The Big Data Ecosystem: Four Technologies (and Some Management Tools)

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These technologies can be broken down into four classes, and each class can be used to harness different aspects of big data. Find out more about these methods and the resources available to handle such massive amounts of data.

Four distinct forms of big data technology

There are essentially four distinct kinds of big data technologies, and they are as follows: data storage, data mining, data analytics, and data visualization. There are specific tools associated with each of these types of big data technologies, and it is important to match your business’s needs with the appropriate tool.

1.Document archiving

It is possible to retrieve, store, and manage massive amounts of data with the help of big data storage technology. Structures in this system make it possible for information to be saved and retrieved quickly and easily. The majority of data storage solutions can be used in tandem with other applications. Apache Hadoop and MongoDB are two widely used programs.

  • The most widely used big data platform currently is Apache Hadoop. It’s a free and open-source software platform for managing large datasets in a distributed data center. Data processing times have been reduced thanks to this redistribution. The framework is built to have a low number of errors, to scale well, and to be able to read any kind of data.
  • Large amounts of data can be stored in MongoDB, which is a NoSQL database. In MongoDB, documents are organized into collections based on their keys and values. It’s one of the most widely used big data databases because of how well it handles both structured and unstructured data, and it’s written in C, C++, and JavaScript.

2. Exploiting Mined Data

Patterns and trends that are useful to a business can be uncovered through data mining. Rapidminer and Presto are two examples of big data technologies that can transform both structured and unstructured data into actionable intelligence.

  • Predictive models can be constructed using Rapidminer, which is a data mining tool. It takes advantage of its strengths in two areas: data processing and preparation, and model construction using machine learning and deep learning. Both departments are able to have an effect on the whole company thanks to the end-to-end model.
  • In order to perform analytical queries on Facebook’s massive datasets, the company created the open-source Presto query engine. The product has recently become widely accessible. Presto allows users to perform analytics on data compiled from numerous internal sources with just a single query.

3. Analyzing Data

The goal of big data analytics is to use technological advances to cleanse and transform data into actionable intelligence that can guide organizational decision-making. Tools like Apache Spark and Splunk are used in this phase (which follows data mining) to execute algorithms, models, and other tasks.

  • As a result of its speed and efficiency in running applications, Apache Spark has become a popular big data tool for data analysis. By avoiding batch storage and processing with MapReduce, it is faster than Hadoop by utilizing random-access memory (RAM). Spark is versatile and can be used for many different kinds of data analysis and queries.
  • Splunk is yet another widely used big data analytics tool for extracting meaning from massive data sets. You can make dashboards, reports, charts, and other visual representations of data with it. Splunk users can also use AI to enhance their information harvesting.

4. Graphical representation of data

Big data technologies allow us to at last visualize this information in a way that is truly impressive. Data visualization is an important skill for those working with large amounts of information, as it allows them to tell a compelling story with just a chart, demonstrating how those recommendations will improve the bottom line.

  • Due to its intuitive drag-and-drop interface, Tableau has become a standard in the world of data visualization, allowing users to rapidly produce charts, graphs, and diagrams of all shapes and sizes. Users can feel safe uploading and sharing dashboards and visualizations on this platform.
  • In order to make sense of big data analytics and then communicate those findings to other teams, Looker is a business intelligence (BI) tool. For instance, using social media analytics to keep tabs on the weekly activity of a brand requires only a query to set up the necessary charts, graphs, and dashboards.

Get educated on big data with the help of Coursera.

Learn everything you can about the latest big data tools. This course from Yonsei University’s Emerging Technologies: From Smartphones to the Internet of Things to Big Data specialization will teach you everything you need to know about big data analysis using Hadoop, Spark, and Storm, three of the most popular big data technologies in the world.
University of California, San Diego’s Big Data concentration may be a good fit if you want to study big data in a broader sense. The professor will help you get started with Hadoop and Spark.

 

Large-Scale Information: What Exactly Is It? Simple Steps for the Average Joe

Big data refers to the unprecedented troves of data now available for analysis in search of hidden regularities, tendencies, and connections.
Big data is the study of massive datasets for the purpose of identifying previously unseen connections, patterns, and trends. Since there are so many ways to collect information, we now have access to data in more formats, in greater quantities, and at a much faster rate than ever before. Big data is the term used to describe the new era of information with its larger scale and higher complexity.

 

Just what are the characteristics of “big data?”

There is no hard and fast definition of what constitutes “big data,” but in general, it is defined as data sets that are too large to be processed efficiently by traditional methods of data analysis.

 

Three crucial aspects of big data

There are three main characteristics of big data, and they are volume, velocity, and variety.

  • Quantity of information is measured in terms of volume. Simply put, “big data” describes extremely large datasets.
  • The speed at which information is obtained is referred to as its “velocity.” Fast-moving, massive amounts of data are increasingly being loaded directly into RAM rather than being written to disk.
  • Different types of data formats are meant by the term “variety.” In addition to numbers, text, images, and audio, big data can also take the form of other media.

Companies that deal with large amounts of data may also focus on other “Vs,” such as “value,” “veracity,” and “variability.”

