Data Ingestion is the crucial first step in any Big Data workflow.
It's the process of moving data from hundreds or thousands of different sources into a
centralized system, like the Hadoop Distributed File System (HDFS), where it can be stored and
analyzed. This section provides a high-level overview of the two main strategies: Batch and
Real-Time.
Batch Ingestion
Data is collected and processed in large, periodic "batches"
(e.g., hourly, daily). This is ideal for high-volume, non-urgent data where
throughput is more important than speed. Think of it like collecting all your mail
for the week and opening it on Saturday.
Key Tool: Sqoop
Use Case: Moving daily sales logs, importing legacy databases.
Pro: High throughput, reliable for massive datasets.
Con: High latency (data is not fresh).
Real-Time (Streaming) Ingestion
Data is collected and processed continuously as it's
generated, often in milliseconds. This is critical for use cases that require
immediate action or insights. Think of it as reading text messages the moment they
arrive.
Key Tools: Flume, Kafka
Use Case: Social media feeds, sensor data, fraud detection.
Pro: Extremely low latency (fresh data).
Con: More complex to manage, can be resource-intensive.
Batch vs. Real-Time: Key
Trade-offs
Batch Ingestion: Apache Sqoop
Apache Sqoop (SQL-to-Hadoop) is the specialized tool for bulk data
transfer between structured data sources (like relational databases) and Hadoop. It is *not*
for streaming data. Its job is to efficiently "scoop" massive tables from a database (like
MySQL or Oracle) and dump them into HDFS or Hive. It does this by creating MapReduce jobs in
the background, parallelizing the data transfer.
How Sqoop Works: The Import Process
Source: Relational Database (e.g., MySQL, Oracle)
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Process: Sqoop Import Command
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Engine: Generates MapReduce Job
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Destination: Hadoop (HDFS, Hive, HBase)
Real-Time Collection: Apache Flume
Apache Flume is a distributed, reliable service for efficiently
collecting, aggregating, and moving large amounts of streaming data, especially log data. It's
designed for real-time collection. Its architecture is based on "Flume Agents," which are
independent processes. Each agent is composed of three key parts: a Source, a Channel, and a
Sink.
Flume Agent Architecture (HTML/CSS
Diagram)
Data
Source (e.g., Web Server Logs)
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Flume Agent
Source (Listens)
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Channel (Buffers)
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Sink (Writes)
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Destination (e.g., HDFS or Kafka)
Streaming Platform: Apache Kafka
Apache Kafka is more than just an ingestion tool; it's a distributed
*streaming platform*. It acts as a massive, fault-tolerant, scalable "buffer" or "central
nervous system" for data. Its key feature is the "publish-subscribe" model. Producers write
data to "Topics," and Consumers read from those "Topics" at their own pace. This *decouples*
your data sources from your data processors, making your entire architecture more resilient and
flexible. Flume is often used to push data *into* Kafka.
Kafka's Publish-Subscribe Model
(HTML/CSS Diagram)
Producer 1 (Web App)
Producer 2 (Flume Agent)
Producer 3 (IoT Device)
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Kafka Cluster (Broker)
Topic A (Clickstream)
Topic B (Log Data)
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Consumer 1 (Spark)
Consumer 2 (HDFS Sink)
Consumer 3 (Analytics DB)
Compare Tools
Evaluating and comparing ecosystem components is a critical skill. No
single tool is "best"; they are designed for different jobs. Sqoop is for batch database
transfers. Flume is for real-time log collection. Kafka is a durable, scalable buffer for
streaming data. Use this chart to compare them across key attributes. Click the buttons to add
or remove tools from the comparison.
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