Top 7 Big Data Analytics Tools | Its Technology And Techniques

Big data is a boom right now. That is why many big data analytics tools come into existence or update themself to be more competitive. But do you ever think that big data is in trend more than any other technology? The reason is that Big data technologies use almost every technology to get their raw data.

Because of the growing number of technologies, applications, software, games to collect and manage users’ data. Big data is used to manage all this data from different sources and turn that data into valuable information to make the most challenging decisions in the industry.

Although in this blog, we are going to cover big data analytics tools and techniques. But before we dig into the details of these tools. Let’s have a quick look at techniques. 

What are the tools used for big data analytics?

There are hundreds of big data analytics tools available in the world. But there are only a few most trusted and reliable; let’s look at these tools. 

1. Hadoop: It is the most popular data warehouse to store massive amounts of data with ease.
2. MongoDB: It is the leading database software to analyze data fast and efficiently.
3. Spark: Most reliable software for real-time data processing and works efficiently to process large amounts of data in real-time. 
4. Cassandra: Most powerful database that works flawlessly to handle data in chunks 
5. Python: The best-in-class programming language to perform almost every big data analytics operation with ease.

What are the 4 different kinds of big data analytics?

4 major types of Big Data Analytics

• Prescriptive Analytics.
• Descriptive Analytics.
• Predictive Analytics.
• Diagnostic Analytics.

What is Big Data Analytics?

Big data analytics is used to extract valuable data from the raw data generated from various sources. That data helps us get meaningful insights, hidden patterns, unknown correlations, market trends, and a lot more, depending on the industries. The primary motive of big data analytics is to provide valuable insights to make better decisions for the future. 

Why is big data analytics important?

We can’t imagine the world without big data analytics. As everything is going online, and we are spending most of our time online. That is why there is a Trillion TB of data generated every day, and to handle this amount of data, we require big data analytics.

There are lots of companies using big data analytics to make more potent decisions for their future strategies. Even in some cases, it is working in real-time. Let’s have an example of it, i.e., Netflix. As we know, Netflix is the leading cloud-based OTT platform that also suggests your movies and series based on your interest.

Netflix uses big data analytics by combining various big data analytics tools, techniques, and frameworks. Although Netflix has its big algorithms, it uses big data analytics to get the most accurate results. 

The Lifecycle Phases of Big Data Analytics

Define the Goal

Big data analytics without any goal is worthless, and that is why it is required to define the goal in the initial phase of the big data analytics lifecycle.

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Identification of Data

There are a massive amount of data sources available online. But to be precise with big data analytics, you need to pick specific data sources to get the best results and minimize the load on your big data analytics tools.

Data Filtering

After identifying data sources, removing unnecessary or corrupt data from the total amount of data you have generated from the tools is time. 

Data Extraction

After the data filtration process, it is time to set the sources to extract data from them at regular intervals and then transform them into compatible forms.

Data Aggregation

After data extraction, it is time to combine the same dataset from various sources to get more precise data for further process. 

Data Analysis

After getting the required data, it is time to perform various data analytics techniques using the most potent statistics tools. 

Visualization of Data

Data visualization is the next step after data visualization, and it is not an easy job for anyone to understand the data in an unorganized form. That is what data visualization tools help us to visualize the data so that anyone can understand it. Some of the most effective big data analytics tools for visualizations are Tableau, PowerBI, QlikView, Excel, and many more. 

Final Analysis Result

Once we are done with the data analysis process, it is time to finally analyze the results. In this, we need to make sure that we make available the results to business stakeholders to make futuristic decisions. 

Different Types of Big Data Analytics

Have a look at different types of big data analytics:-

Descriptive Analytics

It summarizes the past data into a form that people can easily read and understand. It is pretty easy to create reports related to company revenue, sales, profit, and lots more with this analytics. Apart from that, it is also quite beneficial in social media metrics. 

Diagnostic Analytics

It deals with identifying the cause of the problem that occurred in the first place. It uses various techniques such as drill-down, data mining, data recovery, and so on. Diagnostic analytics provide in-depth insight into a particular problem. 

Predictive Analytics

This analytics is used to make predictions for the future. It used the historical data and the present data by using various big data techniques such as data mining, machine learning, data analytics, etc. The data generated from this analytics is used for different purposes in different industries. 

Prescriptive Analytics

When we want to have a prescribed solution to a particular problem, then we use these analytics. It works with both descriptive and predictive analytics to get the most accurate result. Apart from that, it also uses AI and machine learning to get the best results. 

