Big Data Integration and Orchestration

Two chores glue a big data stack together. Integration combines data from many sources into a unified, consistent view (data lakes, ETL/ELT pipelines, analytics inputs). Orchestration automates, sequences, schedules, and monitors the jobs that move and transform that data so each step runs in the right order and failures are visible. The tools below split along that line, and each fits a layer of the architecture in Big Data Fundamentals and Architecture.

ETL (Extract, Transform, Load) cleans data before loading it into a warehouse. ELT loads raw data first and transforms it inside the destination, which suits cheap lake storage and powerful engines such as Apache Spark.

flowchart LR
    SRC["Sources<br/>databases, logs, IoT"] --> ING["Ingest<br/>Sqoop, Flume, NiFi, Kafka"]
    ING --> LAKE["Lake / HDFS / S3"]
    LAKE --> TR["Transform<br/>Spark, Hive"]
    TR --> WH["Warehouse / serving store"]
    ORC["Orchestrator<br/>Airflow, Oozie"] -. "schedules and monitors" .-> ING
    ORC -.-> TR

Tools

Tool	Category	What it does	Typical use
Apache NiFi	Integration, ingestion	Visual drag-and-drop flow design for routing, transforming, and mediating data between systems, with built-in back-pressure and provenance tracking	Stream sensor data from IoT devices to a cloud analytics system
Apache Kafka	Integration, streaming	Distributed event log connecting producers and consumers (Stream Processing and Event Streaming)	Stream website clickstream data to an analytics engine
Apache Flume	Ingestion	Distributed service that collects, aggregates, and ships large volumes of log data, mainly into HDFS or cloud storage	Collect logs from many web servers in near real time
Apache Sqoop	Ingestion	Bulk transfer between relational databases and Hadoop	Import a SQL table into HDFS (now retired, but still seen in legacy stacks)
Apache Airflow	Orchestration	Define workflows as Python code using DAGs (directed acyclic graphs) of tasks with dependencies, schedules, retries, and a monitoring UI	Daily pipeline that extracts, transforms, and loads transactions into a warehouse
Apache Oozie	Orchestration	Workflow scheduler for Hadoop jobs (MapReduce, Spark, Hive, Pig)	Periodic batch jobs on a Hadoop cluster (Hadoop and MapReduce)

Airflow in a Few Lines

A DAG declares what runs and in what order; Airflow handles scheduling, retries, and backfills.

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
 
with DAG("daily_sales", start_date=datetime(2026, 1, 1), schedule="@daily") as dag:
    extract   = PythonOperator(task_id="extract",   python_callable=extract_fn)
    transform = PythonOperator(task_id="transform", python_callable=transform_fn)
    load      = PythonOperator(task_id="load",      python_callable=load_fn)
    extract >> transform >> load

Source: Apache Airflow documentation.

Which One to Pick

Question	Answer
Move and transform data in real time with a GUI?	NiFi
Stream events between many systems with replay?	Kafka
Collect log files into storage?	Flume
Schedule complex multi-step batch workflows?	Airflow
Schedule jobs inside a Hadoop-only shop?	Oozie

Integration tools move bytes; orchestrators decide when and in what order. Production stacks usually combine one of each, for example NiFi or Kafka for ingestion and Airflow to schedule the Spark jobs that follow.