Big data analytics – The growing use of technology in recent years has also led to an increase in the amount of data generated per minute. Everything we do online generates some data.

The DOMO report set, Data Never Sleeps, includes the amount of data generated every minute. The eighth edition of the report shows that one minute online includes over 400,000 hours of video streaming on Netflix, 500 hours of video streaming on Youtube by users, and almost 42 million messages sent to WhatsApp.

The number of Internet users has reached 4.5 billion people, which is almost 63% of the total world population (according to our calculations). This number is expected to rise in the coming years as we see the expansion of technology.

These large amounts of structured, semi-structured, and unstructured data are called big data. Companies analyze and use this data to learn more about their customers.

Big data analytics is a process that allows data scientists to create something from large batches of data. This big data analysis is done using several tools that are considered as big data analysis tools.

What is Big Data Analysis

Companies can make informed decisions based on big data analytics platforms that uncover hidden patterns, correlations, customer preferences, and market trends in data.

Data analytics technologies and methods enable companies to collect new information and analyze datasets at scale. Answer business intelligence (BI) questions related to business operations and performance. Big data tools are used for predictive modeling, algorithmic statistics, and even what-if analysis.

Why is big data analytics important?

Data analytics can play a vital role in helping organizations improve their business-related decision-making through software tools and big data analytics platforms designed for big data analytics.

The result is increased marketing efficiency, new potential revenue opportunities, the ability to provide personalized customer service, and increased cost efficiency.

Implementing these benefits as part of an effective strategy can give you a competitive advantage over your competitors.

The use of big data analytics enables companies to make better business decisions by analyzing large amounts of data to uncover hidden patterns.

The real-time big data analytics platform applies logic and math to analyze data faster for better and more informed decision making.

The most popular big data analytics tool

Open source big data analytics tools are designed to be made available to the public and are usually managed and managed by an organization with a specific mission. Let’s take a look at some important big data processing tools. Let’s look at a few examples of big data analytics tools and software used for big data analytics. Below are the best and most popular big data analytics tools.

1. MongoDB

This free and open source platform, which rose to prominence in 2010, is a document-oriented database (NoSQL) used to store large amounts of information in a structured way. MongoDB is very popular among developers as it supports various programming languages such as Jscript, Python, and Ruby.

Special features:

The backup function can be called after writing or reading data from the master.
Documents can be stored in schemaless databases.
The mongo database makes it easy to store files without messing with the stack.

2. Apache Storm

Small businesses, especially those that don’t have the resources to analyze big data, are increasingly getting powerful and easy-to-use tools. Storm has no language barriers (programming) and can support everyone. It is designed to handle large amounts of data with fault tolerance and horizontal scalability.

Storm leads the way in real-time data processing because Storm has a distributed real-time big data processing system. APACHE Storm is used in many of today’s largest process systems. The best known are NaviSite, Twitter and Zendesk.

Special features:

With the help of the APACHE Storm node, it can process up to 1 million messages per second.
Storm continues to process data even if a node goes down.

3. Apache Hadup

Big data is processed and stored on this Java-based open source platform, and the cluster system enables efficient and parallel data processing. Data from the server can be processed by multiple structured and unstructured machines and accessed by Hadoop users on different platforms. Amazon, Microsoft, IBM and other tech giants use it today as one of the best big data analytics tools.

Special features:

Businesses can use this storage solution for free and it’s an effective solution.
Can be installed on multiple hard drives or off-the-shelf hardware JBODs.
The Hadoop Distributed File System ( HDFS ) provides fast access.
Dividing large amounts of data into smaller chunks makes it easier to scale.
It can be easily implemented with MySQL, JSON and is very flexible.

4. Cassandra

APACHE Cassandra, a distributed database without SQL engine, allows you to retrieve records in large numbers. Many technology companies value high availability and scalability without sacrificing speed, or performance without sacrificing speed.

It can process petabyte-sized resources with almost zero downtime and perform thousands of operations per second. The public version of this best big data tool was created by Facebook in 2008.

Special features:

Cassandra allows you to quickly store and process data efficiently on efficient, off-the-shelf hardware.
Data can be structured, semi-structured, or unstructured, and users can modify the data as needed.
With replication, you can easily distribute your data across multiple data centers.
If a node fails, it will be replaced immediately.

5. Kubole

Machine learning ad hoc analytics uses open source big data analytics technology to extract data from the value chain. Qubole provides end-to-end services to move data pipelines with less time and effort. Set up Azure, AWS, and Google Cloud services at the same time. It also reduces cloud computing costs by up to 50%.

Special features:

To attract more customers, Qubole offers predictive analytics.
You can use this tool to move multiple data sources to one location.
Users can see real-time system information while monitoring it.

6. Lots

You can create pipelined data with minimal code. Sales, marketing and support solutions cover a wide range of requirements. It not only provides ETL and ELT solutions, but also provides an interactive graphical user interface.

With Xplenty, you can save money on hardware and software and get support via live chat, email, phone, and virtual appointments. The data can be processed through the cloud for big data analysis and shared using Xplenty.

Special features:

Integrated applications are available locally and in the cloud.
On the platform, verification of algorithms and certificates is usually possible along with SSL/TSL encryption.
Databases, warehouses, and field services can receive and process data.

7. Spark

Apache Spark also provides scalable data processing and multitasking. Big data tools also allow you to process data across multiple computers.

It is widely used by data analysts due to its easy to use API and the ability to process petabytes of data.

Spark is now perfect for machine learning and artificial intelligence, which is why the giant tech giants are currently moving in that direction.

Special features:

Users can choose the language they want to work in.
Streaming can be processed in Spark using Spark Streaming.

8. CAC

It is one of the best tools used today by data analysts to build statistical models and data scientists manage data from multiple sources and use it to search and extract it. , or you can update.

Data can be accessed in SAS or Excel spreadsheets using the Statistical Analysis System (SAS). In addition, SAS also introduced new big data tools and products to better understand artificial intelligence and machine learning.

Special features:

The data can be read in any format and is compatible with many programming languages including SQL.
Non-programmers will appreciate the easy-to-learn syntax and rich library.

9. Rapid Miner

The goal is to automate the design of data analysis workflows using visual tools. With this platform, users do not need to code to separate data. Educational technology, teaching and research are some of the industries in which they are widely used today.

Despite being open source, 10,000 rows of data and it only supports 1 logical processor. Machine learning models can be deployed to the web or mobile devices using Rapid Miner (only if the user interface is ready for real-time data mining).

Special features:

You can access various types of files (SAS, ARFF, etc.) through a URL.
For a better evaluation, Rapid Miner may display some of the results in history.

10. Data Pine

Datapine has been providing business intelligence since 2012 (Berlin, Germany). Since its launch, it has gained significant popularity in many countries, especially among SMEs that need to extract data for surveillance purposes. and choose from four price tiers starting at $249/month. Dashboards are available by feature, industry, and platform.

Special features:

Using historical and current data, datapine provides forecasts and predictive analytics.
Our AI-powered business intelligence tools and assistants are designed to reduce manual tracking.

Conclusion

Therefore, you should have a clear overview of the various big data predictive analytics tools. These tools enable individuals or businesses to improve the way they make business decisions.

However, to learn more about using big data analytics tools and big data analytics, you can enroll in the KnowledgeHut Big Data Certification online course.

This course will provide you with solid skills as you advance your big data career using the most powerful big data tools and technologies.

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