ITTA offers an Apache Hadoop catalogue focused on big data and distributed processing. The Introduction to Hadoop Development course covers HDFS architecture, the MapReduce model, YARN, the ecosystem (Hive, HBase, Spark, Sqoop, Oozie), basic installation and configuration, Hadoop job writing, and integration with enterprise data sources. Audience: developers, data engineers, data architects, BI and data science profiles. Sessions delivered in Geneva, Lausanne and interactive virtual classroom.
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This Apache Hadoop Developer Certification Training will help you get a detailed idea about Big Data and Hadoop.
Apache Hadoop, created in 2006 and inspired by Google MapReduce and GFS publications, structured the big data era over the 2010s. Today, the landscape has evolved: cloud-native approaches (AWS S3 + Athena, Azure Data Lake + Synapse, Google BigQuery), engines like Apache Spark, Delta Lake, Iceberg, Snowflake and Databricks have largely taken over on new data projects. Hadoop is however not dead, contrary to what some quick takes suggest.
Apache Hadoop remains very present in two contexts: organisations that invested in Hadoop clusters over 2010-2020 and maintain them in production (banks, telecoms, public sector, research, industry), and hybrid ecosystems where Hadoop coexists with Spark, Hive, Kafka and cloud solutions. The Apache ecosystem (HDFS, YARN, Hive, HBase, Spark, Oozie) remains actively developed. Understanding Hadoop remains useful for data engineers inheriting existing platforms, migrating them, or wanting to understand the conceptual foundations of big data.
Our Hadoop course in the ITTA catalogue:
This course covers the fundamentals of Hadoop and its ecosystem: HDFS architecture (NameNode, DataNode, blocks, replication), MapReduce model (Mapper, Reducer, Shuffle, Combiner), YARN as resource orchestrator, ecosystem (Hive for SQL on Hadoop, HBase for NoSQL, Spark for in-memory processing, Sqoop for import, Flume and Kafka for ingestion, Oozie for orchestration), basic cluster installation and configuration, MapReduce job writing in Java, Hive queries in HQL, reading HDFS data with Spark, integration with enterprise sources (relational databases, log files, real-time streams), operations best practices. It is designed for technical profiles starting or maintaining a Hadoop platform.
Our Hadoop audience is targeted. You meet data engineers inheriting an existing Hadoop platform and needing to maintain it, back-end developers contributing to ETL jobs on cluster, data architects driving a migration from Hadoop to a modern lakehouse architecture (Spark on S3/Azure Data Lake + Delta Lake or Iceberg) and wanting to understand the existing before evolving it, BI profiles wanting to understand the technical bricks below their dashboards, students or data engineering retraining profiles wanting solid culture on big data foundations, consultants intervening in banking, telecom or industrial environments that still heavily use Hadoop.
Hadoop fits into a broader landscape covered by our data catalogue. The database design and development sub-domain regroups modelling and database training (relational and NoSQL). The data science sub-domain brings the analytical and applied AI use cases. The data and databases sub-domain on the IT pro side covers administration and BI competencies.
On the publisher side, the open source data ecosystem is coherent. The Open Source publisher regroups our open technologies training. The Apache Cassandra publisher brings the distributed NoSQL dimension, complementary to HBase. The Python publisher is central to modern data engineering and data science. For profiles combining Hadoop and AI, the ITTA Artificial Intelligence publisher allows extending to applied AI and ML.
Your organisation invested in Hadoop in 2014-2020 and still actively operates the platform. The Introduction to Hadoop Development course gives you the technical foundation to maintain, optimise and evolve this platform, having understood HDFS, YARN, MapReduce and the Hive ecosystem.
You want to migrate a Hadoop legacy to a Spark-on-S3 or Azure Data Lake architecture, with Delta Lake or Iceberg. Understanding Hadoop is a prerequisite to migrate without breakage. The course gives you the mapping of the existing and concepts to transpose.
You enter data engineering (from back-end development, BI or data science) and you want to understand big data foundations. Hadoop remains pedagogically very formative on distributed computing, partitioned storage and fault tolerance concepts, which then reappear in Spark, Kafka, lakehouse, etc.
The 2026 data landscape is more nuanced than a binary opposition. Hadoop (historical HDFS + MapReduce) is widely used in legacy cluster maintenance. Spark has largely replaced MapReduce as distributed compute engine (faster thanks to in-memory, more modern API, multi-language support). Modern lakehouse architectures (object storage S3 / ADLS / GCS + table format Delta Lake or Iceberg + Spark, Trino, Databricks or Snowflake engine) are the trajectory of new large-scale data projects. Hive remains widely used for SQL on data lake files. HBase remains for distributed NoSQL cases.
Our course addresses Hadoop in this global context and honestly explains when Hadoop remains relevant (legacy, certain industrial cases) and when a new project benefits from starting directly on Spark + lakehouse + cloud.
Several trends shape big data in 2026. Lakehouse architectures (object storage + Delta Lake / Iceberg + Spark / Trino) have become the de facto standard for new projects. Databricks and Snowflake have asserted themselves as two reference platforms for data engineering and analytics at scale. Generative AI enters the data pipeline via RAG (retrieval-augmented generation), leveraging vector databases, embeddings and data warehouses. Data engineers now must combine big data, streaming (Kafka, Flink), orchestration (Airflow, Dagster), modern formats (Parquet, ORC, Delta, Iceberg) and AI. Hadoop remains one brick among others in this new ensemble.
Our Hadoop sessions are scheduled in Geneva, Lausanne and in interactive virtual classroom with a live trainer. The course is very practice-oriented on a Hadoop cluster. Material modalities are communicated in advance by our education team. For data teams seeking grouped upskilling on their real Hadoop cluster, we organise in-house sessions calibrated on your architecture (Cloudera distribution, on-premise or cloud environment, deployed Hive, HBase, Spark ecosystem). This modality is very suited to banking, telecom, research and industrial contexts maintaining mature Hadoop platforms.
Is Hadoop still relevant to learn in 2026?
Yes, in two cases: if you work or will work on an existing Hadoop platform (very frequent in banking, telecom, public sector, industry), and if you want to understand the conceptual foundations of big data before Spark and lakehouse. For a new 2026 project without legacy, starting directly on Spark + lakehouse is the right reflex.
Do I need Java knowledge to follow?
A Java culture helps for historical MapReduce jobs. The course also addresses Hive (SQL) and Spark (Python/Scala). Prior programming experience is required, but not necessarily advanced Java.
Does the course cover Spark?
Spark is introduced as part of the Hadoop ecosystem (HDFS reading, YARN integration). To go further on Spark specifically, a dedicated session is more suited. The topic can be handled in-house on demand.
Hadoop vs cloud (AWS, Azure, GCP): what choice?
The cloud today offers managed equivalents of Hadoop (EMR on AWS, HDInsight on Azure, Dataproc on GCP) and modern lakehouse architectures. For a new project, the cloud is often the most relevant trajectory. For an on-premise Hadoop legacy, cloud migration requires a total cost and sovereignty analysis. Our course addresses these trade-offs.
ITTA offers a coherent data catalogue from big data foundations (Hadoop, Spark) to modern uses (Python data, data science, applied AI, cloud). This continuity allows addressing Hadoop within its ecosystem and discussing modernisation trajectory with a trainer also mastering lakehouse and cloud. Our Hadoop trainers are data engineers active on data platforms in French-speaking Switzerland, providing concrete examples in banking, telecom, public sector and industry. Sessions available in Geneva, Lausanne and interactive virtual classroom, in-house and inter-company.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
