Top 10 Big Data tools for data analysis

29 May 2024
4 minutos
Blog

In the modern business world, Big Data tools are essential for extracting valuableinsights to make informed decisions based on real data collected and analysed in real time. These tools allow companies to control and leverage the vast amount of data they generate on a daily basis, transforming the way they interact with information. If you are interested in training in the interesting world of Data Science at UDIT you will find the Degree in Data Science and Artificial Intelligence where we prepare you for a promising future.

The 5Vs to define a Big Data tool

Big Data is characterised by the 5Vs: Volume, Velocity, Variety, Veracity and Value. Knowing these characteristics allows you to evaluate and select the most appropriate tools according to the specific needs of each company.

Volume

This refers to the enormous amount of data that is constantly being generated. Big Data tools must be able to handle and process large volumes of dataefficiently to be useful.

Velocity

The speed with which data is generated and processed is crucial. In a business environment where decisions must be made in real time, the ability to process data quickly is indispensable.

Variety

Data comes from multiple sources and in different formats (text, images, videos, etc.). Big Data tools must be able to process and analyse this diversity of data.

Accuracy

Accuracy and reliability of data are essential for sound decision making. It is essential that tools include mechanisms to ensure data quality.

Value

Not all data is useful. Big Data tools must be able to identify and extract the valuable information that really impacts decision-making.

These five characteristics are key to selecting the right Big Data tools for any data analytics project.

Must-have Big Data tools

Here are some of the most widely used Big Data tools in use today, each with unique features that can transform enterprise data management.

Airflow

A workflow management platform that allows complex data pipelines to be scheduled and executed. Airflow integrates with Python and is ideal for creating machine learning models and transferring data in Big Data systems.

Delta Lake

Developed by Databricks, this open-format storage layer providesreliability and performance in data lakes for streaming and batch operations, with support for ACID transactions.

Apache Drill

Low latency distributed query engine for large datasets. Allows querying structured and semi-structured data from a variety of sources and is compatible with BI tools such as Tableau and Qlik.

Druid

Real-time analytics database offering low latency and high concurrency, allowingsimultaneous analysis by multiple users without impacting performance.

Alluxio Enterprise AI

Data management platform designed for intensive AI and ML tasks. It provides the performance needed for applications such as generative AI and natural language processing.

Alteryx AiDIN

Combines AI, generative AI and ML technology with the Alteryx Analytics Cloud platform, improving analytical efficiency and productivity through advanced functionality and document generation.

Databricks LakehouseIQ

Programming is the process of writing code that can be executed by a computer. It is a fundamental skill for software engineers and is closely related to mathematics and logic.

Apache Hadoop

Software framework for distributed storage and processing of large data sets. It provides distributed storage and the ability to process large volumes of data efficiently.

Apache Spark

Unified analytics engine for large-scale data processing, with support for multiple programming languages and high performance for in-memory and disk-based applications.

MongoDB

NoSQL database offering high performance, availability and scalability. Its flexible data model supports JSON documents with dynamic schemas and allows horizontal scaling through distributed sharding.

These tools represent only a fraction of what is available for working with Big Data, but they are fundamental to any data analytics strategy. Each offers unique features that help companies manage their data efficiently and extract valuable insights to improve their operations and results. With the right knowledge, Data Science and Artificial Intelligenceprofessionals can maximise the value of these tools, driving innovation and growth in their respective organisations.

Train in Data Analytics and AI with UDIT

Study at UDIT the Bachelor's Degree in Data Science and Artificial Intelligence with a unique curriculum that delves into the technical side of AI programming and algorithms. You will master the methodologies and tools that are revolutionising companies, accessing environments, tools and libraries such as Anaconda, Python, TensorFlow, MySQL and MongoDB, always under the guidance of active professionals and accompanied by internships associated with real cases.

If you are interested in training in the Degree in Data Science and Artificial Intelligence, do not hesitate to contact us. We will help you solve your doubts and accompany you in the process of choosing your future.