Module 01: Introduction to Big Data & Hadoop
Q1: What is Big Data, and why has it gained importance in recent years?
A1: Big Data refers to extremely large and complex datasets that traditional data processing software cannot adequately handle. It has gained importance due to:
- The exponential growth of data generated from various sources such as social media, IoT devices, and enterprise systems.
- The need for businesses to harness data for insights, decision-making, and competitive advantage.
- Advances in technology that enable the storage, processing, and analysis of large datasets efficiently, leading to better predictions and enhanced customer experiences.
Q2: Explain the main characteristics of Big Data (the 3Vs).
A2: The three main characteristics of Big Data are:
- Volume: Refers to the vast amounts of data generated every second. For example, social media platforms generate terabytes of data daily.
- Velocity: The speed at which data is created and processed. Businesses require real-time processing to derive insights and make decisions quickly.
- Variety: The different forms of data, including structured (databases), semi-structured (XML, JSON), and unstructured data (text, images, videos).
Q3: How does the traditional approach to business differ from a Big Data business approach?
A3: Traditional business approaches focus on historical data analysis using structured databases, often with predefined queries. In contrast, Big Data approaches emphasize real-time processing of diverse data types, utilizing machine learning and analytics for deeper insights. This shift allows companies to be more agile and responsive to market changes.
Q4: Can you describe a real-life Big Data case study?
A4: One notable case study is Netflix. It uses Big Data analytics to analyze user viewing patterns and preferences, leading to personalized content recommendations. This data-driven approach helps increase user engagement and retention, ultimately impacting their content investment decisions.
Q5: What is Hadoop, and what problem does it solve?
A5: Hadoop is an open-source framework that allows for the distributed storage and processing of large datasets across clusters of computers. It solves the problem of scalability and fault tolerance in handling Big Data, enabling organizations to process and analyze vast amounts of data efficiently.
Q6: What are the core components of Hadoop?
A6: The core components of Hadoop include:
- Hadoop Distributed File System (HDFS): A distributed file system that stores data across multiple nodes.
- MapReduce: A programming model for processing large datasets in a parallel and distributed manner.
- YARN (Yet Another Resource Negotiator): A resource management layer that schedules jobs and manages resources across the cluster.
Q7: What is the Hadoop ecosystem, and which tools are included?
A7: The Hadoop ecosystem consists of various tools and frameworks that work alongside Hadoop to facilitate data storage, processing, and analysis. Key tools include:
- Hive: A data warehousing tool that provides SQL-like querying capabilities.
- Pig: A high-level platform for creating programs that run on MapReduce.
- HBase: A distributed NoSQL database built on top of HDFS.
- Zookeeper: A coordination service for distributed applications.
- Oozie: A workflow scheduler for managing Hadoop jobs.
Module 02: Hadoop HDFS and MapReduce
Q8: Explain how Hadoop HDFS works.
A8: HDFS is designed to store large files by dividing them into smaller blocks (typically 128 MB) and distributing them across multiple nodes in a cluster. Each block is replicated (usually three times) to ensure fault tolerance. The NameNode manages metadata and the location of each block, while DataNodes store the actual data blocks. This architecture allows for high throughput and scalability.
Q9: How does Hadoop handle node failures in HDFS?
A9: HDFS is built with fault tolerance in mind. When a DataNode fails, the NameNode detects the failure and re-replicates the data blocks from the surviving replicas to ensure that the required number of replicas is maintained. This allows for continuous data availability and reliability.
Q10: What are the roles of the NameNode and DataNode in HDFS?
A10:
- NameNode: Acts as the master server that manages the metadata of the filesystem, including the namespace, permissions, and the mapping of blocks to DataNodes. It does not store data itself.
- DataNode: These are worker nodes that store the actual data blocks. They periodically send heartbeat signals to the NameNode to confirm their availability and report the status of the blocks they store.
Q11: Explain the concept of MapReduce.
A11: MapReduce is a programming model used for processing large datasets in a distributed manner. It consists of two main functions:
- Map: The input data is divided into smaller chunks, and the Map function processes each chunk, producing a set of key-value pairs.
- Reduce: The Reduce function takes the key-value pairs produced by the Map phase, groups them by key, and performs aggregation or summarization to produce the final output.
Q12: What is the role of combiners in the MapReduce framework?
A12: Combiners are optional functions that act as mini-reducers that execute after the Map phase and before the Reduce phase. Their role is to combine intermediate outputs locally on each mapper to minimize data transfer across the network, thus improving performance by reducing the amount of data that needs to be sent to the reducers.
Q13: How does MapReduce handle large-scale matrix-vector multiplication?
A13: In a MapReduce framework, matrix-vector multiplication can be handled by splitting the matrix into rows. Each map task processes one row and calculates the product with the vector. The reduce phase aggregates the results from all map tasks to form the final output vector.
Q14: What are the limitations of Hadoop and MapReduce?
A14: Limitations include:
- Batch processing: Not suitable for real-time data processing or low-latency applications.
- High latency: Due to disk I/O and the overhead of managing distributed tasks, MapReduce can be slower for certain operations.
- Complexity: Writing and debugging MapReduce jobs can be challenging, especially for users unfamiliar with the paradigm.
Module 03: NoSQL
Q15: What is NoSQL, and why was it developed?
