By now, it has been fully established that big data is much more than just a buzzword, which was thought once by a lot of people. Instead, it’s probably the biggest asset that businesses may ever have. In order to leverage big data, businesses need to have robust strategies in place for handling massive volumes of data. And this is exactly where big data analytics tools come into the picture. They help businesses to identify trends, point out patterns and derive many valuable insights that can be used by decision-makers to make informed business decisions.
It’s important to understand that big data is of no use without the analysis of the captured information and making sense of this data falls under the domain of big data analytics tools that offer different capabilities for businesses to obtain competitive value. Big data analytics is a collection of different processes which are related to business, data scientists, production teams, business management, among others.
There’re several big data analytics tools are being utilized for big data analytics model. We’ve created this post to give you an overview of some of the most popular big data analytics tools, how they work, and why they have gained popularity.
Before delving deeper, let’s have a quick look at some features and characteristics that any big data analytics tool must contain.
1- Fundamental features of big data analytics tools
- Analytic capabilities: Different big data analytics tools come with different types of analytic capabilities like decision trees, predictive mining, neural networks, time series etc.
- Integration: Sometimes additional programming languages and statistical tools are required by businesses to conduct different forms of custom analysis. So, it’s required for big data analytics tools to come equipped with it.
- Scalability: Data wouldn’t be the same always and will grow as a business grows. With the scalability feature of big data analytics tools, it’s always effortless to scale-up as soon as the business captures new data.
- Version control: The majority of the big data analytics tools get involved in the adjustment of the parameters of data analytics models. Version control feature helps to improve the capabilities to track changes.
- Identity management: Identity management is a required feature for all effective big data analytics tools. They should be able to access all the systems and all related information which may be associated with the computer software, hardware, or any other individual computer.
- Security features: Data security should be paramount for any successful business. The big data analytics tools that are used should come with safety and security features to safeguard the collected data. In addition, data encryption is an imperative feature which should be offered by big data analytics tools.
- Visualization: This feature of big data analytics tools enables professionals to display the data in a graphical format, making it more useable.
- Collaboration: Though analysis can be a solitary exercise sometimes, it frequently involves collaboration and thus, this feature is required.
You can always go out and purchase big data analytics tools in order to cater to the needs of your business. But all big data analytics tools aren’t created equal and some may not be efficient in dealing with the task for which you’re buying it. In addition, buying additional tools beyond your business’s existing analytics and business intelligence applications may not be necessary based on the particular business goals of a project.
In this post, we’re going to take a closer look at some of the most popular big data analytics tools to help you make an informed purchase decision. Just ensure that the tool you select comes with all of the features mentioned above together with other ones that may be required to support your business results and organizational decision-making teams as well.
2- Popular big data analytics tools
Here’re some of the widely used big data analytics tools together with their key advantages.
2.1- Apache Hadoop
It’s a software framework employed for the handling of big data and clustered file system. This open-source framework offers cross-platform support and is being used by some of the giant tech companies including Microsoft, IBM, Facebook, Intel etc.
Advantages:
- Highly scalable
- Offers quick access to data
- Presence of Hadoop Distributed File System (HDFS) that comes with the ability to hold every type of data
- Highly effective for R&D purposes
2.2- Tableau Public
This intuitive and simple tool offers valuable insights through data visualization. A hypothesis can be investigated with the help of Tableau Public. You can embed visualizations published to this tool into blogs and share web pages through social media or email.
Advantages:
- Enables free publishing of visualizations to the web
- No programming skills required
2.3- Google Fusion Tables
When it comes to big data analytics tools, Google Fusion Tables is a cooler version of Google Spreadsheets. You can use this excellent tool for data analysis, large dataset visualization etc. In addition, you can add Google Fusion Tables to your business analysis tools list.
Advantages:
- Lets you visualize larger table data online
- Lets you summarize and filter across a huge number of rows
- Enables you to create a map in minutes
2.4- Storm
It’s an open-source and free big data computation system. It comes with distributed stream processing, fault-tolerant, real-time processing system together with real-time computation capabilities.
Advantages:
- Guarantees the processing of data
- Reliable at scale
- Very fast and fault-tolerant
2.5- RapidMiner
It’s a cross-platform that comes with an integrated environment for predictive analysis, data science, and machine learning. It comes under different licenses and the free version allows for up to 10,000 data rows and 1 logical processor.
Advantages:
- The effectiveness of front-line data science algorithms and tools
- Integrates well with the cloud and APIs
- The convenience of code-optional GUI
2.6- Qubole
This all-inclusive, independent big data platform manages, learns, as well as, optimizes on its own from the usage. It enables the data team to focus on business outcomes rather than managing the platform.
Advantages:
- Increased flexibility and scale
- Faster time to value
- Optimized spending
- Easy to use
2.7- NodeXL
It’s one of the best big data analytics tools available in the market. This open-source software offers exact calculations and comes with advanced network metrics.
Advantages:
- Graph visualization
- Graph analysis
- Data import
2.8- Apache SAMOA
SAMOA or Scalable Advanced Massive Online Analysis is an open-source platform for machine learning and big data stream mining. With this, you can create distributed streaming ML algorithms and have them run on multiple DSPEs.
Advantages:
- True real-time streaming
- Fast and scalable
- Simple to use
2.9- Lumify
This free and open-source tool lets you perform big data fusion/integration, visualization, and analytics. Some of its primary features are 2D and 3D graph visualizations, full-text search, integration with mapping systems, automatic layouts, among others.
Advantages:
- Scalable and secure
- Supported by a dedicated and full-time development team
- Supports the cloud-based environment
2.10- MongoDB
It’s a NoSQL database written in JavaScript, C, and C++. It comes with features like Aggregation, Indexing, Replication, MMS (MongoDB management service), file storage, load balancing, among others.
Advantages:
- Reliable and low cost
- Easy to learn
- Offers support for multiple platforms and technologies
2.11- Datawrapper
It’s one of the big data analytics tools that are used by newsrooms throughout the world. This open-source platform enables its users to quickly generate precise, simple, and embeddable charts.
Advantages:
- Works very well on every type of device
- Fast and interactive
- Fully responsive
- No coding is required
2.12- Open Refine
This big data analytics tool lets professionals clean up data for analysis. It comes with cells under columns which is similar to relational database tables. With this tool, you’d be able to perform things like cleaning messy data, the transformation of data, parsing data from websites etc.
Closing Thoughts
Big data analytics tools have become imperative for large-scale industries and enterprise because of the massive volume of data they need to manage on a regular basis. These tools help businesses save a significant amount of resources and in obtaining valuable insights to make informed business decisions. As big data analytics refers to the complete process of capturing, organizing, and analyzing massive sets of data, the process requires very high-performance analytics. In order to be able to analyze such massive volumes of data, specialized software like big data analytics tools are must.
In the present situation, the volume of data is steadily increasing along with the technology growth and world population growth. This is a clear indication of the immense necessity of having big data analytics tools for businesses to leverage the power of that data. These tools are being heavily used in some of the most widespread sectors including travel and hospitality, retail, healthcare, government, among others.
With huge investments and interests in big data technologies, professionals with big data analytics skills are in high demand. For those looking to step into this field, probably this is the best time to get some certifications to showcase their skills and talent. It’s important to note that the domains of the big data landscape are quite different and so does their requirement. Since data analytics is the emerging one in every field, the need for trained professionals with adequate knowledge is naturally huge as well.
