Official Training Big Data – Concept, Tools & Techniques

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Big Data – Concept, Tools & Techniques

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Big Data Training: Concepts, Tools and Techniques

Big Data – Concept, Tools and Techniques is a 36-course Learning KIT that teaches you how to work with large-scale data environments using industry-standard tools including Hadoop, Apache Spark, and NoSQL databases. The training covers the full spectrum from Big Data fundamentals through to real-time stream processing and applied data analytics. It is built for aspiring data engineers, analytics professionals, and IT practitioners who want to develop practical Big Data skills. Expert tutor support is available 24/7 through the DiviTrain platform.

What this training includes

  • 32+ hours of e-learning — 365 days access
  • 5 hours of hands-on labs included
  • Expert tutor support available 24/7
  • Organizations seeking team-wide training can explore our corporate volume solutions.

Ready for roles like

  • Big Data Engineer
  • Data Engineer
  • Data Architect
  • Analytics Engineer
  • Data Warehouse Engineer

What this Big Data training covers

Big Data Fundamentals and the 5 V's +
Understand what defines Big Data and why traditional data tools fall short at scale. You will explore the five core dimensions — Volume, Velocity, Variety, Veracity, and Value — and learn how distributed computing addresses each one in real production environments.
Hadoop Architecture and HDFS +
Learn how the Hadoop Distributed File System stores and manages large datasets across clusters. Topics include NameNode and DataNode architecture, data replication strategies, and the core principles of cluster setup and administration.
MapReduce Programming Model +
Work with the MapReduce paradigm for distributed batch processing. You will learn how Mapper and Reducer functions divide and process data across nodes, and how Hadoop manages job execution and fault tolerance at scale.
Hadoop Ecosystem: Hive, Pig, Sqoop, Flume, and Oozie +
Explore the broader Hadoop toolset used in production data environments. This module covers Hive for SQL-style querying, Pig for data transformation scripting, Sqoop for relational database integration, Flume for log ingestion, and Oozie for workflow scheduling.
Apache Spark Core and RDDs +
Get hands-on with Apache Spark's core engine and the Resilient Distributed Dataset model. You will learn how Spark's in-memory processing delivers significant performance improvements over MapReduce for iterative and interactive workloads.
Spark SQL and DataFrames +
Use Spark SQL and the DataFrame API to query and transform large structured datasets. This module covers running SQL operations on distributed data, optimizing queries, and integrating Spark SQL with existing data warehouse workflows.
Spark Streaming and Real-Time Processing +
Build real-time data pipelines using Spark Streaming and micro-batch processing. You will work with continuous data sources, apply transformations on live streams, and understand the architecture behind low-latency analytics systems.
NoSQL Databases: MongoDB, Cassandra, and HBase +
Learn when and how to use non-relational databases in Big Data architectures. This module covers document storage with MongoDB, wide-column storage with Apache Cassandra, and Hadoop-native storage with HBase — including CRUD operations, indexing, and replication patterns.
Data Ingestion, ETL, and Pre-Processing +
Understand how raw data flows from source systems into Big Data platforms. Topics include ETL pipeline design, data quality and cleansing, schema-on-read versus schema-on-write, and the distinctions between data lake and data warehouse architectures.
Big Data Analytics and Applied Use Cases +
Apply Big Data tools to real analytical problems across industries including retail, finance, healthcare, and telecommunications. You will explore how organizations use Spark MLlib and batch analytics to extract insight from large datasets and support data-driven decisions.

Where can Big Data skills take your career

Career paths and next steps after this Big Data training +
Big Data skills are in high demand across cloud-native companies, financial institutions, and technology teams building data-driven products at scale. After completing this training, many professionals move into MLOps to learn how to deploy and maintain machine learning models in production, or progress toward cloud data engineering roles on platforms like Microsoft Azure.

Frequently Asked Questions

What Big Data tools and frameworks does this training cover +
The training covers the core tools used in modern Big Data environments, including Hadoop and its ecosystem (HDFS, Hive, Pig, Sqoop, Flume, Oozie), Apache Spark (Core, SQL, Streaming), and NoSQL databases including MongoDB, Apache Cassandra, and HBase. You will also work with ETL concepts, data pipeline design, and applied analytics use cases across all 36 courses.
Do I need programming experience to start this Big Data training +
Basic familiarity with programming concepts is helpful, particularly for the Spark and NoSQL modules. The training starts from fundamentals and does not assume prior Big Data experience. Modules covering Spark SQL and DataFrame operations will be easier to follow if you have a working knowledge of SQL or Python.
What is the difference between Hadoop and Apache Spark +
Hadoop is a distributed storage and batch processing framework built around HDFS and MapReduce. Apache Spark is a faster in-memory processing engine that can run on top of Hadoop or independently. Spark processes data significantly faster than MapReduce for iterative workloads, which is why it has become the standard engine for Big Data analytics, machine learning, and streaming. This training covers both frameworks so you understand when to use each.
Is the exam voucher included and how do I register for the exam +
The exam voucher is not included in this training. The exam is administered globally by Pearson VUE, either at an authorized testing center or via online proctoring. Once your preparation is complete, you register and purchase your exam voucher directly through the official certification or Pearson VUE website.
Can my team or organization get certified together +
Yes. DiviTrain offers volume licensing for teams and organizations looking to upskill at scale. Whether you are certifying a small IT team or rolling out training across departments, our corporate solutions provide flexible access and invoicing options. Visit our For Teams page to learn more.
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Value Comparison

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Same exam. A fraction of the cost. See how this CertKit stacks up against the alternatives.

Best ValueDiviTrain CertKit Classroom Training Pluralsight / LinkedIn
Price $349 €1,500–€2,000 From $399/year
Video training ✓ ✓ ✓
Hands-on labs ✓Guided, virtual environment ✓ Premium only
MeasureUp practice exams ✓60 days included ✕ ✕
Expert tutor support ✓Available 24/7 ✕ ✕
Access duration 365 days 5 days While subscribed
Study at your own pace ✓ Fixed schedule ✓
Exam voucher included ✕Book via Pearson VUE Sometimes ✕

* Prices shown are indicative examples. Actual prices may vary by product, provider and region.

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