Big Data Technologies – Big Data is a general term that refers to technologies and techniques for processing and analyzing large amounts of data, both structured, semi-structured and unstructured.

There are many challenges that will be faced when dealing with big data, starting from how data is retrieved, stored, to data security issues.

Even though the term big data has often been heard and spoken about, many of us still wonder: What is meant by big data? What are the uses of big data? What are the big data technologies? Why is big data needed in various fields?

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What Is Big Data Technologies ?

There is no standard definition of big data. Broadly speaking, big data is a collection of data that has a very large amount or a complex structure so that traditional data processing technology can no longer handle it properly.

Currently, the term big data is also often used to refer to fields of science or technology related to the processing and utilization of this data.

The most important aspect of big data is actually not just how much data can be stored and processed, but what uses or added value can be obtained from this data.

If we can’t extract the added value, the data will only become useless trash. This added value can be used for many things.

Such as improving operational smoothness, sales accuracy, improving service quality, market predictions or projections, and so on. In the field of informatics, there is a term “Garbage in Garbage out” or garbage input will also produce garbage output.

The point is that if the input we give to the system is low quality input, then the quality of the output will of course be low as well. The input referred to here is data.

For this reason, ensuring the quality of input and output in each stage of data processing to obtain a quality final output is a must in implementing big data.

4 Top Big Data Technologies

Big data technologies can be classified into four main types of data storage, data mining, data analysis, and data visualization.

Each is associated with a specific tool, and you should choose the right tool for your business needs based on the type of big data technology you need.

1. DataStorage

Big data technologies, which deal with data storage, can collect, store and manage large amounts of data. It consists of an infrastructure that allows users to store data for easy access.

Most data storage platforms are compatible with other programs. Two commonly used tools are Apache Hadoop and MongoDB.

MongoDB
- MongoDB is a NoSQL database that can be used to store large amounts of data. MongoDB groups documents into collections using key-value pairs (base data units). Written in C, C++ and JavaScript, it is one of the most popular big data databases because it can easily manage and store unstructured data.

Apache Hadoop
- Apache is the most widely used big data tool. It is an open source software platform that stores and processes big data in a distributed computing environment on a family of hardware.
  This distribution allows faster data processing. This framework is designed to reduce bugs or errors, is scalable, and handles all data formats.

2. Data Mining

Data mining extracts meaningful patterns and trends from raw data. Big data technologies like Rapidminer and Presto can turn unstructured and structured data into actionable information.

Presto
- Presto is an open source query engine originally developed by Facebook to perform analytical queries on large amounts of data. Now widespread. Queries in Presto can combine data from multiple sources within an organization and perform analysis in minutes.

Rapidminer
- Rapidminer is a data mining tool that can be used to create predictive models. It describes these two roles as powerhouses, processing and preparing data, and building deep learning machines and models. The end-to-end model allows both functions to influence the organization.

3. Data Analysis

Big data analytics uses technology to clean data and turn it into information that can be used to make business decisions.

The next step (after data mining) is for the user to run their algorithms, models, etc. Using tools like Apache Spark and Splunk.

Splunk
- Splunk is another popular big data analysis tool for getting insights from big data sets. It has the ability to create charts, reports, graphs and dashboards. Splunk also allows users to integrate artificial intelligence (AI) into data results.

ApacheSpark
- Spark is a popular big data tool for data analysis because it is fast and efficient when running applications. It is faster than Hadoop because it uses Random Access Memory (RAM) instead of being stored and batch processed via MapReduce. Spark supports a variety of data analysis queries and tasks.

4. Data Analytics

Finally, big data technology can be used to create stunning visualizations of that data. In a data-centric role, data visualization is a useful skill.

To provide recommendations to stakeholders about profitability and business operations to tell high-impact stories with simple graphics.

Lookers
- Looker is a business intelligence (BI) tool used to understand big data analytics and then share those insights with other team members. Charts, graphs and dashboards can be configured with queries, for example to monitor weekly brand engagement through social media analytics.

Tableau
- Tableau is a very popular tool in data visualization because its drag-and-drop interface makes it easy to create pie charts, bar charts, box plots, Gantt charts, and more. It is a secure platform that allows users to share visualizations and dashboards in real time.

Characteristics Of Big Data Technologies

The characteristics of Big Data are commonly referred to by the abbreviation 4V, namely:

1. Volume

Refers to the size of the data that needs to be processed. Currently, the volume of data in the world has exceeded zettabytes (1021 bytes), in fact, many companies or organizations need to process data up to petabytes in size per day. This large volume of data will require different processing technologies than traditional storage.

2. Variety

Big Data comes from a variety of sources, and types fall into one of the following three categories of structured, semi-structured and unstructured data. These various data types require special processing capabilities and algorithms.

An example of data with high variation is the processing of social media data consisting of text, images, sound and video.

3. Velocity

Is the speed of data generated. Data generated at high speed requires different processing techniques from ordinary transaction data. Examples of data generated at high speed are Twitter messages and data from machines or sensors.

4. Veracity

Refers to the accuracy or consistency of data. Data with high accuracy will provide quality analysis results. Conversely, data with low accuracy contains a lot of bias, noise and abnormalities.

If this data is not processed properly, it will produce output that is not useful, and can even provide an erroneous picture or conclusion.

Veracity is quite a tough challenge in Big Data processing.
In addition to the 4V, there are also those who add one more so that it becomes 5V, namely value.

This value is often defined as the potential social or economic value that data might generate. The four characteristics above (volume, velocity, variety and veracity) need to be processed and analyzed in order to provide value or benefits for business and life.

Therefore, the fifth characteristic is closely related to our ability to process data to produce quality output.

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