Also Read

• What Are the Top Data Analytics Applications in The World
• Top 7 Most Powerful Data Analytics Tools For Beginners
• 10 Useful Data Science Techniques That a Data Scientist use

Top 7 big data analytics tools to prefer in 2022

Let’s closely look at the top 7 significant types of big data analytics tools, open-source and paid ones.

R-Programming

R programming is one of the best big data analytics tools free for everyone. It is a leading statistics programming language that can be used for statistical analysis, scientific computing, data visualization, and more. The R programming language can also extend itself to perform various big data analytics operations.

With the help of this powerful; language, data scientists easily create statistics engines that provide better and precise data insight based on relevant and accurate data collection. It has class data handling and storage. We can also integrate other data analytics tools in R programming.

Apart from that, you can be integrated with any programming language such as Java, C, Python to offer faster data delivery and accurate analysis. R provides lots of plots and graphs to use in any dataset. 

Features:

• It includes a set of operations for working with arrays, particularly matrices.
• Effective storage facility and data handling.
• It offers a complete, integrated set of big data tools for data analysis.
• It includes graphical data analysis tools that may see on-screen or printed.

Apache Hadoop

Apache Hadoop is the leading big data analytics tool open source. It is a software framework used to store data and run applications on clustering of commodity hardware. It is the leading framework that consists of a software ecosystem.

Hadoop uses its Hadoop Distributed File Systems or HDFS and MapReduce. It is considered a top data warehouse for big data analytics. It has the incredible ability to store and distribute big data sets across hundreds of inexpensive servers.

It means that you can perform big data analytics without any extra cost. You can also add new nodes to it as per your requirements, and it will never let you down.

Features

• Distributed Processing & Storage: The framework offers a lot of flexibility and manages distributed processing and storage on its own. It leaves just unique logic for data processing to be written by users.

• Highly and easily scalable: Vertical and horizontal scalability are both available. On the other hand, the differentiator is horizontal scalability. It allows more nodes to be added to the system on the fly as data volume and processing demands rise without affecting existing systems or applications.

• Cost-effective: Hadoop delivers cost savings by introducing massively parallel computation to commodity servers. It always results in a significant drop in the cost per terabyte of storage. This makes it possible to model all of your data.

MongoDB

MongoDB is the leading database software in the world. It is based on a NoSQL database that can be used to store a higher volume of data than RDBMS-based database software. MongoDB is robust, and it is one of the best big data analytics tools.

It uses collections and documents rather than using rows and columns. The document consists of the key-value pairs, i.e., a basic unit of data in MongoDB. The documents can contain various kinds of units. But the size, content, and number of fields vary from document to document in MongoDB. 

The best part of MongoDB is that it allows the developer to alter the document structure. The document structure can be based on the classes and objects defined by the programmers in respective programming languages.

MongoDB has a built-in data model that enables the programmer to ideally represent hierarchical relations to store arrays and other elements. 

Features

• High Performance: Due to characteristics such as scalability, replication, indexing, and others, MongoDB has a very high speed compared to other databases.

• Replication: MongoDB enables high availability and redundancy by creating numerous copies of the data and sending these copies to a separate server. This ensures that if one server fails, the data can be accessed from another.

• Indexing: Every field in the documents in the MongoDB database is indexed with main and secondary indices. This makes it easier and faster to obtain or search data from the pool of data. If the data isn’t indexed, the database will have to search each document individually for the query. This might take a long time and is inefficient.

RapidMiner

RapidMiner is one of the leading platforms for analysts to integrate data prep, machine learning, predictive model deployment, etc. It is the best big data analytics tools free that can be used for data analytics and text mining.

It is the most powerful tool with the best-in-class graphical user interface for the analysis process design. It is platform-independent and works on Windows, Linux, Unix, and macOS. It offers various features such as security controls, reducing the need to write lengthy codes with the help of a visual workflow designer tool.

It enables the users to adopt large datasets for training in Hadoop. Apart from that, it allows team collaboration, centralized workflow management, Hadoop impersonation, etc. It also enables

It also assembles the requests and reuses Spark containers for smart optimization of processes. RapidMiner has five products for data analysis, i.e., RapidMiner Studio Auto Model, Auto Model, RapidMiner Turbo Prep, RapidMiner Server, and RapidMiner Radoop. 

Features

• RapidMiner Radoop can connect to various Hadoop clusters, including Cloudera Distribution, MapR Hadoop, Apache Hadoop with Hive, Hortonworks Data Platform, Amazon Elastic MapReduce, and DataStax Enterprise.

• Excel, Access, IBM DB2, Oracle, Microsoft SQL, Ingres, Sybase, MySQL, SPSS, Postgres, dBase, Text files, and other data sources are supported.

