upon completing all modules and the hands-on final assessment.
Verifiable CredentialHDFS, MapReduce and the landscape.
RDDs, DataFrames and the Spark model.
Transform and analyse big data in Python.
Query massive datasets efficiently.
A real large-scale processing project.
Process data at massive scale.
Build distributed data pipelines.
Deliver fast, large-scale analytics.
Prepare big data for machine learning.
Comfort with Python and SQL is recommended, since we use PySpark and Spark SQL. This is an advanced course aimed at aspiring data engineers.
A laptop; we use free tools — Apache Spark locally and free cloud notebooks (like Databricks Community). Set-up help is provided.
To receive the Edmire Big Data Engineer Certificate, complete all 5 modules and the capstone — a Spark data-processing project. Resubmission is free if needed.
Spark is now the dominant processing engine, but understanding the Hadoop ecosystem (especially HDFS) remains valuable. We focus hands-on on Spark while giving you the Hadoop context.
Classroom training at our Dubai centre, live online instructor-led classes, or custom in-house delivery — with all materials yours to keep.
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