Big Data Tutorials
The Big Data learning path is designed for mastering large-scale data processing. Our tutorials cover essential concepts and tools, suitable for both beginners and intermediate learners. Through hands-on labs and real-world code examples, you'll gain practical experience in data analysis, processing, and visualization. Our interactive playground allows you to experiment with Big Data technologies in a risk-free environment.
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Restore the Right Objects and Keep New Work
Recover the approved HDFS files while keeping later work and real read-only group access.
Big Data
Create a Recoverable Point Before Editing
Create a native HDFS recovery point and compare protected history with later live changes.
Big Data
Track Kafka Consumer Offsets and Replay Events
Observe committed next offsets and lag, resume a bounded named consumer, and deliberately replay retained records through an inactive-group reset.
Big Data
Read Spark CSV with an Explicit Schema
Compare string-only and inferred CSV loading with an explicit seven-column schema, then apply the same types to a new delivery.
Big Data
Understand Map, Shuffle, and Reduce
Predict product counts, inspect Map/Shuffle/Reduce with a small teaching tool, and run the same WordCount algorithm using Hadoop's prepared example JAR in local mode.
Big DataLinuxShell
Understand Spark Execution and Common Errors
Connect familiar transformations to a real execution plan and event log, distinguish computation from directory partitions, and repair two common expression errors.
Big Data
Your First Hadoop Lab
Discover what big data means and where Hadoop fits, then upload a retail text file to HDFS, read it back, and verify its contents in a prepared environment.
Big DataLinuxShell
Your First Hive Query
Discover where Apache Hive fits in a data workflow, explore a prepared retail table, and write your first SQL file to select and check paid order records.
Big DataShell
Your First PySpark DataFrame
Run a complete Spark script, inspect a small order DataFrame and its schema, and predict how one extra record changes a fresh run.
Big Data
Write and Inspect Partitioned Spark Parquet Data
Store prepared orders in date-partitioned Parquet, reconstruct and query their typed records, and compare overwrite with append in a disposable output.
Big Data
Submit and Rerun a Spark Batch Application
Use spark-submit with explicit master and input/output arguments, inspect real exit status and logs, and recover safely from missing input and existing output paths.
Big DataShell
Submit and Inspect a YARN Batch Job
Run the familiar product WordCount over HDFS inputs using real YARN execution, inspect the application status and logs, and validate and rerun its output safely.
Big DataShellLinux
Share Work with Kafka Consumer Groups
Observe two live consumers sharing partitions, an independent group reading the same events, and assignment recovery after one member leaves.
Big DataLinuxShell
Send and Read Your First Kafka Event
Understand Kafka's event-delivery role, create a one-partition topic, observe separate producer and consumer terminals, and replay retained order events.
Big DataLinux
Select, Filter, and Transform Spark Order Data
Use typed column expressions to select fields, combine row conditions, calculate decimal order amounts, and compare paid and pending results.
Big Data
Save Hive Query Results as ORC Tables
Persist a decimal daily-sales report in ORC, read it back, and compare repeatable overwrite with duplicate-producing append on a disposable table.
Big Data
Route Kafka Events with Keys and Partitions
Publish keyed order events, inspect actual keys, partitions, and offsets, and observe per-order routing without assuming a global record order.
Big Data
Read JSON Events into Spark DataFrames
Read static JSON Lines with explicit types, select order events, and flatten a simple nested customer struct without changing to streaming.
Big Data
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