This article looks at the concepts and terms which are essential for Big Data at the fundamental level.
Introduction to Big Data:
Big Data is a term used for the ‘non-traditional’ strategies and technologies which are needed to ‘gather, organize, a process’ information and perception from the relevant dataset. The definition of Big Data is different for each project, practitioner, and vendor and business professionals because they use it differently.
In general, Big Data means large data sets and the category of computing strategies & technologies which are used to handle these large data sets. Large data sets are a set of data which has a large volume which cannot be processed with traditional processing tools. Such data sets are difficult to store on a single computer.
What Makes These Big Data Systems Different?
The requirements of working with Big Data Systems are similar to the requirements of any other dataset. The big datasets differ from other datasets in their characteristics which are the 3 Vs, volume, velocity, and variety.
Volume: Big Data systems are defined and distinguished on the basis of the amount of data which can be processed. These datasets have a large magnitude, such as terabytes and petabytes which requires cluster management and algorithms to break the data into smaller tasks and pieces to process and store.
Velocity: The speed of information movement from one system to another system also distinguishes Big Datasets from traditional datasets. The constant and frequent flow of information from multiple sources needs real-time processing so that the users can gain insight and understand the system. That is difficult for large datasets. To process, analyze the message the big data in real-time, a robust system with the availability of components to guard the failures along the data pipelines is required.
Variety: Big Data possess very unique and distinguished issues and problems which has a wide range of variety. The variety comes due to both source processing and source qualities. Big data systems are designed to store and accept the data either raw or closer to a raw state, unlike the traditional systems. Which means that at the time of processing any data transformation or alteration can occur in the memory.
Other Characteristics of Big Data:
In addition to the above characteristics, three other characteristics have been identified by the various organizations and individuals which are in fact the extensions of the original Vs.
Veracity –this indicates the variety of data sources and its complexity to process the data. it also means the challenges a person faces to evaluate the quality of big data.
Variability – it means the variation of quality of data which is the result of data variation. In order to transform the low-quality data in a useful form, to identify, filter and process this data requires additional resources.
Value – the systems and processes to extract and gather big data are usually complex which makes it difficult to extract the data with actual value.
How Is the Data Processed in Big Data System?
As we know that big data systems have a large amount of magnitude which is difficult to process with traditional tools, hence the question comes how can this dataset be processed? There are different ways devised to process and store the big data by breaking into small pieces. The activities which are involved in Big Data processing include:
- Ingesting Data into Systems
- Persisting the Data in Storage
- Data Computing and Analyzation
- Visualizing the results
Clustered Computing is one of the essential and important strategies which are used by Big Data Solutions. This strategy is considered as the foundation or base for the other big data processing activities discussed above.
Single or individuals computers are mostly unable to deal and handle the data in big data systems. Therefore to address the processing, storage, computing and other issues of big data, clustered computing techniques is used. It has certain benefits such as ‘resource pooling, high availability, easy scalability’.
Big Data Tools and Technologies:
Big Data encompasses a variety of data, so it creates a challenge to deal with its volume and complexity. The results of a recent survey show that more than 80% data is created unstructured. The main challenge is to devise ways which can be used to structure all unstructured raw data plus when the data gets structured how can we store it?
To solve these challenges, some tools/techniques have been designed for analyses and store Big Datasets. These tools are categorized into two categories, the storage tools and the analysis/querying tools.
1- Apache Hadoop – Storage Tool
Apache Hadoop is a storage tool which has a free software framework based on Java. It can be used to store a large amount of data effectively in cluster form. The Hadoop Distributed File Systems (HDFS) is another storage system of Hadoop which is used to split and distribute the Big Datasets across multiple nodes of the cluster. It allows processing data easily across the nodes.
2- Microsoft Hindsight – Storage Tool
This tool is designed by Microsoft and powered by Apache Hadoop. This can be used as a service in the cloud. The default file system of this tool is ‘Windows Azure Blob Storage’ which provides high availability at low costs.
3- Hive – Data Mining Tool
This tool can be used to manage scattered and distributed big data. It allows access to big data by using & supporting SQL-like query option which is HiveSQL/HSQL. The primary purpose of this tool is data mining.
4- Presto – Querying Tool
Presto is developed by Facebook and sourced it Query engine which is SQL-on-Hadoop. This query engine is built to deal with the petabytes of data. Presto is independent of MapReduce techniques and it can retrieve data in less time as compared to the Hive.
5- Excel – Storage and Analysis Tool
Microsoft Excel can be used as both to store as well as analyses the big data. It is possible to store big data in Hadoop platform through Excel.
There is a long list of tools which can be used to analyze and store unstructured, scattered and raw big data. In addition to the above-mentioned tools, NoSQL, PolyBase, and Sqoop can also be used to handle big data.
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