Gone are the days when banks used to store customer information (such as names, photographs, and specimen signatures) in individual postcard-like data sheets. That was an era where thick registers were used in different government offices like post offices, property tax collection centers etc. to store customers' details or maintain the daily attendance records of employees. If an employee had to update any of the registered customer's details, the task could take up the whole day. Hours were wasted searching for that particular customer's details and then creating a new record to replace the old one.
The customers, too, had to wait for hours for such minor tasks to be completed. Apart from the tediousness of searching for data from piles of ledgers, such paper files could be lost at any time due to disasters like floods or fire, apart from the degradation of the very paper on which the data was recorded.
The story is different in modern times. Almost every task, even the trivial ones are getting automated. Automation and speeding up requires data to be processed at a much faster rate. That's where the term ‚??Big Data‚?? comes in.
Big Data is referred to any data that is large in volume, changes quickly and has a vast variety. The tools that are used for big data management range from DBMS to full-fledged software tools having mining and warehousing tools integrated. There are a lot of vendors providing a wealth of great tools. So great that it becomes confusing to choose one over other. I have gone through some sites over this issue of selecting the ‚??best ones‚?? and also asked some experts in the domain. The result is this list of tools with a short description of each tool.
5 Best Open Source Tools For Handling Big Data
1. Apache Hadoop
Apache Hadoop is the most prominent and used tool in big data industry with its enormous capability of large-scale processing data. This is a 100% open source framework and runs on commodity hardware in an existing data center. Furthermore, it can run on a cloud infrastructure. Hadoop consists of four parts:
Hadoop Distributed File System: Commonly known as HDFS, it is a distributed file system compatible with very high scale bandwidth.
MapReduce: A programming model for processing big data.
YARN: It is a platform used for managing and scheduling Hadoop's resources in Hadoop infrastructure.
Libraries: To help other modules to work with Hadoop.
2. Apache Storm
Apache Storm is a distributed real-time framework for reliably processing the unbounded data stream. The framework supports any programming language. The unique features of Apache Storm are:
‚??fail fast, auto restart‚?? approach
The guaranteed process of every tuple
Written in Clojure
Runs on the JVM
Supports direct acrylic graph(DAG) topology
Supports multiple languages
Supports protocols like JSON
Storm topologies can be considered similar to MapReduce job. However, in the case of Storm, it is real-time stream data processing instead of batch data processing. Based on the topology configuration, Storm scheduler distributes the workloads to nodes. Storm can interoperate with Hadoop's HDFS through adapters if needed which is another point that makes it useful as an open source big data tool.
Apache Cassandra is a distributed type database to manage a large set of data across the servers. This is one of the best big data tools that mainly processes structured data sets. It provides a highly available service with no single point of failure. Additionally, it has certain capabilities which no other relational database and any NoSQL database can provide. These capabilities are:
Continuous availability as a data source
Linear scalable performance
Across the data centers easy distribution of data
Cloud availability points
Apache Cassandra architecture does not follow master-slave architecture, and all nodes play the same role. It can handle numerous concurrent users across data centers. Hence, adding a new node is no matter in the existing cluster even at its up time.
Also called Konstanz Information Miner, this is an open source data analytics, integration and reporting platform. It integrates different components for data mining and machine learning through its modular data pipelining concept. A graphical user interface allows the assembly of nodes for data pre-processing (which includes extraction, transformation, and loading), data modeling, visualization, and data analysis. Since 2006, it has been widely used in pharmaceutical research, but now it is also used in areas like customer data analysis in CRM, financial data analysis and business intelligence.
KNIME is written using Java and is based on Eclipse. It makes use of its extension capability to add plugins, hence providing additional functionality.
The core version of KNIME includes modules for data integration, data transformation as well as the commonly used methods for data visualization and analysis.
It allows users to create data flows and selectively execute some or all of them.
It allows us to inspect the models, results and interactive views of the flow.
KNIME workflows can also be used as data sets to create report templates, which can be exported to different document formats like doc, PPT, etc.
KNIME's core architecture allows the processing of large data volumes which are only limited by the available hard disk space.
Additional plugins allow the integration of different methods for image mining, text mining as well as time series analysis.
5. R Programming Tool
This is one of the widely used open source big data tools in the big data industry for statistical analysis of data. The most positive part of this big data tool is - although used for statistical analysis, as a user you don't have to be a statistical expert. R has its own public library CRAN (Comprehensive R Archive Network) which consists of more than 9000 modules and algorithms for statistical analysis of data.
R can run on Windows and Linux server as well inside SQL server. It also supports Hadoop and Spark. Using the R tool one can work on discrete data and try out a new analytical algorithm for analysis. It is a portable language. Hence, an R model built and tested on a local data source can be easily implemented in other servers or even against a Hadoop data lake.