• Several data management approaches are available. It includes data loading, modelling, transformation, and visualisation.

Apache Spark

Apache Spark is one of the best and most powerful big data analytics tools open source. It can process a large number of data sets with the help of its data processing framework. It is pretty easy to distribute data processing tasks across multiple computers with its conjunction or with other distributed computing tools. 

It has built-in features for streaming SQL, machine learning, and graph processing support. It also earns the site as the speediest and common generator for big data transformation. We can process the data a hundred times faster in memory and ten times faster in the disc.

Apart from that, it also has 80 high-level operators to build parallel apps more quickly. It also offers high-level APIs in Java. The platform also provides great flexibility and versatility since it works with different data stores like HDFS, Openstack, and Apache Cassandra. 

Features

• Spark code is reusable and may be used to connect streaming data with historical data, batch processing, and conduct ad-hoc queries on streaming data.

• Spark allows Hadoop applications to run up to 100 times quicker in memory and 10 times faster on storage. Spark reduces the number of disc read/write operations required for intermediate results. It keeps data in memory and only conducts disc operations when necessary. DAG, query optimizer and a highly efficient physical execution engine use by Spark to accomplish this.

• Hadoop may use as an input data source or a destination for Spark. Apache Spark is well-integrated with Hadoop’s HDFS file system that supports various file formats. And it includes ORC, JSON, parquet, CSV, and Avro.

Microsoft Azure

Microsoft Azure is one of the leading big data analytics tools. Microsoft Azure is also known as Windows Azure. It is a public cloud computing platform that Microsoft handles, and it is the leading platform that provides a wide range of services, including computing, analytics, storage, and networking.

Windows Azure offers big data cloud offerings in two categories standard and premium. It works seamlessly with a massive amount of data workloads. 

Apart from that, Microsoft Azure has best-in-class analytics with industry-leading SLA and enterprise-grade security and monitoring. It is also the best and highly effective platform for developers and data scientists. It offers real-time data that is easy to make in the most advanced applications. 

There is no need for IT infrastructure or virtual servers for processing. It can embed with other programming languages easily, like JavaScript and C#.

Features

• Scalability has been improved: Microsoft Azure can be scaled up or down fast to meet your business’s demands and surroundings. This makes it a practical alternative for numerous enterprises with varying sizes.

• Strong Analytical Support: Microsoft Azure has built-in data analysis and critical insight service. Cortana Analytics, Machine Learning, Stream Analytics, and SQL services are all included in the service.

• System of storing that is unique: Compared to competing cloud services, Azure offers more delivery points and data centres. That is why it can provide a better user experience and deliver content to your business environment more quickly. Users may store data in Azure in a quick and secure environment.

Zoho Analytics

Zoho Analytics is one of the most reliable big data analytics tools. It is a BI tool that works seamlessly for data analytics and helps us to visually analyze the data to get a better understanding of the raw data.

Likewise, any other analytics tools allow us to integrate multiple data sources, such as business applications, database software, cloud storage, CRM, and many more. We can also customize the report at our convenience because it allows us to generate dynamic and highly customized actionable reports.

It is also quite flexible and easy to upload data in Zoho analytics. We can also create a custom dashboard in it because it is easy to deploy and implement. Users around the world widely use this platform. Moreover, it also enables us to generate a comment threat in the app for facilitating collaboration between staffers and teams.

It is the best big data analytics tool that requires less knowledge and training than any other tools mentioned above. Therefore it is the best choice for startups and entry-level businesses.

Features

• Geo Visualization: Interactive map charts allow sales professionals to compare geographical performance quickly and simply. Comparisons can do between countries, states, local areas, and other areas.

• Connects to various data connectors: Connection is easier between files and feeds, CRM systems, cloud storage, various databases, Google Analytics, social media, financial platforms, e-commerce platforms, HR, and more.

• White Labelling: Individual reports or dashboards can be embedded using this technique. And the solution is white-labelled to simplify integrating into websites and apps.

Conclusion

Big data is hyped, and it will become more popular with the growing number of data. That is why we need to know the best big data analytics tools. In this blog, we have tried our level best to cover all these big data analytics tools and techniques that are in trend and will remain the same as the basics of big data.

If you still have any doubts about big data analytics tools and technology, we can help you. Apart from that, there are many other tools for big data analysis, but in this blog, we have mentioned the best ones based on our research and experience.

If you think that we have missed any of the best big data analytics tools, please comment below, and we will try to add them. Apart from that you need any help in data science, you can try out data science assignment help experts anytime. 

Frequently Asked Questions