As we know, in today’s developing technology, data is very important. Moreover, the data we generate when we are active online continues to double every day. To process large amounts of data (big data), a big data specialist needs to use big data analytics tools.
Big data specialist is a term that covers various professions related to data, such as data engineers, data scientists, data analysts, data architects, and database administrators. In this article, we will discuss 10 tools that are used for big data purposes, watch until they run out!
Top 10 Big Data Analytics Tools
R-programming is one of the programming languages used in processing big data. The nature of this one programming language is open source, that is, it can be used for free and can be modified by anyone. Its open source nature makes many users actively contribute in developing R-programming.
Some of the advantages of R-programming
- R programming can be integrated with other programming languages, such as SQL
- Used for the process of cleansing and data manipulation, spatial analysis, data analysis and modeling, data visualization, to text analysis with natural language processing.
- Has many functions and packages that make it easier for data practitioners.
2. Apache Hadoop
As with R-programming, Apache Hadoop is open source. This is a framework tool made by Google and Apache. The Hadoop Framework is here and allows processing more data, storing heterogeneous data and speeding up the processing process.
Reporting from AWS, Hadoop is a very effective open source framework for storing very large amounts of datasets. Apart from storing, this framework can of course also efficiently process data ranging from gigabytes to petabytes in size.
Cassandra or Apache Cassandra in full, is an open source database management product distributed by Apache. Cassandra is designed to manage large capacity structured data (big data) spread across multiple servers. This software is highly scalable, so there is no doubt that dozens of large companies have entrusted Cassandra as one of their work supports, such as Facebook, Twitter, and Apple.
MongoDB is a data-based software that is quite prominent in website development. Because MongoDB is a type of NoSQL database, its data is stored using documents in JSON format, in contrast to SQL type databases which use table relations.
This is what is considered to make data management using MongoDB better. Thus, many large companies such as Google, Adobe and eBay use it.
5. Apache Spark
According to the official Apache website, Apache Spark is a framework used to analyze big data. Processing data through the Apache Spark framework is considered faster than other frameworks such as MapReduce, because the data is processed through in – memory.
The development of data in the terabyte level of data that is produced every day, creates a need for a solution that can provide real time analysis at high speed, one of which is by using Apache Spark.
Advantages of Apache Spark:
- Faster performance than traditional data processing frameworks.
- Easy to use, data processing applications built with Spark can be written in Python, R, Java, and Scala programming languages.
- Equipped with SQL Library, Streaming, and Graph Analysis which facilitate processing and data analysis.
6. Microsoft Azure
Microsoft Azure, otherwise known as Windows Azure, is a cloud computing platform developed by Microsoft. This software provides various cloud services, such as computing, analysis tools, data storage space, to networking.
Microsoft Azure aims to help businesses manage challenges and meet company goals. Therefore, this service offers a variety of tools that support the interests of all industry sectors. In addition, the tools and services offered are also compatible with all types of open source technologies.
7. Zoho Analytics
According to the official Zoho website, Zoho Analytics is a complete, reliable and scalable analytics platform. Developers and system integrators (SI) can use this platform to develop and deploy custom analytics and integration applications.
Another advantage of Zoho Analytics is that it is user friendly, making it easier for users to upload and control data. Using Zoho Analytics, it enables data practitioners to create multifaceted and custom dashboards. This platform is easy to use and implement.
This tool is widely used by data analysts, because it has several features that are quite sophisticated. These tools will make it easier for users to clean or change data according to the wishes of a data analyst.
Xplenty is a solution for ETL processes that has a cloud base and can provide a fairly simple data pipeline. This tool also has the advantage of being a strong data transformation and also free of coding. In addition, the security for the data itself is also quite guaranteed
RapidMiner was formerly known as YALE (Yet Another Learning Environment). RapidMiner is open source software. This software is a solution for analyzing data mining, text mining and predictive analysis.
RapidMiner uses various descriptive and predictive techniques to provide insight to users so they can make the best decisions. RapidMiner is written using the Java language so it can work on all operating systems.
10. Map Reduce
Literally, the definition of MapReduce is a programming model designed to be able to process very large amounts of data by dividing the processing into several tasks that are independent of one another. In processing data, in general MapReduce can be divided into two processes, namely the Map process and the Reduce process.
To use MapReduce, a programmer only needs to make two programs, namely programs that contain calculations or procedures to be carried out by the Map and Reduce processes.
So there is no need to worry about how to dismember the data to be distributed to each computer, and process it in parallel and then put it back together. All of these processes will be done automatically by MapReduce which runs on top of the Google File System.
So, those are 10 big data analytics tools that must be mastered by data practitioners. If you are interested in becoming a data professional, then it is important that you start learning this skill. You can learn independently or self-taught by relying on various free resources on the internet.
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