Top 10 Big Data Tools
Tuesday, August 18, 2020 1:33
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Top 10 Big Data Tools
“Every company has Big Data in its future and every company will eventually be in the data business”.
- Thomas H Davenport
With the drastic upgrade in technology, Big Data has become a buzz-word making it crucial for every company regardless of its size.
The senior executive of Accenture Institute, Jeanne Harris, says “Data is useless without a skill to analyze it.”
When every company has Big Data in its future, why not make your career future proof by moving it to Big Data Analytics. How about going for online Big Data courses and get certified? Obviously, it will open doors to a bright career in the domain.
Today there is soaring demand for Big Data professionals across the organizations globally. The number of jobs for Big Data professionals would rise to 2,720,000 by 2020, according to IBM.
The shortage of Big Data skilled professionals is affecting the wages of the candidates significantly. The average annual salary of a Big Data Engineer in the US is around $757k, according to Glassdoor.
Now, we will look at some of the Big Data tools that you need to master in order to make your career in the domain.
Top Big Data Tools
1. Apache Hadoop
It is impossible to talk about Big Data without Hadoop. A Big Data Framework, Hadoop, allows the distributed processing of large data sets across networks of computers. It can be scaled from a single server to a huge number of machines.
Its key features include flexible and quicker data processing, authentication improvement, and a robust ecosystem to meet the analytical needs of developers. It also provides support for the POSIX style file system extended attributes.
2. Apache Storm
An open-source and free data computation system, Apache Storm is a real-time framework that supports any programming language, meant for data stream processing. It provides a distributed real-time, fault-tolerant processing system.
Its key features include great horizontal scalability, auto restart on crashes, Clojure written, works with DAG topology. It has multiple use cases such as log processing, ETL, continuous computation, real-time analytics, distributed RPC, and machine learning.
3. Apache Cassandra
When you are required to do effective management of huge amounts of data, Apache Cassandra is there for you.
Its key features include support for replicating across various data centers by providing low latency for users; to facilitate fault tolerance data is automatically replicated to multiple nodes; it is best suited for applications where data is too sensitive to be lost.
4. Cloudera
Cloudera is considered as the fastest, easiest, and highly secure modern big data platform. It is open-source and has free platform distribution across Hadoop, Spark, and many more. It lets you collect, process, control, maintain, discover, and distribute huge amounts of data.
Its key features include ease in the implementation, high security and governance, comprehensive distribution, less complex administration. The advantage of Cloudera is that it can administer the Hadoop cluster seamlessly.
5. Tableau
The most popular data visualization tool that is utilized by the world’s largest organizations to understand the data is referred to as Tableau. It is a software solution meant for business intelligence and analytics which offers a variety of integrated products.
Its key features are: it offers smart features; it is razor-sharp when it comes to speed; no code data queries; it has interactive, shareable, mobile-ready dashboards; it extraordinarily supports connection with most of the databases. It offers ultimate flexibility when you wish to create data visualizations. Its data blending capabilities are excellent.
6. R
R is a free, open-source, multi-paradigm, and dynamic software environment which is considered as one of the most comprehensive statistical analysis packages. It enables wide-scale data analysis and data visualization when used with JupyteR(Julia, Python, R).
Its key features are: it can run inside SQL server, and runs on both Windows, Linux server; it is highly portable; effective data handling and storage; supports Hadoop and Spark; excellent for calculation in arrays and matrices. It has unmatched graphics and charting facilities.
7. Plotly
Plotly is an analytics tool that allows you to create charts and dashboards to be shared online. It provides data visualization and UI tools that are required in Machine Learning, Data Science, and engineering.
Its key features are: it can easily turn any data into attractive and informative graphics; with free community plan, Plotly offers unlimited public file hosting.
It also provides refined information on data provenance.
8. Qubole
Qubole is a Big Data platform that is all-inclusive and independent which learns from your actions and optimizes on its own. This makes you be carefree of managing the platform.
Its key features include: increased flexibility and scalability; optimized investment; easy to use; eliminates vendor and technology lock-in; quicker time to value.
9. Talend
Talend is an open-source big data platform meant for simplifying and automating data integration. The graphical wizard of Talend generated native code. It also lets you perform data management, and keeps a check on data quality.
Its key features include: managing multiple data sources; streamlines ETL and ELT for big data; streamlines all the DevOps processes; Speeds up your moves to real-time. It provides various connectors under one roof which lets you customize the solution according to your requirement.
10. HPCC
High-Performance Computing Cluster or HPCC is a complete big data solution over a highly scalable supercomputing platform; this is the reason that it is also called DAS (Data Analytics Supercomputer). It is an open-source tool that is based on Thor architecture that allows pipeline parallelism, data parallelism, and system parallelism.
Its key features include: fast, powerful, and scalable; comprehensive, and cost-effective, support high-performance online queries. The architecture of HPCC is based on a commodity computing cluster that improves performance.
Apart from the tools that are mentioned above, there are many Big Data tools such as Lumify, MongoDB, Datawrapper, Knime, Xplenty, SAMOA, Rapidminer, Skytree, Splice Machine, and many more.
Conclusion
You have now come across the top big data tools and their features. You can check out on some online training providers for taking the training and upskilling yourself. The training goes according to the level of your knowledge, flexible learning hours, and different modes of learning.
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