4 Types of Big Data Technologies (+ Management Tools)

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Meta and Google use big data technologies to track sales and boost supply chain effectiveness and consumer satisfaction. 

They forecast future results as they expand and become more integrated into our lives. There is currently so much big data that the International Data Corporation (IDC) projects that by 2025 there will be 175 Zettabytes (ZB) of data worldwide, up from 33 Zettabytes (ZB) in 2018.

Big data technologies are computer programs used to manage all kinds of datasets and turn them into commercially helpful information. 

Prominent data engineers, for example, use complex analytics to evaluate and process enormous volumes of data in their work.

The four types of big data technologies and the tools that can be utilised are listed below.

Big Data Technologies: Four Types

  • Data storage technologies are used to store large amounts of data efficiently and cost-effectively. Examples include Hadoop, NoSQL databases, and cloud storage solutions. There are four main types of big data technologies:
  • Data processing technologies: These technologies are used to promptly process and analyse large amounts of data. Examples include Apache Spark, MapReduce, and Apache Flink.
  • Data visualisation tools: These tools represent data in a visual format, such as charts and graphs. Examples include Tableau, Qlik, and Power BI.
  • Machine learning and artificial intelligence technologies extract insights and make predictions from data using algorithms and statistical models. Examples include TensorFlow, sci-kit-learn, and Keras.

Data storage technologies

Data storage technologies are used to store large amounts of data efficiently and cost-effectively. Some common storage technologies include:

  • Hadoop: Hadoop is an open-source framework for storing and processing large amounts of data in a distributed manner. It is often used for storing and processing data from multiple sources, such as social media, sensors, and weblogs.
  • NoSQL databases: NoSQL databases are designed to handle large amounts of unstructured data and are often used for storing and processing data in real-real time including MongoDB, Cassandra, and Couchbase.
  • Cloud storage solutions: Cloud storage solutions allow companies to store their data on servers managed by a third party. Examples include Amazon S3, Microsoft Azure Storage, and Google Cloud Storage.
  • Data warehouses: Data warehouses are designed to store large amounts of structured data and are often used for business intelligence and analytics purposes. Examples include Amazon Redshift, Google BigQuery, and Snowflake.
  • File systems: File systems store and manage large amounts of data on a single computer or network. Examples include the Hadoop Distributed File System (HDFS) and the Linux ext4 file system.

Data processing technologies

Data processing technologies are used to process and analyse large amounts of data. Some common standard processing technologies include:

  • Apache Spark: Apache Spark is an open-source data processing engine designed to handle large amounts of data in a distributed manner. It can be used for various tasks, including data ingestion, transformation, and analytics.
  • MapReduce: MapReduce is a programming model for processing large amounts of data in parallel across a distributed cluster of machines. It is often used for data processing tasks that involve extracting data from large datasets and aggregating it in some way.
  • Apache Flink: Apache Flink is an open-source data processing engine that handles batch and stream processing tasks. It is often used for real-time analytics, fraud detection, and recommendation systems.
  • Apache Storm: Apache Storm is an open-source data processing engine that handles real-time stream processing tasks. It is often used for real-time analytics, event processing, and anomaly detection.
  • Apache Beam: Apache Beam is an open-source data processing engine that handles batch and stream processing tasks. It is often used for data ingestion, transformation, and analytics.

Data visualising tools: such as charts and graphs

Visualizing tools represent data charts and graphs. Some common data visualisation tools include:

  • Tableau: Tableau is a data visualisation tool that allows users to create interactive dashboards, charts, and maps. It is often used for tasks such as business intelligence and analytics.
  • Qlik: Qlik is a data visualisation tool that allows users to create interactive dashboards, charts, and maps. It is often used for tasks such as business intelligence and analytics.
  • Power BI: Power BI is a data visualisation tool part of the Microsoft Office suite of products. It allows users to create interactive dashboards, charts, and maps.
  • Google Charts: Google Charts is a free data visualisation tool that allows users to create various charts and graphs. It is often used for tasks such as business intelligence and analytics.
  • D3.js: D3.js is an open-source JavaScript library for creating interactive data visualisations in web browsers. It is often used for data journalism and data visualisation for the web.
  • Matplotlib: Matplotlib is a data visualisation library for Python that allows users to create various charts and graphs. It is often used for tasks such as data analysis and scientific computing.

Machine learning and artificial intelligence technologies

Machine learning and artificial intelligence technologies extract insights and make predictions from data using algorithms and statistical models. Some common machine learning and artificial intelligence technologies include:

  • TensorFlow: TensorFlow is an open-source machine learning library developed by Google. It is often used for image recognition, natural language processing, and predictive modelling tasks.
  • Scikit-learn: Sci-kit-learn is an open-source machine-learning library for Python. It is often used for classification, regression, and clustering tasks.
  • Keras: Keras is an open-source machine learning library for Python designed to be user-friendly and easy to use. It is often used for deep learning and neural network training.
  • PyTorch: PyTorch is an open-source machine-learning library developed by Facebook. It is often used for deep learning and natural language processing tasks.
  • XGBoost: XGBoost is an open-source machine-learning library designed to be fast and scalable. It is often used for tasks such as classification and regression.
  • RapidMiner: RapidMiner is a data science platform that includes a variety of machine learning and artificial intelligence tools. It is often used for data exploration, predictive modelling, and machine learning model deployment.
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