Hadoop Ecosystem | Hadoop Tutorial for Beginners | Hadoop [Part 9]

Understanding Data Processing in Hadoop

Overview of Virtual Machines and Performance

• Discussion on the types of virtual machines, specifically bare-metal VMs like Hyper-V and VMware, which guarantee better performance compared to non-bare-metal setups.

• Introduction to ETL (Extract, Transform, Load) processes traditionally used for data handling.

Transition from ETL to ELT in Hadoop

• Explanation of why Hadoop favors ELT (Extract, Load, Transform) over traditional ETL due to challenges in transforming large datasets on-the-fly.

• Mention of tools for extracting structured data into Hadoop systems, such as Apache Sqoop.

Using Apache Sqoop for Data Transfer

• Description of Sqoop's role in transferring data from SQL databases like Oracle and MySQL into Hadoop; it is not classified as an ETL tool but rather a data transfer tool.

• Clarification that Sqoop can only handle structured data and cannot process flat files or JSON directly.

Change Data Capture with GoldenGate

• Introduction to the concept of Change Data Capture (CDC), particularly using Oracle's GoldenGate when direct access to core banking databases is restricted.

• Explanation that CDC captures only changes (deltas) in the database without allowing direct interaction with the original database.

Utilizing Apache Flume for Unstructured Data

• Overview of Apache Flume as a tool designed for collecting unstructured data from various sources and delivering it to destinations like HDFS.

• Distinction between Flume’s point-to-point delivery system versus Kafka’s message queue capabilities; Flume does not store data but simply transfers it.

Comparing Flume and Kafka

• Highlighting the differences between Flume's temporary storage model and Kafka's persistent message queuing system that allows multiple consumers access to stored messages.

• Discussion on how Kafka can retain messages for up to seven days by default, enabling more complex analytics scenarios.

Real-Time Data Analysis with Spark

Introduction to Spark Streaming and Real-Time Data Processing

Overview of Spark Streaming

• Spark Streaming is a utility for analyzing real-time data, which may be new to some learners.

Comparison of Flume and Kafka

• Flume operates on a pull-based model, allowing data reading without modifying existing setups, while Kafka requires a producer installation for direct data access.

• In an existing setup, using Flume to send data to Kafka is more feasible than installing Kafka directly.

Data Flow Architecture

• The architecture involves sending one copy of the data to HDFS (Hadoop Distributed File System) and another copy into Spark Streaming for real-time processing.

• Other tools capable of real-time processing include Apache Flink and Apache Storm.

Functionality of Flume and Kafka

• Flume acts like WhatsApp by pushing data but does not store it; in contrast, Kafka stores the data until consumed.

• In the ICICI Bank architecture example, one copy goes to Spark Streaming for processing while another remains in Kafka.

Kafka's Data Management Features

Data Retention Configuration

• Kafka allows configuration of how long data should remain stored (default is seven days), with options based on size limits (e.g., 10 GB or 20 GB).

Topics in Kafka

• Topics in Kafka help manage how long specific datasets are retained within its cluster architecture.

Real-Time Processing Applications

Use Cases for Real-Time Processing

• An example includes sending immediate offers based on customer transactions processed through systems like Facebook clicks.

Importance of Original Data Storage

• The original transaction data must be stored securely while enabling real-time analysis through tools like Splunk or ELK stack.

Integration with Other Tools

Using Scoop with HDFS and Hive

• Scoop can push data primarily into HDFS but can also be configured to send it to Kafka or Hive for further analysis.

Transactional Data Analysis Challenges

Real-Time Data Processing and Fraud Detection

Change Data Capture (CDC) Systems

• The discussion begins with the importance of capturing data using CDC systems, specifically mentioning that Flume is used to collect log files for real-time processing.

• Real-time data processing requires a CDC system to push data out efficiently; examples include Spark Streaming and Flink, which are highlighted as popular frameworks.

Use Case: Credit Card Fraud Detection

• A practical example involving Citibank illustrates how credit card transactions are monitored in real time to detect fraud.

• The speaker emphasizes the volume of transactions processed daily by banks like HDFC, indicating that manual monitoring is impractical.

Transaction Monitoring Process

• All credit card transaction data is collected and sent through systems like Kafka or directly into Spark Streaming or Flink for analysis.

• Logic can be implemented to trigger alerts based on unusual transaction patterns, such as spending outside typical behavior.

