What Are The Good Tools For Big Data Analytics?

Big Data carries a whole new world of opportunities for businesses all over the world. Obviously, ingesting and capturing large volumes of data is a tough task. However, the solution you receive once you finish the toil of generating insights is worth the wait! Big Data Analytics, the solution that we are talking about, is the detailed analysis of productive patterns and correlations extracted from the stored data.

In other words, analytics is a simple representation of the data indicating hidden information that can be used for future prospects. Since you are dealing with a large amount of data, analytics generation is a complex process in itself. What you need are the tools to help you uproot the analytics from Big Data you’ve stored! 

Now the question arises, what are the best tools for Big Data Analytics? Don’t think much! We’ve listed the best analytics tools for you with their pros and cons. 

Good Tools For Big Data Analytics 

 

1. Hadoop

It won’t be misleading to call Apache Hadoop the synonym of Big Data Analytics. Hadoop is the software library which is an open-source framework responsible for processing large volumes of data with cluster computing. The processing power and multitasking abilities make it stand out from other tools. 

With the use of Java as the programming language, Hadoop has inducted the interests of numerous developers. Hadoop Big Data services are here to change the way you analyze, store, retrieve, and process Big Data. 

Pros:

1. Hadoop Distributed File System will hold every type of data, whether it is a picture, video, plain text, or XML. 
2. Hadoop will strengthen the R&D process of your business to reflect in the products and services. 
3. You can easily access the data in Hadoop which smoothens the all-over process. 
4. Above all, scalability will remain the best feature of Hadoop. 

Cons:

1. Data Redundancy can occupy extra space, and you need to invest extra time and energy to solve the issue. 
2. If input and output operations are not optimized, it can hamper the efficiency of data processing. 

2. Cassandra

Cassandra is a distributed database management system designed to handle a large amount of data effortlessly. Its efficiency to claim zero single-point failures offers a high-quality tool for developers. The fact that Cassandra is a NoSQL database, schema-free, supports easy replication, maintains consistency throughout, and flaunts simple API. 

On the other hand, Cassandra offers flexibility in both storage and distribution of the data. The database was created by Facebook and later acquired by Apache. Now, it is an integral part of giants like Netflix, Apple, and Twitter due to all the outstanding features. 

Pros:

1. No Single-point failure offers high efficiency. 
2. The lightning-fast speed at which Cassandra manages data saves a lot of time. 
3. High scalability of the database allows extra space for data and added customer accommodations. 
4. Supports ACID properties.

Cons: 

1. The troubleshooting error will hinder your working on the database. 
2. Row-level locking feature is absent. 

3. Spark

If you require Big Data analytics in your budget, consider using the largest open-source data processing analytics engine, or Spark! The framework is designed to carry heavy computations with a lightning-fast speed. The extended support for Machine Learning, Data Streaming, Graph Processing, and SQL queries sets it apart from its peers. 

Spark’s compatibility with every other tool, including Hadoop, adds another flair to its rich variety of features. The Core API welcomes varied developers of languages like R, SQL, Python, Scala, and Java. 

Pros:

1. With the integrations of different languages, Spark has a beautiful API.
2. The Spark community is highly knowledgeable and large in number, so constant support is guaranteed. 
3. Both transformations and conversions are very easy. 

Cons: 

1. Features are less mature as Spark is still under the development phase.  
2. Real-time processing is absent.  

4. Splunk 

Splunk is a software system designed to generate Big Data Analytics in real-time. The quick reporting of Splunk is often used for managing risks based on the patterns and correlations of the data. Despite the scale the company possesses, Splunk adds value to the data you’ve stored and captured. Apart from that, the graphical representations and dashboards are added advantages of using Splunk. 

With Splunk, you can also customize the interface based on your requirements and suitability. Moreover, the system will automatically place all the important tools in preference. 

Pros:

1. Its adaptability will extend across all sectors and scales of businesses. 
2. Visual reporting in the form of graphs and dashboards. 
3. Real-time monitoring for quick addressal. 

Cons:

1. Less popular than competitors.
2. Complex architecture.

In The End

All the tools comprising Big Data services India has to offer will enhance the productivity of your business. The architecture and detailed application of the tools will ease the process of analytics generation from Big Data. So, mark down your requirements and select the best tool for Big Data Analytics. 

However, you would require someone by your side to narrow down the complex analytics and design a solution for your business. This is where Ksolves makes its presence felt! Contact us for further analysis!

Contact Us for any Query

Email : sales@ksolves.com

Call : +91 8130704295

Read related articles:

Why is Apache NiFi the best choice?