Right from the moment you begin your day till the time you hit your bed, you are dealing with data in some form. This article will give you the top 10 open-source big data tools that do this job par excellence. These tools help in handling massive data sets and identifying patterns.
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 organization that wants to catch the nerve of your customer base. Check out the free big data courses.
So, how do organisations harness big data, the quintillion bytes of data?
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. Also, now is the perfect time to explore an introduction to big data online course.
1. Hadoop
Even if you are a beginner in this field, we are sure that this is not the first time you’ve read 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 used when the data volume 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, commonly known as HDFS, is a distributed file system compatible with very high-scale bandwidth.
- MapReduce: It refers to a programming model for processing big data.
- YARN: All 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 in memory. It can run complicated algorithms, which is a prerequisite for dealing with large data sets.
Proficient in handling batch and real-time data, Apache Spark is flexible to work with HDFS and OpenStack Swift or Apache Cassandra. Often used as an alternative to MapReduce, Spark can run tasks 100x faster than Hadoop’s MapReduce.
3. Cassandra
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, making it more critical for big data uses.
It also offers features that no other relational and NoSQL databases can provide. This includes simple operations, cloud availability points, performance, and continuous availability as a data source, to name a few. Apache Cassandra is used by giants like Twitter, Cisco, and Netflix.
To know more about Cassandra, check out Cassandra Tutorial to understand crucial techniques.
4. MongoDB
MongoDB is an ideal alternative to modern databases. A document-oriented database is an 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 to store 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, or objects. MongoDB is easy to learn and provides support for multiple technologies and platforms.
5. HPCC
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 want to accomplish big data tasks with minimal code use, 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.
8. Atlas.ti
With this big data analytical tool, you can access all available platforms from one place. It can be utilized for hybrid techniques and qualitative data analysis in academia, business, and user experience research. Each data source’s data can be exported with this tool. It provides a seamless approach to working with your data and enables the renaming of a Code in the Margin Area. It also assists you in managing projects with countless documents and coded data pieces.
9. Stats iQ
The statistical tool Stats iQ by Qualtrics is simple to use and was created by and for Big data analysts. Its cutting-edge interface automatically selects statistical tests. It is a large data tool that can quickly examine any data, and with Statwing, you can quickly make charts, discover relationships, and tidy up data.
It enables the creation of bar charts, heatmaps, scatterplots, and histograms that can be exported to PowerPoint or Excel. Analysts who are not acquainted with statistical analysis might use it to convert findings into plain English.
10. CouchDB
CouchDB uses JSON documents that can be browsed online or queried using JavaScript to store information. It enables fault-tolerant storage and distributed scaling. By creating the Couch Replication Protocol, it permits data access. A single logical database server can be run on any number of servers thanks to one of the massive data processing tools. It utilizes the pervasive HTTP protocol and the JSON data format. Simple database replication across many server instances and an interface for adding, updating, retrieving, and deleting documents are available.
Conclusion
These were the top 10 big data tools you must get hands-on experience with 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.
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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 hear your views.
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