As the data around us is increasing it is becoming difficult to manage data and use it in a meaningful way. To deal big data in a purposeful manner, we need to make use of specialized tools which can make data handling efficient and effective.
Using traditional tools cannot organize the analytics of big data, hence few of the available tools are discussed below. The tools of big data are distinguished into three main categories they are:
- Stream Processing: This type of processing needs to handle large amounts of real-time data. Applications like sensors in the industry, online streaming and log file processing requires real-time processing of large data. The live processing of big data requires less latency while processing huge data. The Mapreduce model handles this efficiently by providing high latency as the map phase data need to be saved on the disk before the reduce phase begins, this leads to more delay and makes it not feasible for data processing in real-time.
- Batch Processing: Apache Hadoop is known as the most dominant tool for batch processing used in big data. It is widely used among different domains such as data mining and machine learning. It balances the load by distributing it through different machines. It functions extremely well in processing large data as it is specifically designed for batch processing.
- Interactive Processing: The interactive analysis tools allow user to interact with data and make data analysis in their own way. In this type of processing, user can make interactions with the computer as they are directly connected to it.
These three categories consist of various tools which are classified according to the way they process data. Below, the functioning of each tool is described briefly.
Stream Processing Tools
This is one of the Most popular stream processing platforms, it is scalable, open source, fault tolerant and distributed for unlimited data streaming. It is developed specially for streaming data that is simple to operate and makes sure all the data is processed. It processes millions of records each second which makes it and efficient platform for data streaming.
This is another intelligent and real-time platform useful in accessing big data to retrieve information produced by machines. It enables users to monitor, access and analyze data through a web interface. The results are represented through reports, alerts and graphs. The unique characteristics of splunk like indexing of structured and unstructured data, creating dashboards, online searching and real time reporting makes this tool different from other stream processing tools.
Batch Processing Tools
Hadoop which is basically a software platform developed for distributed data-intensive applications. It uses mapreduce as a computational paradigm. Google and other web companies have developed Mapreduce, which is a programming model useful in analyzing, processing and generating huge data sets. It breaks a complex problem into subproblems and continues this process till every subproblem is handled directly.
It is a programming model which has the capability to process programs in both parallel and distributed ways. It has the ability of processing from small cluster to very large cluster. It makes use of the method of cluster to process and execute in a distributed manner. With the help of Dryad framework programmers can work on as many machines as they can, even having multiple cores and processors.
Talend Open Studio
This tool provides the facility of graphical interface to the users to visually analyze data. Apache Hadoop introduced Talend as an open source software. Unlike Hadoop, users have the ease of solving problems without the need of writing java code. Moreover, users have the drag and drop option of icons according to their defined tasks.
Interactive Analysis Tools
It was proposed by a well-renowned company Google that supports interactive processing. Dremel’s architecture is very different from Apache Hadoop that was developed for batch processing. Additionally, it has the ability to run a group of queries in seconds over a table that has trillions of rows with the help of column data and multi-level trees. It also supports hundreds of processors and can accommodate petabytes of data of thousands of Google’s users.
A distributed platform which supports processing of interactive analysis of big data is known as Apache Drill. It is more flexible when compared to Google’s dremel in terms of support for different query languages, various sources and data types. Drill is aimed to handle thousands of servers, to process trillions of user records and can process petabytes of data in a very little time. Dremel and Drill are designed to effectively explore the nested data. Apache drill and Google’s dremel are specialists in large scale interactive analysis processing to respond to ad-hoc queries, as for storage they are using HDFS and for batch analysis, Map/Reduce model is used.