
The market trends are flooded with hundreds of data analysis tools. However, the choice of the best tool depends on your needs, goals and variety.
Lets now look at the top 10 big data analytics tools.
What is Big Data Analytics?

A big data analytics platform can help an organization make informed decisions.
It does this by revealing patterns, correlations, and trends in market data, as well as customer preferences.
Data analytics technologies and techniques enable organizations to collect and analyze large data sets. Business intelligence (BI), or answering questions about it, is related to the performance and operation of an organization.
Big data tools are utilized for statistical algorithms, What-If studies, and predictive modeling.
- Microsoft Azure
- Cloudera
- Sisense
- Collibra
- Tableau
Big Data Analytics: Evolution
The concept of "big data" has gained popularity in recent years. Businesses are realizing that they can extract value from all the data they collect.
In the 1950s, long before the phrase "big data" was uttered by the media, businesses used basic analytics to discover insights and trends in spreadsheets.
The speed and efficiency of big data analysis are its most significant benefits. In the past, companies would collect information, use big data analysis software to analyze the data, and uncover information that they could use in future decisions.
Today, companies can tap into this data to identify insights and help them make better decisions. You can work faster and still maintain agility. This gives organizations an advantage over their competitors that they didnt have before.
Check out some of the best free big data analysis tools.
Why Is Big Data Analytics Important?
By using big data analytics software and frameworks that are aimed at analyzing large amounts of data, data analysts can help organizations improve their business decisions.
The outcome might be an improvement in marketing, new business prospects, the capacity to provide clients with individualized services, and increased cost effectiveness. By implementing these advantages as part of a strategic plan, it is possible to gain a competitive advantage over your competitors.
The use of big-data analysis can aid firms in making better business decisions by analyzing massive amounts of data and revealing hidden patterns. Big data platforms that use real-time analytics apply logic and mathematics to get faster insights, leading to a more informed and streamlined decision-making process.
Open-Source Technologies For Big Data Analytics Include:
- Hadoop
- Apache Spark
- Cassandra
- MongoDB
- HPCC
- Apache Storm
- Apache SAMOA
- Atlas.ti
Big Data Analytics: What It Is And How It Works

To operationalize big data, big data analytics involves gathering, scrubbing, processing, and analyzing massive datasets.
1. Collecting Data
Every organization has a different approach to data collection. Todays technology allows organizations to collect both structured and unstructured data, from a variety of sources - from cloud storage and mobile applications to IoT sensors in stores and beyond.
Data warehouses will store some data so that business intelligence solutions and tools can easily access it. Data lakes are used to store unstructured or raw data thats too complex or diverse for a warehouse.
2. Process Data
Data must be properly organized once it is collected and saved to ensure accurate results for analytical queries.
This is especially true when the data is large and unstructured. Data is increasing exponentially, and data processing has become a major challenge for many organizations. Batch processing is one option for processing large blocks of data over time.
Batch processing can be useful if there is a long turnaround between the collection and analysis of data. Stream processing examines small batches of information at once. This reduces the time delay between data collection and analysis, allowing for faster decision-making.
Stream processing can be more expensive and complex.
3. Clean Data
To improve the quality of data and achieve better results, all data must first be cleaned. Duplicate or irrelevant data should be removed or accounted for.
Data that is dirty can be misleading and obscure, resulting in flawed insights.
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4. Analyze Data
It takes time to transform huge amounts of data into usable form. Advanced analytics can transform big data into valuable insights once it is ready.
These statistical methods for big data analysis include:
- Data mining is the process of identifying patterns and relationships in large datasets by creating clusters and identifying anomalies.
- Predictive Analytics uses historical data from an organization to predict the future and identify upcoming risks.
- Deep learning mimics human learning patterns using artificial intelligence (AI) and machine learning. It layers algorithms to find patterns and patterns even in the most complex data.
Big Data Analytics: Benefits and Advantages

1. Risk Management
Use Case: Banco de Oro is a banking company in the Philippines that uses Big Data Analytics to identify fraud and discrepancies.
