Virtual Training Data Science, AI, and Advanced Analytics

Big Data Analytics with Apache Spark Online Course

Join our virtual, live instructor-led session and master Big Data Analytics with Apache Spark Training from anywhere in the world.

10 Days Duration
Live Online Delivery
7 Dates Available
Certificate Included
Master Big Data Analytics with Apache Spark to architect scalable data pipelines, optimize distributed workloads, and deploy real-time streaming solutions using industry-standard frameworks.

Upcoming Virtual Training Schedules

Join from anywhere in the world with our live instructor-led sessions

Code Start Date End Date Duration Fee
BDA-02 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Register my team →
BDA-02 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Register my team →
BDA-02 Weekend (8 Weeks) USD 1,700 Reserve my seat → Register my team →
BDA-02 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Register my team →
BDA-02 Weekend (8 Weeks) USD 1,700 Reserve my seat → Register my team →
BDA-02 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Register my team →
BDA-02 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Register my team →
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USD 1,700
BDA-02
Training Date
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10 Days
USD 1,700
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Training Date
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8 Weeks
USD 1,700
BDA-02
Training Date
to
10 Days
USD 1,700
BDA-02
Reserve my seat
Training Date
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10 Days
USD 1,700
BDA-02
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Here's What You'll Learn

Each module tackles real challenges you face in your role

1

Spark Foundations and Big Data Ecosystem

2

The Spark Programming Model

3

Spark SQL and Structured Data

4

Data Sources and Storage Formats

5

Advanced Spark Performance Tuning

6

Spark Structured Streaming Fundamentals

7

Integration with Apache Kafka

8

Machine Learning with Spark MLlib

9

GraphX and Graph Analytics

10

The Data Lakehouse with Delta Lake

11

Cloud Deployment and Cluster Management

12

Monitoring, Security, and Governance

13

Testing and CI/CD for Spark Jobs

Market-specific guidance for Australia

A country-aware view of the pressures, proof points, and practical tools that shape how this course applies locally.

Why this course matters in Australia

Strategic context for the risks, opportunities, and capability gaps this training addresses locally.

Big Data Analytics with Apache Spark matters in Australia because organisations are under pressure to process larger, faster-moving datasets without rebuilding every pipeline from scratch. Spark’s distributed in-memory model is relevant for teams that need faster batch analytics, streaming, and machine-learning workflows than traditional approaches can comfortably support. The course is especially useful for data engineering, analytics, and platform teams that must decide how to modernise ETL, support near-real-time reporting, and reduce processing bottlenecks as data volumes grow.

Faster path from batch to streaming

Australian teams using Spark can combine batch processing and Structured Streaming in one ecosystem, which helps reduce fragmentation when organisations want near-real-time dashboards or event-driven pipelines.

Modernising legacy ETL

The course is relevant where traditional MapReduce-style jobs or older ETL patterns are too slow for current reporting demands, especially in data-heavy sectors that need shorter turnaround times.

Higher-value use of data engineering talent

Spark skills help teams spend less time on cluster mechanics and more time on data modelling, pipeline reliability, and analytics delivery, which is valuable in a market where experienced data engineers are in demand.

This training is timely because Australian organisations are continuing to expand cloud data platforms and seek faster analytics delivery across batch and streaming workloads. It is particularly relevant where operational teams need to cut data latency, improve pipeline resilience, and support more advanced analytics without increasing platform complexity.

Tools and platforms relevant to this field

4

Field-relevant examples that may be featured in training where they support the confirmed scope. Exact coverage depends on participant needs and delivery format.

  • Databricks Lakehouse Platform Databricks
    Used for Spark-based engineering, SQL analytics, and managed notebooks in cloud data platforms.
  • Apache Spark Apache Software Foundation
    Used for distributed batch processing, Structured Streaming, and machine-learning workloads at scale.
  • Apache Kafka Apache Software Foundation
    Used to feed streaming pipelines into Spark for event ingestion and low-latency processing.
  • Delta Lake Databricks
    Used to add reliability, schema enforcement, and transactional storage patterns to Spark data lake workflows.

Where this course runs

Big Data Analytics with Apache Spark Training is delivered in the cities below — pick the one that fits your schedule.

Real Results from Real Professionals

Thousands of professionals have transformed their careers through our training programs. Now, it's your turn.

Customize Training Duration

The standard duration for Big Data Analytics with Apache Spark Training is 10 Days. The options below are alternative durations with adjusted pricing.

Looking for the standard 10 Days schedule? Use the button below.

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Barbours
Bank of Rwanda
RFA
Dahabshil Bank
Dorcas Aid
Finn Church Aid
KCB Foundation
Ministry of Education Saudi Arabia
NSSF Uganda
RBA
Reserve Bank of Malawi
WASREB Kenya
Virginia Commonwealth University