The Best Big Data Tools for Data Enthusiasts!

Big Data Tools. Image Source: Photo by Danny Meneses/Pexels

Data is one of the most essential assets in data processing. Data is like gold mined to get the best quality gold and can be turned into gold that can be used as raw material for jewelry. Likewise, with the data obtained every day, the quantity is increasing erratically. In handling this voluminous data set, data analysts and specialists are needed to gain valuable insights from the existing data set. They must understand the process by process of data in its raw form until it is ready to be processed and decisions are made for related parties.

One of the ways that an analyst can get actionable insights from data is the role of the tools that will be used. A big data specialist, data analyst, or data scientist must also understand what tools they want to apply to support their data processing. For example, if a data analyst wants to do statistical data processing, then R or MatLAB tools seem to be one of the right choices. 

Meanwhile, if a data practitioner wants to do data processing in relational database management, he can use SQL. So, are these tools enough? Are there other reference tools for large-scale data processing? UMN friends must be curious, right? This time, we will discuss more about the best big data tools for data activists. 

1. Map Reduce

MapReduce is a programming paradigm and execution model used in Big Data analysis. It is a popular and effective technology for processing and calculating data in a distributed and parallel manner in an environment consisting of computer clusters.

MapReduce is designed to address the problem of processing extensive and complex data by breaking the task into smaller parts, which are then processed in parallel. This helps improve processing performance and reduce the time required to complete large tasks.

By dividing and processing data in a distributed manner, MapReduce enables efficient and scalable Big Data processing. This technology has been the foundation for various Big Data processing systems, such as Apache Hadoop, which uses MapReduce as a key component in its framework.

Also read: Big Data: Definition, Examples, and Its Functions.

2. Zoho Analytics

According to Zoho’s official website, Zoho Analytics is a complete, reliable, and scalable analytics platform. Developers and system integrators (SIs) can use this platform to develop and deploy custom analytics applications and integrations.

3. MongoDB

MongoDB is a type of NoSQL database that is quite popular for websites.

If other SQL databases store data using table relations, then the difference with MongoDB is that it is document-based using the JSON (JavaScript Object Notation) file format.

The use of NoSQL itself, in general, is usually more specialized to handle substantial amounts of data or big data. Thus, the architecture of the two types of databases is very different.

MongoDB is a database system that uses essential components, namely: 

• MongoDB database is a container with a storage structure called a collection.
• MongoDB collection is a place where data information is collected in the form of documents. Collections are combined like tables that contain data in an SQL database.
• MongoDB document is the smallest unit in MongoDB.

4. RapidMiner

RapidMiner provides various features and functions to facilitate complex data analysis, including data processing, mining, and visualization. Using this tool, users can import data from various sources, clean and process it, apply various analysis techniques such as classification, clustering, and regression, and build predictive models.

One of the advantages of RapidMiner is the drag-and-drop interface that allows users with varying levels of expertise in data analysis to use the tool efficiently. It also provides a variety of statistical modeling and machine learning algorithms that can be applied to data to generate valuable insights and information.

RapidMiner also supports integration with other data analysis tools like Hadoop, Spark, and other distributed databases. This allows users to work with large datasets and use the parallel processing capabilities offered by other big data tools.

5. Cassandra

The next big data tool is Cassandra. Cassandra is one of the popular databases used because it has good speed and scalability. This is shown by Cassandra DB being included in the top 10 list of the most widely used database engines based on the DB Engine website. The Apache Cassandra database is the right choice for those of you who need high scalability and availability without ignoring the performance of the system you want to create. Cassandra’s ability to store huge amounts of data is no doubt; dozens of large companies have used Cassandra as one of the work supports in improving their business, such as Facebook, IBM, Reddit, Apple, Twitter, and many more.

6. Xplenty

Xplenty has various advanced features in terms of data analysis, making it very popular. This one tool will make it easier for users to clean and change data as desired.

Xplenty itself is one of the cloud-based solutions for ETL that provides a simple pipeline.

Xplenty has several advantages, including strong data transformation and guaranteed data security. 

Also read: Data Science College Majors to Become a Data Scientist.

Well, that was a series of big data tools that can be used in processing large-scale data, plus the mainstay that data enthusiasts often use. What tools do you use more often? 

By Reyvan Maulid | UMN News Service

English translation by Levina Chrestella Theodora

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