It is used to identify suspects and root causes.
2. Product Development and Innovations
Use Case: Rolls-Royce is one of the worlds largest jet engine manufacturers, and uses Big Data analytics in order to determine the efficiency of its designs and whether any improvements are needed.
3. Improved Decision Making in Organizations
Starbucks uses Big Data Analytics to make strategic business decisions. The company uses it, for example, to determine if a certain location is suitable for a brand new outlet.
They will consider several factors such as the population, demographics and accessibility of the site.
4. Enhance Customer Experience
Delta Air Lines improves user experience by using Big Data analytics. They monitor Twitter to learn about their customers experiences regarding their travels, delays and other issues.
The airline responds to negative tweets by taking the necessary steps to rectify the situation. The airline can build better customer relationships by publicly addressing the issues and providing solutions.
Big Data Analytics: Different Types

Big Data analytics can be classified into four different types:
1. Descriptive Analysis
This summarizes past data in a way that is easy to read. It is useful for creating reports such as a companys sales, revenue, and profit.
It also helps with the tabulation and analysis of social media metrics.
2. Diagnostic Analytics
It is used to determine what initially caused the problem. Examples include techniques like data mining, drill-down and data recovery. Diagnostic analytics are used by organizations because they give a detailed insight into a problem.
Use Case: A report from an e-commerce firm shows that sales are down even though customers continue to add products to their shopping carts. It could be because the form did not load correctly, that the shipping fee was too high, or that there were not enough payment options. Diagnostic analytics can help you find the cause.
3. Predictive Analysis
Predictive analytics uses historical and current data to make future predictions. Data mining, AI and machine learning are used to analyze data in order to make future predictions. It predicts customer trends, market patterns, etc.
PayPal decides on the precautions to be taken to protect its clients from fraudulent transactions. The company builds a predictive algorithm using historical data on payment and customer behavior.
4. Prescriptive Analysis
This type of analysis prescribes a solution to a specific problem. Perspective analytics is a combination of descriptive and predictive analytics. It relies on AI or machine learning most of the time.
Use Case: Prescriptive analytics can be used by an airline to maximize its profit. An algorithm that automatically modifies airline prices based on variables like client demand and weather is developed using this kind of analysis. It can also be used for holiday seasons, oil prices, and destinations.
Big data Analytics Tools

1. APACHE Hadoop
This is a Java-based platform used to store and analyze big data. Its built on a system of clusters that allows it to run efficiently, and process data in parallel.
It can handle both structured and unstructured data, from a single server to several computers. Hadoop offers its users cross-platform support. It is currently the most popular big data analytics tool, and is used by tech giants like Amazon, Microsoft, IBM etc.
Read More: Advantages of Big Data Automation for a Data-Driven Business
Apache Hadoop Features
- The storage system is free to use, and it offers a cost-effective solution for business.
- Offers quick access via HDFS (Hadoop Distributed File System).
- It is highly flexible and can easily be implemented using MySQL and JSON.
- It is highly scalable, as it allows for the distribution of huge amounts of data into small segments.
- It can be used with small commodity hardware such as JBODs or a collection of disks.
2. Cassandra
APACHE Cassandra, Large volumes of data are retrieved using an open-source NoSQL database. It is one of the most popular data analysis tools, and many IT organizations have applauded and hailed it for its high availability and scalability without sacrificing performance and speed.
It can deliver thousands of operations per second and handle petabytes with little to no downtime. Facebook created it in 2008, and published it publicly.
Features of APACHE cassandra
- Data Storage Flexibility: Supports all types of data, i.e. It supports all types of data, i.e.
- Data Distribution System: Distribute data easily by replicating it on multiple data centers.
- Cassandra is designed to be run on commodity hardware, and offers both fast data storage and processing.
- Fault tolerance: If a node fails in any way, it is replaced immediately.
