Hadoop Training in Vizag | Softenant Technologies
Hadoop is a distributed-data ecosystem for storage and batch processing at scale. This course starts with HDFS architecture, blocks and replication, then covers MapReduce, YARN, Hive and the place of Spark-style processing in a big-data workflow.
Technology-specific learning focus
Instead of generic analytics language, learners follow data through ingestion, distributed storage, partitioned querying, job execution and operational checks such as failed tasks or skewed data.
Skills and syllabus areas
- HDFS files, blocks, replication and commands
- NameNode, DataNode and YARN roles
- MapReduce stages and data locality
- Hive tables, partitions and SQL-style queries
- Spark ecosystem and cluster workflow concepts
Project evidence
How practical work is assessed
Projects are reviewed against defined inputs, implementation choices, test cases, output quality and documented limitations. Learners should be able to explain the workflow, not just show a final screen or code sample.
Hadoop course syllabus: 20 modules
This 20-module syllabus is structured around the tools, decisions and practical outputs used in Hadoop work. Each module is tied to a demonstrable task, not a generic learning-stage label.
Big-data workloads and distributed-system vocabulary
Use a workload-comparison worksheet to classify storage, processing and scaling needs, then produce a justified big-data use-case brief.
Hadoop ecosystem, cluster roles and batch-processing use cases
Map HDFS, YARN, MapReduce, Hive, HBase and Spark in diagrams.net, then produce an annotated Hadoop ecosystem architecture.
HDFS files, blocks, replication and fault tolerance
Load sample files into an HDFS practice environment, inspect blocks and replication, then produce a storage and fault-tolerance report.
NameNode, DataNode and metadata responsibilities
Use the HDFS web interface and command-line tools to trace NameNode metadata and DataNode storage, then produce a labelled cluster-role diagram.
HDFS commands, ingestion and storage-layout practice
Run mkdir, put, get, ls and du commands in HDFS, organise a raw-data zone, then produce a reusable ingestion command log.
YARN resource management and application execution
Submit and inspect a YARN application with ResourceManager tools, then produce an execution timeline showing containers and resource allocation.
MapReduce mapper, shuffle, sort and reducer stages
Trace mapper, shuffle, sort and reducer output on a sample dataset, then produce a stage-by-stage MapReduce results file.
Data locality, partitioning and job-performance concepts
Use partitioned input files and job counters to compare data locality choices, then produce a short performance comparison with evidence.
Hive databases, external and managed table concepts
Create managed and external tables in Apache Hive, load sample data, then produce a table-definition and storage-location worksheet.
Hive SQL, partitions, bucketing and analytical queries
Write HiveQL queries with partitions and buckets against transaction data, then produce an analytical result table and query file.
HBase architecture and column-family storage concepts
Model a time-series lookup in HBase Shell using row keys and column families, then produce a documented table design with sample records.
Sqoop-style relational-data ingestion concepts
Plan a relational-to-Hadoop import with Apache Sqoop syntax, validate row counts, then produce an ingestion checklist and mapping sheet.
Spark ecosystem and Hadoop integration overview
Read HDFS data with Apache Spark and compare the workflow with MapReduce, then produce a notebook-style transformation summary.
Data quality, schema changes and duplicate-record checks
Use Hive queries and spreadsheet checks to find nulls, duplicates and schema drift, then produce a data-quality exception report.
Log and transaction dataset processing workflow
Process sample web logs or transactions through HDFS, Hive and Spark, then produce a cleaned dataset and business summary.
Cluster security, permissions and operational hygiene
Apply HDFS permissions and user-group rules in a lab cluster, then produce an access matrix and operational-security checklist.
Job failures, monitoring signals and troubleshooting notes
Inspect YARN logs, failed task output and job counters, resolve a sample failure, then produce a troubleshooting record with root cause.
Data-lake layers and downstream BI consumption
Design raw, cleaned and curated data-lake layers in diagrams.net and connect them to a BI consumer, then produce an end-to-end data-flow map.
Retail or log analytics pipeline project
Build a retail or log analytics pipeline with HDFS, Hive and Spark, then produce queryable tables plus a concise findings report.
Hadoop interview scenarios and project explanation
Use Hadoop CLI output and project documentation to answer operational scenarios, then produce an interview-ready architecture walkthrough.
Practical Hadoop workflow and project depth
How a Hadoop data workflow fits together
A Hadoop workflow begins before any processing job runs. Raw log, transaction or sensor files must be named, checked for schema consistency and placed in a suitable HDFS location. HDFS divides large files into blocks and replicates those blocks across DataNodes, while the NameNode keeps track of metadata. Learners practise explaining why distributed storage is useful when a single machine cannot store or process a dataset reliably.
HDFS, YARN and MapReduce in practical terms
HDFS is the storage layer, YARN is the resource-management layer and MapReduce is a processing model. A mapper produces intermediate key-value output, the shuffle and sort stage groups related keys, and reducers create final results. Jobs should be interpreted through their inputs, partitions, execution logs and outputs. This makes performance concepts such as data locality and skew understandable instead of treating them as definitions to memorise.
Hive, Spark and data-pipeline decisions
Hive supports SQL-style analysis over large stored datasets, especially when table layout and partitions match the query pattern. Spark is often introduced for faster in-memory transformations and analytics workflows, while HBase is considered for column-family storage needs. The course compares these ecosystem roles so that learners can explain why an organisation might use an HDFS/Hive batch workflow instead of an ordinary spreadsheet or transactional database.
Hadoop project evidence and interview discussion
A portfolio project should show the path from raw file to analytical output: an ingestion note, HDFS layout, Hive table definition, transformation logic, query results, error or quality checks and a final business summary. Interview preparation focuses on NameNode versus DataNode roles, replication, YARN, partitions, MapReduce stages, Hive tables and the reasons a particular cluster-style design was selected.
Hadoop environment and responsible practice
Hands-on learning should use safe sample data and a controlled environment. Learners keep command notes, explain the purpose of each directory and table, and distinguish an instructional cluster setup from production operations. This reinforces the practical habits required for data engineering work: traceability, repeatable jobs, carefully managed data access and evidence-based troubleshooting.
Hadoop operational documentation
A Hadoop implementation is stronger when its operational evidence is explicit. Learners prepare a short runbook that records the source location, HDFS destination, table or job name, partition strategy, expected record count, output location and troubleshooting observations. This mirrors the documentation used by data teams to make recurring batch work understandable, repeatable and easier to hand over.