A15: NoSQL refers to a category of database management systems that are designed to handle large volumes of unstructured or semi-structured data. It was developed to address the limitations of traditional relational databases, particularly in handling Big Data, providing flexibility, scalability, and high performance.
Q16: What are the business drivers behind NoSQL adoption?
A16: Key business drivers include:
- The need to manage large volumes of unstructured data generated by various sources.
- The requirement for high availability and scalability to accommodate growing data and user demands.
- The ability to provide real-time analytics and insights from diverse data types.
Q17: Explain the key-value store architecture.
A17: In a key-value store, data is stored as a collection of key-value pairs. Each key is unique and is used to retrieve its corresponding value, which can be anything from a simple string to complex objects. This architecture provides high-speed data access and is suitable for applications that require quick lookups. Examples include Redis and Amazon DynamoDB.
Q18: What is the difference between a document store and a graph store?
A18:
- Document Store: Stores data in documents, typically in formats like JSON or BSON. Each document can have a varying structure, allowing for flexibility. Examples include MongoDB and CouchDB.
- Graph Store: Designed to represent and analyze relationships between data points. Data is stored as nodes (entities) and edges (relationships), making it suitable for applications like social networks. Examples include Neo4j and ArangoDB.
Q19: Explain how NoSQL handles distribution and replication.
A19: NoSQL databases use various distribution models, including master-slave and peer-to-peer architectures, to distribute data across multiple nodes. Replication ensures fault tolerance by creating copies of data on different nodes, allowing for data availability even if some nodes fail. This distributed approach enhances scalability and performance.
Module 04: Mining Data Streams
Q20: What is a data stream, and how is it different from traditional static data?
A20: A data stream consists of a continuous flow of data generated over time, such as sensor readings or social media feeds. Unlike traditional static data, which is fixed and can be queried after being collected, data streams require real-time processing and analysis to derive insights as the data flows in.
Q21: What is a Bloom Filter, and how is it used in stream processing?
A21: A Bloom Filter is a probabilistic data structure that efficiently tests whether an element is part of a set. It uses multiple hash functions to set bits in a fixed-size bit array. In stream processing, Bloom Filters are used to quickly check for duplicates or filter out non-relevant data, minimizing storage requirements and improving processing speed.
Q22: Explain the Flajolet-Martin Algorithm and its purpose.
A22: The Flajolet-Martin Algorithm is a probabilistic algorithm used to estimate the number of distinct elements in a data stream. It uses hash functions to map each element to a bit string and counts the position of the leftmost 1 bit across all hashed values. This estimation is efficient in terms of memory usage, making it suitable for large streams.
Q23:
How does the Datar-Gionis-Indyk-Motwani (DGIM) algorithm work?
A23: The DGIM algorithm is used to estimate the number of ones in a sliding window of a data stream. It maintains a compact representation of the count using a combination of bits in buckets. Each bucket corresponds to a specific time interval, allowing for efficient updates and queries on the count while minimizing memory usage.
Module 05: Finding Similar Items and Clustering
Q24: Define distance measures and provide examples.
A24: Distance measures quantify how similar or dissimilar two data points are. Common examples include:
- Euclidean Distance: Measures the straight-line distance between two points in Euclidean space.
- Jaccard Distance: Measures similarity between two sets by comparing the size of their intersection to their union.
- Cosine Distance: Measures the cosine of the angle between two vectors, often used in text analysis.
- Edit Distance: Measures the minimum number of edits required to transform one string into another, commonly used in natural language processing.
Q25: Describe the CURE algorithm for clustering.
A25: The CURE (Clustering Using REpresentatives) algorithm identifies clusters in a dataset by selecting multiple representative points from each cluster. It combines both the geometric and statistical properties of the data points, allowing it to handle clusters of various shapes and sizes effectively. CURE can work with both hierarchical and partitional clustering.
Q26: How does the Stream-Clustering Algorithm work?
A26: Stream-Clustering Algorithms process data in real-time and maintain a summary of the data stream rather than storing the entire dataset. They use techniques like clustering small batches of incoming data and merging clusters over time to adapt to changes in data distribution, ensuring efficient clustering with limited memory usage.
Module 06: Real-Time Big Data Models
Q27: What is PageRank, and how is it computed?
A27: PageRank is an algorithm used to rank web pages in search engine results based on their importance. It calculates the importance of a page based on the number and quality of links pointing to it. The algorithm operates on the principle that more important pages are likely to receive more links from other pages.
Q28: Explain how MapReduce can be used to compute PageRank.
A28: PageRank can be computed using MapReduce by breaking the process into iterative steps:
- Map Phase: Each page emits its rank to all pages it links to.
- Reduce Phase: Each page sums up the ranks it received and applies the damping factor to update its PageRank.
- This process is repeated until the PageRanks converge to stable values.
Q29: How can collaborative filtering be implemented in a recommendation system?
A29: Collaborative filtering recommends items based on the preferences of similar users. It can be implemented using:
- User-based filtering: Identifies users with similar tastes and recommends items they liked.
- Item-based filtering: Recommends items similar to those a user has liked in the past. Both approaches can be implemented using matrix factorization techniques to derive latent factors representing users and items.
Q30: Discuss the role of social networks in graph analysis.
A30: Social networks can be represented as graphs, where nodes represent users, and edges represent relationships or interactions. Graph analysis in social networks helps identify communities, influential users, and patterns of information flow. Techniques like clustering and centrality measures are applied to understand user behavior and enhance engagement strategies.
