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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Build a Complete Comparison Table
Create complete Spark product comparisons with viewed-session denominators, valid cart counts, ties and reusable Parquet/CSV output.
Big Data
Create a Recoverable Point Before Editing
Create a native HDFS recovery point and compare protected history with later live changes.
Big Data
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
Replace One Approved Day
Replace approved dates in a Hive ORC archive, clear a valid empty day, and preserve every other date and the original source files.
Big Data
Count Logical Votes Rather Than Records
Count Kafka vote identities, reject conflicting retries, and publish a real booth snapshot without changing the source.
Big DataPythonLinux
Count Journeys Rather Than Raw Events
Use Spark to retain earliest session/product views and match valid cart existence without multiplying journeys.
Big Data
Build an Independent Replay Snapshot
Build a bounded Kafka replay across empty partitions and nonconsecutive offsets, then rebuild independent vote snapshots safely.
Big DataLinux
Filter Rows and Handle Missing Values
Use Hive SQL conditions, NULL tests, TRIM, COALESCE, and CASE to turn an imperfect retail sample into a clearly defined review queue without inventing missing monetary values.
Big Data
Follow a File through HDFS Blocks
Connect HDFS files to their underlying blocks, predict block counts from file size, inspect storage locations, and distinguish logical content from replicated storage.
Big Data
Diagnose Common Hadoop Job Problems
Diagnose connection, input, and output-path mistakes, inspect a controlled failed YARN application, and complete a corrected batch without deleting original data.
Big Data
Handle Bad Kafka Events and Observe Redelivery
Observe fail-fast handling of malformed JSON and a controlled write-before-commit exit, then verify repeated delivery through the same Kafka record position.
Big Data
Join Hive Orders with Customer Data
Enrich orders with customer regions, compare INNER and LEFT JOIN on a missing match, and reconcile regional paid-sales totals.
Big Data
Join Spark Orders with Product and Customer Data
Enrich paid orders through unique customer and product keys, compare missing-match join behavior, and reconcile category and regional revenue.
Big Data
Manage the HDFS File Lifecycle
Keep original retail inputs safe while copying, archiving, exporting, measuring, and removing temporary HDFS files. Learn which filesystem each operation changes.
Big DataLinux
Organize Hive Tables with Date Partitions
Connect daily HDFS directories to Hive partition metadata, register a new date explicitly, and inspect a date-filtered query plan.
Big Data
Create and Inspect Kafka Topics
Inspect partitions and replicas, apply topic-specific retention overrides, and verify that changing one topic does not change another.
Big Data
Create an External Table from CSV Files
Map a simple retail CSV to a Hive external table, verify columns and header handling, change the mapped location, and observe data preservation when a disposable external table is dropped.
Big DataLinux
Consume and Save Kafka JSON Events with Python
Poll and decode real Kafka events, append JSONL records with broker positions, flush successful writes before synchronous commits, and test a same-group restart.
Big DataPythonShell
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