Right from the moment as you begin your day till the time you hit your bed, you are basically dealing with data in some form or the other. Be it giving details or taking details: you are both a consumer, and supplier or data. The digital world we live in today has embraced data to a level no one had thought of.
You must be thinking that I am exaggerating here. Well, hear this out.
Thanks to the power of the digital world, nearly 2.5 quintillion bytes of data is churned on a daily basis.
Yes, you can read the above line again.
With the advancement in the IoT and mobile technologies, not only is the amount of data procured high but also it has become equally important to harness insights from it; especially if you are an organisation that wants to catch the nerve of your customer base.
So how do organisations harness the big data, the quintillion bytes of data?
This article will give you the top 7 open source big data tools that do this job par excellence. These tools help in handling massive data sets, identifying patterns, and trends.
So, if you are someone who is looking forward to becoming a part of the big data industry, equip yourself with these big data tools.
Even if you are a beginner in this field, I am sure this is not the first time you’re reading about Hadoop. It is recognized as one of the most popular big data tools to analyze large data sets as the platform can send data to different servers. Another benefit of using Hadoop is that it can also run on a cloud infrastructure.
This open-source software framework is put into use when the volume of data exceeds the available memory. This big data tool is also ideal for data exploration, filtration, sampling, and summarization. It consists of four parts:
- Hadoop Distributed File System: This file system which is commonly known as HDFS, is a distributed file system that is compatible with very high scale bandwidth.
- MapReduce: It refers to a programming model for processing big data.
- YARN: All the hadoop’s resources in its infrastructure are managed and scheduled using this platform.
- Libraries: They allow other modules to work efficiently with Hadoop.
2. Apache Spark
The next hype in the industry among big data tools is Apache Spark. See, the reason behind this is that this open source big data tool fills the gaps of Hadoop when it comes to data processing. This big data tool is the most preferred tool for data analysis over other types of programs due to its ability to store large computations into memory. It is capable of running complicated algorithms which is a prerequisite while dealing with large data sets.
Proficient in handling both batch and real-time data, Apache Spark is flexible to work with HDFS as well as OpenStack Swift or Apache Cassandra. Often used as an alternative to MapReduce, Spark can run tasks 100x faster than Hadoop’s MapReduce.
Apache Cassandra is one of the best big data tools to process structured data sets. Created in 2008 by Apache Software Foundation, it is recognized as the best open source big data tool for scalability. This big data tool has a proven fault-tolerance on cloud infrastructure and commodity hardware which makes it more critical for big data uses.
Additionally, it offers features which no other relational database and NoSQL database can provide. This includes simple operations, cloud availability points, performance, continuous availability as a data source; to name a few. Apache Cassandra is used by giants like Twitter, Cisco, and Netflix.
MongoDB is an ideal alternative to modern databases. A document -oriented database, it is the ideal choice for businesses that need fast and real-time data for instant decisions. One thing that sets it apart from other traditional databases is that it makes use of documents and collections instead of rows and columns.
Thanks to its power of storing data in documents, it is very flexible and can be easily adapted by companies. It can store any data type, be it integer, strings, Booleans, arrays, and objects. MongoDB is easy to learn and provides support for multiple technologies and platforms.
High Performance Computing Cluster or HPCC is the competitor of hadoop in the big data market. It is one of the open source big data tools under the Apache 2.0 license. Developed by LexisNexis Risk Solution, its public release was announced in 2011. It delivers on a single platform, a single architecture, and a single programming language for data processing. If you are looking to accomplish big data tasks with minimal use of code, HPCC is your big data tool. It automatically optimizes code for parallel processing and provides enhanced performance. Its uniqueness lies in its lightweight core architecture which ensures near real-time results without a large scale development team.
6. Apache Storm
It is a free big data open source computation system. It is one of the best big data tools that offers a distributed real-time, fault-tolerant processing system. Having been benchmarked as processing one million 100 byte messages per second per node, it has big data technologies and tools that use parallel calculations that can run across a cluster of machines. Being open source, robust and flexible, it is preferred by medium and large-scale organizations. It guarantees data processing even if the messages are lost or nodes of the cluster die.
7. Apache SAMOA
Scalable Advanced Massive Online Analysis (SAMOA) is an open source platform used for mining big data streams with a special emphasis on machine learning enablement. It supports the Write Once Run Anywhere (WORA) architecture that allows seamless integration of multiple distributed stream processing engines into the framework. It allows the development of new machine learning algorithms while avoiding the complexity of dealing with distributed stream processing engines like Apache Storm, Flink, and Samza.
These were the top 7 big data tools you must get a hands-on experience on if you want to get into the field of data science. Looking at the popularity of this domain, many professionals today prefer to upskill themselves and achieve greater success in their respective careers.
One of the best ways to learn data science is to take up a data science online course. Do check out the details of the 6-month long Post Graduate Program in Data Science and Business Analytics, offered by Texas McCombs, in collaboration with Great Learning.
This top-rated data science certification course is a 6-month long program that follows a mentored learning model to help you learn and practice. It teaches you the foundations of data science and then moves to the advanced level. At the completion of the program, you’ll get a certificate of completion from The University of Texas at Austin.
Hope you will begin your journey in the world of data science with Great Learning! Let us know in the comment section below if you have any questions or suggestions. We’ll be happy to read your views.0