Best Big Data Analytics Tools & Software for 2023

What is Big Data Analytics?

Fundamentally, Big data analytics includes studying and reporting hidden trends, patterns, and correlations in large amounts of raw data which are then used to make data-informed decisions. These processes employ well known analysis techniques — including clustering regression, and apply them to big copious data sets. This blog explains all about Big Data Analytics, its trends, tools and more.

Evolution of Big Data Analytics

Big Data is not a new concept. First observed during World War 2, primarily for war function, data collection and personal computing was seen gaining popularity. Slowly through time, the corporate and general public also realized the value of big data and it started trickling down to use in commercial sectors and finally to the general public until it became a feasible option for everyday customers.

Limitations of Big Data Analytics Tools

Although Big Data Analytics has proven to be beneficial for businesses looking to grow and make a mark, it also has limitations to its usage. Few of them are:

Data Quality and Accuracy

Big Data analytics relies heavily on the quality and accuracy of the data collected. This means, in case of incomplete, inconsistent or error ridden data, results are significantly affected and are not as accurate as one would hope.

High Infrastructure requirements

Management and analysis of Big data requires substantial computing power and extensive storage capabilities to process and analyze large volumes of data. Building and maintaining such an infrastructure is costly and resource-intensive and not feasible for small and medium enterprises.

Data privacy

Big data analytics often involves handling sensitive and confidential data. Ensuring the privacy and security of such data is crucial but challenging. There is a risk of data breaches, unauthorized access, or mishandling of data, which can have severe consequences for organizations and individuals.

Lack of tech talent and adequate skill set

One needs experienced developers and data analysts to extract meaningful insights from big data . These developers should also possess the necessary expertise to navigate through complex analytics tools and algorithms. There is an acute shortage of professionals with these specialized skills, which limits the effective utilization of big data analytics tools.

Best Big Data Tools of 2023

Although Big Data is a boon for businesses, not every business has robust strategies to handle huge volumes of data. This is where Big Data analytical tools come handy. These tools help different companies analyze, detect trends, identify patterns and derive predictable outcomes which favor their business decisions. Here are a few:

Apache Hadoop

Apache’s Hadoop is an efficient Big Data Tool used for handling big data and clustered file systems. It is an open-source framework that supports multiple platforms and is preferred by many corporate giants such as Microsoft, Facebook, Intel etc.

Hadoop delivers on the following benefits :

• Scalability
• Easy accessibility to data
• Hadoop Distributed File System (HDFS) can handle any and every type of data
• Easy Maintenance and updating

However, there are also few challenges of Hadoop one needs to keep in mind. They are:

• Issue with slow processing small files
• Lack real time processing
• Not the best option for iterative processing

Tableau Public

Yet another handy Big Data tool, Tableau Public is useful for garnering insights into ways to visualize the data. Known for having the largest repository of data visualizations to learn from, Tableau Public facilitates Big Data Analytics for businesses.

• State of the art Visualization capabilities
• Huge community
• User friendly designed interface and mobile friendliness
• Ease of use and little to no technical expertise needed

Qubole

Qubole is a pit stop for all data browsing and querying needs of developers. It is a cloud based data analytics platform which offers solutions to businesses for digital transformation, user engagement, data driven products, marketing,

modernization and security intelligence amongst others. Qubole has proven its worth to businesses when it comes to delivering high quality solutions.

Here are a few given advantages of using Qubole

• Fast time to value
• Cross platform support
• 10X improved productivity
• 1:200 operator user ratio
• Lower costs of cloud computing

Storm

Another effective Data Analytics tool to watch out for is Storm. Storm is an open-source big data computation system. With segregated stream processing, enhanced error detection, real time processing and much more, Storm is very beneficial for businesses looking for worthwhile Data analytics tools.

Given below are a few characteristic features that makes it a splendid tools for enterprises:

• Reliable processing of data in real time
• Easily scalable
• Runs at high speed and is fault tolerant

RapidMiner

Another tool which is preferred by developers is Rapid Miner. Rapid Miner is a cross-platform analytics tool which comes inbuilt with integrated tools equipped with predictive analysis, data science, and machine learning. Along with this it has many licenses completely free of cost up till 10,000 data rows and 1 logical processor.

• Rapid Miner’s benefits include:
• Advanced data science algorithms and tools
• Easy integration within APIs and cloud
• Code-optional GUI

Apache SAMOA

Apache Samoa is an open-source tool often used for developing and executing machine learning algorithms on distributed stream processing platforms. SAMOA stands for Scalable Advanced Massive Online Analysis which provides a framework for building machine learning algorithms on distributed streaming .

SAMOA is primarily used in applications where instantaneous analysis is required and decision-making on streaming data is critical. Few examples in applications of fraud detection, sensor analysis, network monitoring and recommendation systems.

Advantages of Apache’s Samoa include :

• True real-time streaming
• Fast and scalableSimple to use

However, there are also few challenges that SAMOA poses:

• Complex understanding is required of stream processing
• Scalability is a little complex since it is stream processing tool
• SAMOA requires extensive preprocessing of data which can be a time consuming process

Factors to consider while selecting a Best Big Data Analytics Tool

It can be exhausting to choose the right Analytics tool from the many present in the market. Given below are a few factors to keep in mind when selecting the right Big Data analytics tool for your business.

• Know your requirements: List your core objectives and expectations after thorough research of the same.
• Budget: The tools in the market cost variably. It is a good practice to decide on a budget before choosing any tools.
• Advanced Tools: Choose the tools with latest technologies which must go beyond simple mathematical calculations.
• Tool’s Ability to Integrate: Standalone solutions are great for businesses but integrated solutions improves accessibility to analytics reports from already user friendly applications.
• Collaborative Tool: Opt for tools which are collaborative and support brainstorming via different channels.

Conclusion

Big Data is revolutionary and Big Data Analytics even more so. It is capable of transforming industries by innovative solutions and machine learning applications. The $274 billion market of Big Data and Analytics is surely an encouragement for businesses looking to reinvent the way they function. One can easily predict that use of Big Data and Analytics will be a necessity for all businesses in near future.

OpenXcell is an excellent BI and Analytics Company for businesses looking to incorporate benefits of Big Data and Analytics into their work processes. All solutions developed by the company are avant garde and technologically advanced, sure to make a company stand out amongst their peers. Hire dedicated developers equipped with apt skills and knowledge of Big Data and Analytics.