Introduction to Hadoop | Hadoop Tutorial for Beginners | Hadoop [Part 3]

Hadoop and MapReduce Overview

Introduction to Hadoop and MapReduce

• The session begins with an introduction to Hadoop, focusing on its architecture and the programming framework known as MapReduce.

• While MapReduce is a traditional method for data analysis in Hadoop, there are now various other ways to analyze data beyond just this framework.

Learning MapReduce

• To effectively learn MapReduce, familiarity with Java is beneficial since most programs are written in this language. However, alternatives like Python exist.

• Understanding the logic behind MapReduce is crucial; students need not grasp every line of code but should comprehend how it functions conceptually.

Transitioning from Hadoop to Spark

• After covering Hadoop architecture and theory, the course will transition into Spark, which shares similar concepts with MapReduce.

• Students will engage in practical exercises involving running MapReduce programs while developing a foundational understanding of their operations.

History and Development of Hadoop

Origins of Hadoop

• The instructor discusses the historical context of Hadoop's development, noting that it was created in 2005 by Doug Cutting and Mike Cafarella based on ideas from Google’s distributed computing model.

• Despite being over 13 years old, many still perceive Hadoop as a new technology due to its complexity.

Open Source Nature of Hadoop

• As an open-source project under Apache, anyone can download and install Hadoop for free. However, this comes with potential drawbacks regarding stability and support.

Commercial Support for Hadoop

• The instructor compares open-source software (like Android phones prone to bugs due to multiple modifications by manufacturers) with proprietary systems (like Apple), emphasizing the lack of guaranteed support for open-source projects like Apache Hadoop.

Commercial Distributions: Cloudera

Cloudera's Role in Big Data

• Cloudera emerged as a significant player by providing commercial distributions of Hadoop along with support services for businesses needing reliable solutions.

Advantages of Using Cloudera

• Users can obtain a version similar to Apache’s distribution but receive technical support when issues arise if they opt for paid services from Cloudera.

Free vs Paid Versions

• Cloudera offers both free versions without support and paid options that include customer service. This model allows users flexibility depending on their needs.

Big Data Certification and Vendor Landscape

Overview of Big Data Vendors

• Companies like Hortonworks and Cloudera are leading vendors in the Big Data space, providing essential technical support and certification exams for professionals.

• Mapper is another notable company that emerged from former employees of Cloudera and Hortonworks, gaining popularity for its unique architecture distinct from traditional Hadoop.

Popularity and Usage of Platforms

• Major companies such as GE and Flipkart predominantly use Hortonworks or Cloudera, with Mapper being less common in enterprise settings.

• IBM offers its own version called BigInsights, while Microsoft has HDInsight; however, Hortonworks, Cloudera, and Mapper remain the most popular choices among developers.

Differences Between Platforms

• While MapReduce programs run similarly across platforms, the main distinction lies in how each vendor modifies Hadoop. Hortonworks claims to enhance stability through their modifications.

• The analogy of Android devices illustrates that despite different brands (Samsung vs. HTC), they all operate on the same underlying system—similar to how various Hadoop distributions function.

Learning MapReduce

• Understanding MapReduce is crucial for transitioning to Spark since many existing programs rely on it. This foundational knowledge aids in grasping Spark's concepts more effectively.

• Despite a decline in MapReduce usage within enterprises, knowing its principles remains relevant due to its influence on modern data processing frameworks.

Performance Considerations

• Hadoop can serve as a data warehouse but may not meet performance needs for real-time reporting compared to systems like Teradata or Netezza.

• Organizations often utilize cloud solutions like Amazon Web Services (AWS), which provide fully managed Hadoop clusters quickly through services like Redshift for data warehousing.

Challenges in Data Solutions

• There are trade-offs between speed and real-time access when designing data solutions; achieving both simultaneously can be challenging.

Understanding Network Attached Storage and Storage Area Networks

Overview of Network Attached Storage (NAS)

• The concept of Network Attached Storage (NAS) is introduced, highlighting its popularity and functionality. NAS allows users to buy a box that can be installed anywhere for data storage.

• EMC and MC Square are mentioned as companies that have surveyed the market regarding NAS technology, indicating its significance in data management.

Characteristics of NAS

• NAS operates independently from the machine itself, providing a dedicated solution for data storage outside of traditional computing systems.

• The discussion transitions to Storage Area Networks (SAN), which are characterized by large rooms filled with hard disks connected via fiber channel, allowing extensive data storage capabilities.

Comparison Between NAS and SAN

• While both NAS and SAN serve the purpose of data storage, they differ in architecture; SAN offers more robust solutions for larger scale operations compared to typical NAS setups.