Big Data Technologies

Big Data Technologies refer to a set of computational technologies designed to manage, process, and analyse large and complex data sets that are too vast for traditional database systems.

The components of a Big Data Technology Stack are: Data Storage, Data Processing, Data Analysis, Data Visualization.

Data Storage involves storage of large data sets, Data Processing employs technologies for swift data processing, Data Analysis is where the processed data is analysed, and Data Visualization displays the analysed data visually.

In Healthcare, they manage medical records and inform patient care. In Banking, they detect fraudulent transactions, and in Retail, they recommend products to customers through analysis of their previous purchases.

The benefits include increased employability due to high demand for big data professionals, enhancement of problem-solving skills, applicability in various domains, lucrative salaries, versatility, and practical skill enhancement.

Big data helps in predictive modelling in healthcare, detecting fraudulent activity in banking, creating personalised shopping experiences in retail, and planning smarter routes to reduce congestion in transport services.

They assist in identifying disease outbreaks, detecting fraudulent banking transactions, offering personalised shopping experiences, and predicting traffic situations for smarter route planning.

In healthcare, it is used to diagnose diseases early; in banking, to predict customer behaviour; in e-commerce, for personalised shopping experiences; and in entertainment, for personalising recommendations.

Databases that are capable of storing, processing, and analysing massive volumes of unstructured or structured data.

The development of the MapReduce algorithm and Google File System by Google, which laid the groundwork for handling large datasets.

Strength is their scalability and flexibility in handling various data types. Weakness is their potential lack of data consistency and immature tooling.

The type of data you're working with, the speed of processing required, and the nature of your data operations.

The core skills include programming (Python, R, or Java), database technologies (SQL and NoSQL), machine learning basics, statistical analysis, and data visualisation techniques.

The path starts with understanding the basic concepts of Big Data, such as the 3 Vs – volume, velocity, and variety, and learning a programming language like Python.

Websites like Coursera, Udemy, Khan Academy, DataCamp, and Pluralsight offer courses. Specific resources include 'Automate the Boring Stuff with Python', 'SQL for Data Science', 'Intro to NoSQL Data Solutions', 'Statistics and probability', and 'Data Visualisation with Python and Matplotlib'.

Start with understanding the basic concepts of Big Data and a programming language like Python. Next, get comfortable with database technologies, start with SQL and then NoSQL. Build a base in statistics and move towards machine learning. Finally, learn data visualisation techniques.

Hadoop is an open-source, Java-based programming framework that supports the processing and storage of incredibly large data sets in a distributed computing environment. It enables businesses to collect, process and analyse data that was once considered too big or complex to handle.

The key characteristics of Apache Hadoop are scalability, cost effectiveness, flexibility, fault tolerance, and high throughput. These enable it to handle and process vast amounts of data efficiently.

Hadoop primarily consists of four components: Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop YARN, and Hadoop MapReduce. These components work collaboratively to process and store large data sets.

Hadoop's core idea is to divide and conquer. It distributes tasks across multiple nodes, each working with a manageable chunk of data, improving processing times and system resilience for big data.

The main components are Hadoop Common (libraries and utilities), Hadoop Distributed File System (HDFS), Hadoop YARN (resource allocation and task scheduling), and Hadoop MapReduce (for large scale data processing).

Amazon uses Hadoop-based service Elastic MapReduce (EMR) to analyse raw data. Netflix uses Hadoop to analyse customer viewing patterns and preferences. eBay applies Hadoop for search optimization, research, and fraud detection.

HDFS is the primary storage mechanism of Hadoop, designed to handle large volumes of data across multiple nodes in a cluster. It splits data into chunks and distributes them for parallel processing.

Hive enables data summarisation, querying and analysis using a SQL-like interface, translating queries into MapReduce jobs. Pig allows the creation of MapReduce programs using a simple scripting language, specifically designed for large datasets.

Initially, data is processed by MapReduce and YARN; MapReduce writes applications to process data in parallel, while YARN manages resources and schedules tasks. Afterwards, Hive and Pig analyse and query the processed data.

Hadoop's core aim is to simplify handling big data, making it more manageable and productive. It focuses on enabling businesses to extract valuable insights from their large data sets and convert them into actionable intelligence through efficient data storage and robust security measures.

HDFS manages data storage by splitting data into blocks and storing these blocks across multiple nodes in a distributed manner. It also replicates the blocks on various nodes for fault tolerance. This distributed storage approach enhances Hadoop's scalability, reliability, and speed of data handling.