Processing Capabilities of Frameworks

• Apache Storm can process individual events (like single transactions), while Spark Streaming operates on micro-batches, collecting several seconds' worth of transactions before processing them.

• Both frameworks aim for real-time processing but differ in their operational methodologies—Storm processes one event at a time while Spark uses micro-batching.

Machine Learning Integration

• To determine if a transaction is fraudulent, historical transaction data must be analyzed. Machine learning models built from this data help predict potential fraud in real time.

• The challenge lies in comparing new transactions against extensive historical datasets without significant delays; machine learning models facilitate this comparison efficiently.

Resource Requirements for Real-Time Processing

• Implementing these systems requires substantial computational resources (CPU and RAM), highlighting the need for robust infrastructure to support machine learning operations.

Batch Processing with Hadoop

Introduction to Batch Processing Frameworks

• Once data is stored in HDFS, batch processing can be performed using MapReduce as the default framework.

Alternative Tools for Batch Processing

• Pig was mentioned as another tool available for batch processing but has fallen out of favor due to the rise of Spark's popularity.

SQL-like Querying with Hive

Hive and Spark: Understanding Batch Processing

Introduction to Hive and MapReduce

• Hive is a tool that simplifies the process of writing queries for data processing, allowing developers to create tables and run complex queries without deep technical knowledge.

• MapReduce is a batch processing system that can take significant time to execute queries; for example, a query in Flipkart took around 12 hours to run.

Spark as an Alternative

• Spark is introduced as an in-memory execution engine that offers faster performance compared to traditional MapReduce systems, classified as batch processing but capable of near real-time execution.

• While Spark provides speed advantages, it operates differently from Hive, which remains relevant as a data warehouse for table creation and data storage.

Integration of Spark with Hive

• Spark can connect with Hive using its SQL library, enabling users to read tables from Hive and perform operations on them efficiently.

• For large group-by queries requiring full table scans, both Hive and Spark are effective. However, for smaller queries targeting specific rows, loading entire datasets may be inefficient.

MPP Engines: Impala and Others

• Multiple MPP (Massively Parallel Processing) engines like Impala, Hawk, Presto, and Phoenix are discussed as alternatives that optimize query performance by scanning only necessary data.

• Impala executes SQL queries directly without relying on MapReduce; it connects with HDFS to retrieve only the required metadata for efficient querying.

Reliability vs. Speed

• A key distinction between Impala and Hive lies in reliability; while Impala offers faster query execution times (e.g., two minutes), it lacks fault tolerance—if a machine crashes during an Impala query execution, the job fails.

• In contrast, Hive's reliance on MapReduce ensures built-in redundancy; even if one machine fails during processing, the job will complete successfully.

The Role of HBase

• HBase is identified as Hadoop's NoSQL database designed for real-time random reads/writes rather than block-level access typical in HDFS.

• Although HBase allows individual record manipulation through its API, it requires learning its specific language instead of standard SQL.

Phoenix: Bridging SQL with HBase

• Phoenix serves as an interface allowing users to write SQL queries on top of HBase. This combination enhances usability by providing familiar syntax over non-SQL databases.

• Despite being fast due to inherent optimizations within HBase itself, Phoenix is still in development stages (alpha/beta), aiming towards becoming production-ready with support for transaction management.

Hadoop and Its Integration with Phoenix

Overview of Hadoop and Phoenix Integration

• The integration of Phoenix with Hadoop is discussed, highlighting that originally HBase lacked SQL support. Phoenix serves as a layer on top of HBase to enable SQL capabilities.

• There are ongoing developments aimed at enhancing transactional management within the system, promising true transactional capabilities in the near future.

Commercial Distributions vs. Apache Downloads

• When acquiring products from Cloudera, users receive a comprehensive package that includes various tools, unlike downloading from Apache where only basic components like HDFS and MapReduce are available.

• The commercial distributions provide an integrated learning experience by packaging multiple tools together for user convenience.

Tools and Technologies in Cloudera's Offerings

• A variety of technologies such as Spark, Impala, Presto, Drill, and Kafka are included in Cloudera’s offerings alongside traditional components like HDFS and MapReduce.

• Newer technologies like Kudu (a relational file system) are mentioned; however, there is limited knowledge about them among users.

Security Features in Hadoop Ecosystem

• Concerns regarding security within the Hadoop ecosystem are addressed through the introduction of Sentry, which manages access control and authentication processes effectively.