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Top 15 Big Data Technologies

Many enterprises are investing in big data technologies in order to derive valuable business insights from their structured and unstructured data. In this essay, we will show you some of these technologies in some details.

 Top Big Data Technologies

1- Hadoop

Many commercial big data solutions are based on Hadoop. it is an open-source platform, specifically designed to store very large datasets using clusters. It supports both structured and unstructured data and scales effortlessly, so is great for organizations that are likely to need extra capacity without much notice.

This is a great option for organizations that have the developer resource to implement Java, but it does require some effort to get up and running. It can also handle a huge number of tasks without any latency.


2- MongoDB

MongoDB is very useful for organizations that use a combination of semi-structured and unstructured data. for example, organizations that develop mobile apps, those that need to store data relating to product catalogues, or data used for real-time personalization.


3- RainStor

Rather than simply storing big data, Rainstor compresses and de-duplicates data, providing storage savings of up to 40:1. It doesn’t lose any of the datasets in the process, making it a great option if an organization wants to take advantage of storage savings.


4- Distributed file stores

a computer network where data is stored on more than one node, often in a replicated fashion, for redundancy and performance.

5- Data virtualization

a technology that delivers information from various data sources, including big data sources such as Hadoop and distributed data stores in real-time and near-real time.

6- NoSQL Databases

NoSQL databases specialize in storing unstructured data and providing fast performance, while traditional relational database management systems (RDBMSes) store information in structured, defined columns and rows.

although they don’t provide the same level of consistency as RDBMSes. Popular NoSQL databases include MongoDB, Redis, Cassandra, Couchbase and many others; even the leading RDBMS vendors like Oracle and IBM now also offer NoSQL databases.

7- Plotly

Plotly supports the creation of charts, presentations, and dashboards from data analyzed using JavaScript, Python, R, Matlab, Jupyter or Excel. A huge visualization library and online chart creation tool makes it super-simple to create great looking graphics using a highly effective import and analysis GUI.


8- DataHero

DataHero is simple to use a visualization tool, which can suck data from a variety of cloud services and inject them into charts and dashboards that make it easier for the entire business to understand insights. Because no coding is required, it’s suitable for use by organizations without data scientists in residence.


9- IBM SPSS Modeler

IBM’s SPSS Modeler can be used to build predictive models using its visual interface rather than via programming. It covers text analytics, entity analytics, decision management, and optimization and allows for the mining of both structured and unstructured data across an entire dataset.


KNIME is a scalable open source solution that is a perfect solution if the data types are mixed. Text files, databases, documents, images, networks and even Hadoop-based data can all be read. It features a huge range of algorithms and community contributions to offer a full suite of data mining and analysis tools.


11- RapidMiner

RapidMiner is an open source data mining tool that allows customers to use templates rather than having to write code. This makes it an attractive option for organizations without a specific resource or if they’re just looking for a tool to start mining data.


12- Predictive Analytics

Predictive analytics is a subset of big data analytics that attempts to forecast future events or behaviour based on historical data. It draws on data mining, modelling and machine learning techniques to predict what will happen next. It is often used for fraud detection, credit scoring, marketing, finance, and business analysis purposes.

13- In-Memory Databases

 In any computer system, the memory, also known as the RAM, is orders of magnitude faster than the long-term storage. If a big data analytics solution can process data that is stored in memory, rather than data stored on a hard drive, it can perform dramatically faster. And that’s exactly what in-memory database technology does.

14- Edge Computing

 In addition to spurring interest in streaming analytics, the IoT trend is also generating interest in edge computing. In some ways, edge computing is the opposite of cloud computing. Instead of transmitting data to a centralized server for analysis, edge computing systems analyze data very close to where it was created, at the edge of the network.

 The advantage of an edge computing system is that it reduces the amount of information that must be transmitted over the network, thus reducing network traffic and related costs. It also decreases demands on data centres or cloud computing facilities, freeing up capacity for other workloads and eliminating a potential single point of failure.

15- Apache Spark

 Apache Spark is perhaps one of the most well-known big data analysis tools, built with big data at the forefront of everything it does. It’s open source, fast, effective and works with all major big data languages including Java, Scala, Python, R, and SQL.

 It’s also one of the most widely used data analysis tools and is used by all-sized companies, from small businesses to public sector organizations and tech giants like Apple, Facebook, IBM, and Microsoft.

Apache Spark

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