Big Data Platforms, Tools and Techniques

- Overview

A big data platform refers to a system of software and hardware designed to efficiently manage and process large volumes of complex data (big data), including storage, processing, analysis, and visualization capabilities, allowing organizations to extract valuable insights from their data.

Big data tools are specific applications within this platform used to perform tasks like data ingestion, cleaning, transformation, and analysis, while big data techniques are the algorithms and methodologies applied to analyze and interpret large datasets, often involving machine learning (ML) and statistical methods.

When there is a need to manage large amounts of data and perform complex operations on this massive data, it is imperative to use big data tools and technologies. When we say use big data tools and technologies, we are referring to the big data ecosystem and its domain.

No single solution will work for all use cases, it needs and must be created and crafted in an efficient manner as per the company needs.

Big data solutions must be developed and maintained as per the company needs to meet the company's requirements. Stable big data solutions can be built and maintained so that they can be used for the problems required.

- Big Data Tools

Big data tools are used to process, analyze, and extract data, and can help businesses discover patterns and insights. They can be used for: Predictive analytics, Visualization, Statistical computing, Workflow automation, and Cluster and container management. 

Some examples of big data tools include:

• Tableau Public: A free, interactive online platform that allows users to create and share visualizations and data-driven stories
• MongoDB: An open-source NoSQL database that provides cross-platform capabilities
• Apache Kafka: A key component in big data architectures that enables real-time data streaming and processing
• Cloudera: A big data platform that offers tools and services for managing and analyzing large volumes of data
• Apache Flink: An open-source big data technology that allows for the examination and processing of data streams in real time
• Talend: An open-source data integration and management platform that focuses on big data
• Power BI: A big data analytics tool that allows users to integrate data from diverse sources to create insights 
• Qubole: A cloud-native big data platform that uses open-source technology for big data analytics

When choosing a big data tool, it's important to consider the type of big data technology required, such as data storage, data mining, data analytics, or data visualization.

- Big Data Techniques

Big data techniques refer to the methods and technologies used to process and analyze large volumes of data (structured, semi-structured, and unstructured) including tools like machine learning, data mining, statistical analysis, and specialized software platforms like Hadoop, to extract meaningful insights and patterns from vast datasets; essentially, it's the approach to handle and analyze "big data" effectively.

Key implementation process of big data techniques: 

• Data collection: Gathering data from diverse sources, including social media, sensors, web logs, and transactional systems.
• Data storage: Utilizing distributed storage systems like Hadoop Distributed File System (HDFS) to manage large data volumes across multiple servers.
• Data processing: Employing parallel processing frameworks like Apache Spark to efficiently analyze large datasets in real-time.
• Data cleaning and transformation: Preprocessing data to ensure quality and consistency before analysis.
• Data mining: Discovering patterns and relationships within the data using statistical and machine learning algorithms.

Some common big data techniques include:

• Machine learning: Using algorithms to learn from data and make predictions on new data points
• Deep learning: A subset of machine learning that utilizes artificial neural networks with multiple layers for complex data analysis
• Association rule learning: Identifying relationships between items in a dataset
• Classification analysis: Categorizing data into predefined classes
• Regression analysis: Predicting continuous values based on relationships with other variables
• Sentiment analysis: Analyzing text to determine the sentiment (positive, negative, neutral) expressed
• Social network analysis: Studying connections and relationships within social networks