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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10 Days
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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 Ukraine

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

Why this course matters in Ukraine

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

Apache Spark training matters in Ukraine because organizations that handle large, fast-growing datasets need faster batch processing, near-real-time analytics, and more scalable data engineering practices than legacy approaches typically provide. The course is most relevant for data engineering, analytics, platform, and architecture teams that support modernization of reporting, streaming, and machine-learning pipelines. It helps leaders decide how to improve throughput, reduce pipeline latency, and standardize distributed data processing across cloud or hybrid environments. Spark’s role in large-scale data processing, interactive queries, machine learning, and streaming is directly aligned with these operational needs.

Modernize analytics delivery

Ukrainian teams that still rely on slower batch-style workflows can use Spark to shorten turnaround time for reporting and exploration, especially where business users expect faster refresh cycles.

Support streaming use cases

Spark Streaming skills are relevant for organizations that need event-driven processing for logs, transactions, or operational telemetry rather than overnight ETL only.

Build reusable data-engineering capability

A shared Spark skill set helps data engineers and architects standardize how distributed jobs are written, tuned, and maintained across projects, which is important when multiple teams depend on the same platform.

This training is timely where organizations are under pressure to scale analytics without increasing pipeline latency or operational complexity. Spark’s combination of distributed computing, SQL, streaming, and machine-learning support makes it relevant for teams modernizing data platforms in cloud or hybrid environments.

Tools and platforms relevant to this field

5

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

  • Apache Spark Apache Software Foundation
    Used for distributed in-memory processing of large datasets, SQL analytics, streaming, and machine-learning workflows.
  • PySpark Apache Software Foundation
    Used when teams want to build Spark pipelines in Python, which is common for analytics and data-engineering work.
  • Spark SQL Apache Software Foundation
    Used to query structured data efficiently and integrate Spark with familiar SQL-based analytics workflows.
  • Spark Structured Streaming Apache Software Foundation
    Used to build continuous data pipelines for near-real-time ingestion and processing.
  • MLlib Apache Software Foundation
    Used to develop machine-learning pipelines directly on distributed Spark data.

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