What forces are responsible for the meteoric rise of big data?

The emergence of information technology has made possible the collection, storage, and analysis of data on previously unimaginable scales. New users continue to sign up for internet service in the United States and around the world, and advances in technology have enabled the internet to be integrated into a wide variety of products, generating a plethora of additional data points. Big data is growing in both volume and sophistication thanks to the millions of people who use services like Netflix and Google and make purchases on the internet every day.

Here are some real-world applications of “big data”:

  • Connected “smart” (or “Internet of Things”) devices: Products like smart thermostats, smart locks, smart TVs, mobile phones, and fitness trackers all log data that can be used by businesses.
  • Insightful data about people’s behavior, sentiment, and preferences can be gleaned from their social media activity, including their number of likes, shares, posts, comments, and time spent on a post.
  • Websites allow their owners to monitor things like how long people spend on the site, which links are the most popular, where in the world they’re from, and what they clicked on.
  • Customers’ purchases—both online and in-store—provide a rich source of information for businesses. Factors such as selling price, purchase timing, and accepted payment methods can provide valuable insights for businesses.
  • Cameras on the roads, sensors in buildings, and medical devices all have the ability to record data even when they aren’t connected to the internet.
    Regarding medical care, there is a wealth of information available within the health care database. Analysts of data cannt summaries to drive new insights and enhance patient care.
  • Cities, states, and the federal government can use data from many different sources, including vehicle traffic counts, crop yields, weather reports, and census data, to name a few, to inform policy decisions.

What can we learn about ourselves from big data and

Almost any organization can benefit from utilizing big data in order to better understand their operations and make informed decisions. For instance, businesses can use the information they gather to hone in on their customers’ tastes and develop more effective marketing and sales approaches.

When applied to healthcare systems, big data can help identify disease patterns and determine staffing needs. Governments can use traffic data for a variety of purposes, including road planning, monitoring crime and terrorism trends, and determining appropriate responses.
The following resources and approaches are available to data analysts and other professionals working with large datasets:


Using data, predictive models, and machine learning technology, analysts can foresee the likelihood of future events and trends.
When a banking system flags an international payment as potentially fraudulent, for example, real-time analytics is at work, analyzing and using the data the moment it enters a database to make quick decisions.
The term “data mining” is used to describe the process of sifting through large amounts of data in search of meaningful patterns, trends, and correlations. Identifying patterns in data is crucial for assisting businesses in making choices.


Machine learning: Machine learning, a form of artificial intelligence that learns and improves itself continuously, aids in the prediction of trends and the discovery of patterns in large sets of data. For this reason, machine learning can be helpful in dealing with data explosions.
Using computational models inspired by the workings of the human brain, deep learning is a branch of machine learning that has recently attracted a great deal of attention. Applications of deep learning can be found frequently in computer vision, speech recognition, and text recognition.


Known more commonly as “data warehouses,” these facilities are used to store vast amounts of archived information. Typically, the data is prepared for analysis by cleaning and organizing it.
Hadoop is a software framework for distributed data storage and processing, allowing for the processing of large datasets to be distributed across multiple computer clusters. In order to process massive amounts of data, Hadoop has emerged as the platform of choice due to its scalability and its ability to store multiple data types simultaneously.
SQL Server: Data analysis and machine learning have been brought together in the Apache Spark framework. In many cases, it can perform analyses on large datasets faster than Hadoop.

Working with Big Data

According to the World Economic Forum, the top three jobs expected to see growth across industries in 2020 are all related to data in some way: data analysts and scientists, AI and machine learning specialists, and big data specialists. What follows is an examination of the various fields of work that make use of big data.
A data analyst’s duties include collecting raw data, scrubbing it for errors, analyzing it, and modeling the results. Business, science, and healthcare are just a few of the many fields where data analysts are useful.


The role of a data engineer is crucial in the development and upkeep of any organization’s data systems. Data pipelines, data warehouses, and other similar structures allow analysts to easily access and analyze large amounts of data for insights. To do this, big data engineers use special programs designed to work with massive datasets.
In order to better organize and interpret data, a data scientist will often use their mathematical or statistical training to create algorithms, models, and other analytical tools.


A business intelligence analyst’s job is to sift through large amounts of data, such as sales figures and customer engagement rates, to extract useful information about a company’s operations.
Analysts in operations collect information about the inner workings of a company or other institution to better understand the problems that arise from those workings. When there is a problem with production, staffing, or anything else in the business, an operations analyst can use data to find insights and solutions.


Market researchers and analysts compile data about existing and potential clients, the state of the industry, and the methods employed by competitors. Insights gained from this analysis are used to inform future marketing and product development decisions.

Comprehend the value of big data and how to put it to use

The importance of data is only expected to grow, so it is worthwhile to learn how to incorporate big data into your career for greater insight and productivity. You can find a number of introductory courses on the internet.

  • Take UC San Diego’s Big Data course to learn the ropes of Hadoop and how to make sense of massive amounts of data.
  • Try out a Stanford University course to learn the fundamentals of machine learning.
  • Explore Apache Spark’s capabilities for scalable data science and machine learning on massive datasets.