
There are four fields through which all emerging big data technologies can be divided, and these are Data Mining, Storage, Analytics, and Visualization.
Each of these big data methods comes with independent capabilities. You need to know each of these categories since they come with tools that accommodate only specific types of businesses. Before implementing any of the methods, make a big data tools comparison to choose the ones that work best for your business.
Data Storage
This type of big data technology comprises infrastructure designed to fetch, store, and manage big data. Data is organized in a manner that allows easy access, usage, and processing by various programs.
Managing and storing large volumes of structured, unstructured, and semi-structured data is crucial in the Big Data landscape. Technologies in this category include:
- Hadoop Distributed File System (HDFS): A core component of the Hadoop ecosystem, HDFS is designed to store large data files by splitting them into blocks and distributing them across a cluster of computers. It ensures data redundancy and reliability.
- NoSQL Databases: These databases handle unstructured and semi-structured data more efficiently than traditional relational databases. Popular NoSQL databases include MongoDB, Cassandra, Redis, and Couchbase. They provide high scalability and flexibility for managing diverse data formats.
- Apache HBase: A distributed, scalable NoSQL database that runs on top of HDFS. It is suitable for real-time read/write access to large datasets and is commonly used for applications requiring random, quick reads and writes.
- Amazon S3 and Azure Blob Storage: Cloud-based storage solutions that enable scalable, reliable data storage for organizations. They provide cost-effective ways to store vast amounts of data with high availability and durability.
Data Processing and Computing Frameworks
Data mining is the extraction of valuable information from raw data. Usually, this data is in large volumes, with high variability and streaming at tremendous velocity (conditions that make extraction without a unique technology impossible). Below are some top big data technologies used for data mining.
Data processing technologies help transform raw data into meaningful information through efficient computation and analysis. These include:
- Apache Hadoop: An open-source framework that enables the distributed processing of large data sets across clusters of computers. It uses a programming model called MapReduce, which divides data into smaller chunks for parallel processing.
- Apache Spark: A powerful, open-source data processing framework known for its speed and ease of use. Spark performs in-memory computations, making it much faster than Hadoop for many tasks. It supports batch processing, real-time streaming, machine learning, and graph processing.
- Apache Flink: A stream-processing framework that provides low-latency, high-throughput data processing. It is suitable for real-time analytics and supports batch and stream processing in a unified engine.
- Apache Storm: A real-time distributed processing system for handling vast amounts of data. It processes data streams in real time, making it ideal for scenarios that require immediate responses, like fraud detection or monitoring systems.
- Apache Kafka: A distributed event streaming platform used for building real-time data pipelines and streaming applications. Kafka enables high-throughput data ingestion and message queuing, supporting data flows from multiple sources.
Data Analysis and Machine Learning
Big Data analytics involves the cleaning, transformation and modeling of data, for the discovery of information useful for making decisions. The information obtained from the big data analytics tools includes correlations, hidden patterns, customer preferences, and market trends. A variety of sophisticated applications with elements such as statistical algorithms, predictive models, etc. are often used. Below are some of the data analysis technologies you should know.
These technologies enable organizations to derive actionable insights and predictions from their data:
- Apache Mahout: A machine learning library that provides scalable algorithms for clustering, classification, and collaborative filtering. It is designed for large-scale data processing tasks.
- TensorFlow: An open-source machine learning framework developed by Google. It is widely used for building and deploying deep learning models for tasks like image recognition, natural language processing, and recommendation systems.
- R and Python: Programming languages with extensive libraries for data analysis and statistical modeling. Libraries like pandas, NumPy, scikit-learn, and Matplotlib are widely used for data manipulation, visualization, and machine learning in Python.
- RapidMiner: A data science platform that provides tools for data preparation, machine learning, and predictive analytics. It is user-friendly and allows for drag-and-drop functionality to build data models.
Data Visualization and Business Intelligence Tools
Big Data visualization uses powerful computers to ingest raw data derived from corporations. This data is processed to form graphic illustrations that enable people to comprehend large amounts of information in seconds. Below are some top technologies you can use for data visualization.
These technologies help in creating interactive visualizations and reports to make data more comprehensible:
- Tableau: A leading data visualization tool that allows users to create interactive and shareable dashboards. It provides drag-and-drop features for easy data exploration and storytelling.
- Power BI: A Microsoft business analytics tool that provides a user-friendly interface to visualize data and share insights. It integrates seamlessly with other Microsoft services.
- QlikView and Qlik Sense: Tools for self-service data visualization and analytics. They enable users to extract and combine data from multiple sources for in-depth analysis.