Hadoop ensures data security through authentication using Kerberos, authorization tools like Hadoop Access Control Lists and Apache Ranger, data encryption with HDFS offering end-to-end encryption, and auditing through tools like Apache Ranger and Cloudera Manager.

The two main components of Hadoop's architecture are the Hadoop Distributed File System (HDFS) for data storage and MapReduce for data processing.

A Hadoop Cluster refers to a computational cluster designed specifically for storing and analysing vast amounts of data in a distributed computing environment. It is composed of multiple nodes working in tandem.

MapReduce in Hadoop architecture is a model that splits large datasets into blocks distributed across cluster nodes. Each block is processed to give a result set in a key-value pair format, where the key acts as a unique identifier for the value.

Scalability in Hadoop refers to its ability to handle increasing volumes of data by simply adding nodes to the existing cluster. Hadoop was specifically designed for horizontal scalability, which is beneficial for distributed computing.

Scalability in Hadoop is achieved via distributed processing and storage through HDFS and MapReduce systems. As new nodes are added to a cluster, each node shares responsibility for storing and processing data. Data files are split into blocks and distributed across nodes, and MapReduce enables parallel processing.

Implementing scalability with a Hadoop cluster involves refining storage with HDFS and data processing with MapReduce. As more data nodes are added, Hadoop starts automatically utilising them, with the NameNode allocating data to them and MapReduce assigning processing tasks.

Apache Spark is an open-source distributed general-purpose cluster-computing framework used for processing large volumes of data. It supports diverse workloads such as real-time data streaming, machine learning, and graph processing.

The key features of Apache Spark include its speed via in-memory processing, integration with Hadoop data repositories, support for multiple programming languages, and built-in APIs for machine learning, graph processing, and stream processing.

Apache Spark is crucial for Big Data Processing due to its speed, versatility, and ease of use. It can process large volumes of data faster than other tools, supports numerous tasks, and provides high-level APIs for various users.

Apache Spark's main role in Big Data is to process and analyse large data sets at high speed using Resilient Distributed Datasets (RDDs). It also reduces the complexity of large-scale data processing by providing high-level APIs in multiple programming languages.

Apache Spark enhances Big Data execution through its speedy in-memory processing, real-time data processing capability, fault tolerance, and code execution enhancement with its Catalyst Optimizer.

The benefits include speed due to distributed in-memory data storage, multifunctionality, seamless integration with Hadoop, Hive, and HBase, availability of analytics tools like MLlib and GraphX, and a robust community support.

Apache Spark is significant in Big Data analytics due to its capacity for real-time data processing and its ability to handle large datasets too complex for traditional data processing software. It offers an API supporting general execution graphs for various data tasks, has a powerful machine learning library, and enables integration and transformation of large data volumes from various sources.

Key steps include: launching Apache Spark, loading the data to analyze, preparing and cleaning the data, performing data analytics operations, and visualizing and interpreting the results. These steps can be followed in multiple languages like Python, Scala, and R.

Spark offers in-memory execution for faster data processing, allows both real-time and batch processing, adheres to the immutability principle enhancing security and debugging, and has powerful machine learning and AI capabilities through its MLlib, making it an effective tool for Big Data analytics.

Finance, healthcare, e-commerce, and telecommunications are sectors where Apache Spark Big Data is used. It processes vast amounts of data for fraud detection, personalized treatment plans, recommendation systems, and customer churn prediction respectively.

Spark's functionalities are used for data querying, real-time data streaming, machine learning, and graph processing. They extract specific information from datasets, detecting real-time anomalies, enabling predictive analytics, and analyzing data point relationships respectively.

Uber, Netflix, and Alibaba have effectively used Spark Big Data. Uber uses it for real-time pricing and ETA predictions, Netflix for personalized movie recommendations, and Alibaba for personalized recommendation systems and online advertising.

The two types of cluster nodes in Spark's architecture are the Driver node, which runs the main() function of the program and creates SparkContext, and the Worker node, which executes tasks assigned by the driver node.

Apache Spark's architecture offers speed due to in-memory data storage, is easy to use with user-friendly APIs, has flexibility to process different types of data, and is highly scalable, supporting the distribution of thousands of tasks.

The primary components of Apache Spark's architecture are Spark Core for essential functionalities, Spark SQL for structured and semi-structured data, Spark Streaming for processing of live data streams, MLlib (Machine Learning Library), and GraphX for graph manipulation.

Stream Processing is a computing method that involves the real-time ingestion and analysis of data as it is generated. The data often takes the form of continuous streams that flow into the system.