3. Qubole
This open-source tool helps to fetch data from a value chain by using machine learning and ad-hoc analyses. Qubole, a data-lake platform, offers an end-to-end solution with less time and effort required to move data pipelines.
It can configure multi-cloud services, such as AWS Azure and Google Cloud. It also reduces the cost of cloud computing to 50%.
Qubole Features:
- Supports ETL Process: It allows businesses to migrate data from different sources into one location.
- Real-time insight: It monitors the users system and allows him to view real-time information.
- Qubole provides predictive analysis to help companies target more acquisitions.
- Quboles Advanced Security System protects users data stored in the cloud and ensures that any future breaches are protected. It also allows cloud data to be encrypted from any possible threat.
4. Xplenty
Its a tool that allows you to build a data pipeline using minimal code. It provides a variety of solutions in sales, marketing, and support.
It offers solutions for ETL and ELT with its interactive graphic interface. The best thing about Xplentys low-cost hardware & software is that it offers support through email, chat, and telephonic or virtual meetings.
Xplenty uses the cloud to separate data and process it for analytics.
Features of Xplenty
- Rest API allows users to do almost anything.
- Flexible: Data can easily be sent to databases, warehouses and salesforce.
- Data Security: The platform can verify algorithms and certificates on a regular basis. It also offers SSL/TLS encrypted data.
- Deployment: This app offers both cloud and in-house integration apps, as well as support for deployment of apps to the cloud.
5. Spark
APACHE Spark, another framework for processing data and performing numerous tasks at a large-scale, is also available.
The framework can also be used to process data across multiple computers using distributing tools. Data analysts use it because it has easy-to-use interfaces and can handle multi-petabytes. Spark recently broke Hadoops previous world record by processing 100 terabytes in only 23 minutes.
Spark is a great fit for ML and AI and this is why the big tech giants have moved towards it.
Features of APACHE spark:
- Easy to use: Users can run the program in their favorite language. (JAVA, Python, etc.)
- Processing in real-time: Sparks Spark Streaming module can handle real-time streaming.
- Flexible: it can run on Mesos or Kubernetes in the cloud.
6. MongoDB
In 2010, a platform that was free and open-source, and which is document-oriented (NoSQL), came to the forefront.
It is used for storing a large volume of data. MongoDB stores data in collections and documents. Documents are made up of key-value pair pairs, which is considered the basic unit for MongoDB.
MongoDB is popular with top website developers because it supports multiple programming languages, including Python, Jscript and Ruby.
MongoDB Features:
- This C++ DB is schema-less and can store a variety of documents.
- Mongo simplifies the stack: A user can store files easily without any disruption in the stack.
- Master-slave Replication: This replication method can read/write data from the master, and it can also be called for backup.
7. Apache Storm
Storm is an easy-to-use tool for data analysis, particularly in small businesses. Storm is the best because it supports all languages and has no programming barrier.
It was created to handle large amounts of data using fault-tolerant and horizontally scalable methods. Due to its distributed real-time large data processing solution, Storm is the industry leader in real-time data processing.
Many tech giants use APACHE Storm today. Twitter, Zendesk and NaviSite are some of the more notable names.
Storm Features:
- Storm can process data even when the node is disconnected
- Highly Scalable - It maintains the performance momentum even when the load increases
- Fast: APACHE Storm has an incredible speed and can handle up to one million 100-byte messages on a single node.
8. SAS
It is today one of the most popular tools used by data analysts to create statistical models. SAS allows a data scientist to mine, manage and extract data from various sources.
Statistical Analytical System (SAS) allows users to access data in any format, whether it is SAS tables or Excel sheets. SAS Viya is a cloud-based platform for business analytics. To get a better grip on AI & ML they have also introduced new products and tools.
Features of SAS
- The language is flexible and easy to learn. It has a large library that makes it accessible for non-programmers.
- Vast Data Format : This format supports many programming languages, including SQL. It can also read data in any format.
- Encryption is a security feature that provides end-to-end protection. Its called SAS/SECURE.
