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Home - Lessons - Hadoop and Big Data Engineering Certification Training - M1 — The Hadoop Stack as a System - A small cluster on local hosts and the configuration files that set it up

Hadoop and Big Data Engineering Certification Training A small cluster on local hosts and the configuration files that set it up

Hadoop and Big Data Engineering Certification Training

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M1 — The Hadoop Stack as a System

HDFS, YARN, MapReduce and the layer each one adds to the platform

The master and worker model and where the single point of failure sits

A small cluster on local hosts and the configuration files that set it up

The data flow from an edge node to a file on the distributed filesystem

ZooKeeper coordination: leader election, failover and watchers

Where the open-source stack ends and a vendor distribution begins

M2 — HDFS: Blocks, Replication and the NameNode

Block size, replication factor and the block report heartbeat

The NameNode namespace, the fsimage and the edit log

Safe mode, checkpoints and reading fsck output on a damaged block

Reading and writing with the command line client and with Java

The write pipeline as a record travels through several nodes to disk

Adding a data node, decommissioning one and re-replicating its data

M3 — YARN: Resource Management and Containers

The resource manager, the node manager and the application master

How a job is queued, scheduled and assigned to a container

Container memory and CPU limits and sizing the map and reduce stages

Reading the application timeline and the container logs

Queue configuration, capacity and the preemption a backlog causes

Why an over-allocated map stage stalls an entire pipeline

M4 — MapReduce and the Move to Spark

The map, shuffle, sort and reduce stages and the role of combiners

Key, value and value types and the serialisation that carries them

Counters, job history and how a single slow task shows up

The Spark session, the DataFrame and lazy evaluation against MapReduce

Partitions, shuffles and the narrow and wide dependency boundary

Choosing batch, Spark SQL or streaming for one workload

M5 — Hive: Tables, Partitions and ORC

External and managed tables and what the filesystem holds afterwards

Partitioning by date and bucketing for directories too numerous to list

The metastore, the table definition and the schema on disk

ORC and Parquet files, compression and the columnar read advantage

SerDe, the data file format and the layout the reader expects

ACID transactions and the compaction that keeps small files in check

M6 — HBase, Sqoop and Flume

The HBase table, row key, column family and the region split

Row key design for a random read pattern and for a scan pattern

The HBase write path, the memstore and the region server flush

Sqoop import from a relational table with a parallel extract

Sqoop incremental import driven by a checkpoint column

Flume agents, sources, channels and sinks for a log stream

M7 — Schedulers, Pipelines and the Certification Tracks

Airflow DAGs, operators and sensors for an orchestrated pipeline

Backfill strategy, idempotence and a failed task recovery plan

Data quality checks that stop a bad file before it reaches the table

HDPC and the HDP administrator objectives mapped onto the stack

The Cloudera data engineer exam objectives and its hands-on tasks

The HDInsight path in the Microsoft data engineer role and its lab setup

M8 — Capstone: Build a Hadoop Pipeline on a Small Cluster

Stand up a five-node cluster with HDFS, YARN and a metastore service

Ingest a relational extract with Sqoop and a log stream with Flume

Model the data in Hive with partitions and an ORC layout

Write a Spark job that joins and aggregates the two sources

Write a second pipeline job and schedule it with the orchestrator

Document the cluster, the capacity plan and the exam revision plan

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