9. Data Pine
Datapine, a BI analytical tool, was first founded in Berlin (Germany) in 2012. It has quickly gained popularity in many countries.
Mostly, it is used to extract data (for small and medium companies that want to monitor their data). Its enhanced UI allows anyone to check and view the data according to their requirements. The customer service is available in four different price brackets, starting at $249 per month.
Dashboards are available by industry, platform, and function.
Datapine Features:
- Automation: Datapines AI assistant and Business Intelligence tools can help you reduce the amount of manual work.
- Predictive Tool: Datapine offers forecasting/predictive analytics by predicting future outcomes using previous and current data.
- Add-on: It offers visual analytics and discovery, ad-hoc reporting, as well as intuitive widgets.
10. Rapid Miner
This is a visual workflow tool thats fully automated and used for Big Data analysis.
This is a platform that does not require users to write code in order to separate data. It is heavily used today in a variety of industries, including edtech, research, training, etc. Its a free platform, but it has limitations.
You can only add 10,000 rows of data and one logical processor. Rapid Miner allows users to easily deploy ML models on the web and mobile devices (once the interface is ready for real-time data collection).
RapidMiner Features:
- Accessibility: Users can access more than 40 types of files, including SAS, ARFF and others. Accessible via URL
- Cloud Storage: Users have access to cloud storage services such as AWS, dropbox and Dropbox.
- Data validation: Rapid Miner allows the visual display of multiple historical results for better evaluation.
Read More: Big Data solutions Examples and a Roadmap for their Implementation
Big Data Analytics Concepts

The tools and traditional methods of big data analytics help organizations to make sense of the data they collect by providing them with a way to interact and visualize it.
Big data analytics can create future predictions by using these techniques to find patterns, connections, and trends.
Large data collections are often referred to as "Big Data," and big data analytics is the process of acquiring, organizing, analyzing, and discovering patterns within these data sets in order to support business decisions.
Big Data Analytics is a process that helps businesses better understand the data contained within them. Analysts who work with big data have access to the knowledge that comes from studying data. The data analytics tools for big data include a wide range of tools which can be used to improve the data analysis.
These tools include data analytics, data cleansing, data mining, and visualization, as well as data integration, storage, management, and storage.
Big data analytics requires Very High Performance Analytics. To evaluate this large amount of information, software tools for applications like predictive analytics, data-mining, text-mining, forecasting and data optimization are required.
The Best Big Data Analysis Tools: Factors to Consider
When selecting the right big data analytics tool, there are several factors to take into consideration.
- First, you must determine what type of data is available. This will help you choose the right tool. You will need a tool that is different if you are working with structured data.
- Second, the size of the data will determine which tool is best for your company. You will need a tool that is different if you are dealing with a large amount of data.
- Third, you must consider the level of analysis that is required. You will need different tools if you require a more complex analysis.
- Budget is the fourth factor that will determine what type of tool can be purchased.
How to Choose the Right Big Data Technologies and Tools
- The sheer number of tools and technologies available for big data analysis can be overwhelming. There are four main factors to consider when choosing the right tool for your business:
- Big data analytics tools should be able handle the volume, diversity, and speed of data. This data may come from social media, sensor data, transactions, etc.
- Tools should be able process data in near-real time or real time. You can then make informed decisions using the latest information.
- The tools must be able to provide insight at an individual level. This means that they should allow you to drill into the data and understand whats happening on a granular basis.
- Tools should be able to integrate with other systems. You can then get a full picture of the activities in your big data analytics company.
Big Data Analytics Limitations
Big data analytics tools have several limitations, including
- These can be costly and require many resources to implement.
- It can be difficult to use them and requires a skilled staff member to make the most of them.
- It can be difficult to collect the data they need to be effective.
- They are slow and might not be able to keep up with data that changes rapidly.
- Depending on their configuration, they can produce biased results.
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The Conclusion Of The Article Is:
The importance of big data is increasing and it will continue to grow in the future. Big data is in high demand and there are many tools available on the market share.
All you need to do is choose the right tool for your analytics project.