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10 Big Data Technologies You Must Know

Jun 23, 2020 | Business Intelligence, Digitalization Trends, Industry 4.0 / IoT / IIoT

As the market for big data analytics rapidly expands to include  mainstream customers, its important to know big data technologies that  really matter.

Everyone’s talking about data science, with its predictive modeling,  data mining, and machine learning. But most of this would not be  possible, especially on a large scale, without data engineering. Listed  below are few big data technologies that every data engineer must know.

1.Predictive analytics: This technology, which includes both hardware and software solutions, will help your firm discover, evaluate, optimize, and deploy predictive models (Business Intelligence – BI and Predictive Analysis). This it does by analyzing big data sources, thereby improving business performance or mitigating risk. 

2.NoSQL database: In comparison to their RDBMS  counterparts, NoSQL databases are enjoying exponential growth. The NoSQL  database type offers dynamic schema design, offering the potential for  increased customization, flexibility, and scalability, that’s much  needed when storing big data. 

3.Search and Knowledge discovery: You need to know  these tools and technologies for the self-service extraction of  information. Search and knowledge discovery is about gaining new  insights from large repositories of both structured, as well as  unstructured data that resides in sources, such as file systems,  streams, databases, APIs, and other platforms and applications. 

4.Stream analytics: If you need to aggregate, filter,  enrich, and analyze a high throughput of data. Stream analytics looks  into data that comes from multiple, disparate, and live data sources and  in varying formats. 

5.In-memory data fabric: This technology provides  low-latency access and lets you process large quantities of data. It  distributes data across dynamic random access memory (DRAM), SSD, or  Flash of a distributed computer system. 

6.Distributed file stores: A computer network that  stores data on more than one node, often in a replicated fashion, to  deliver redundancy and performance. 

7.Data virtualization: If you need information that’s  delivered from various big data sources, such as Hadoop and distributed  data stores, in real-time and near real-time, data virtualization is  your technology. 

8.Data integration: Data integration  is about tools that enable data orchestration across solutions such as  Apache Hive, Apache Pig, Amazon Elastic Map Reduce (EMR), Hadoop,  Couchebase, MongoDB, Apache Spark, etc. 

9.Data preparation: To ease the burden of shaping,  cleansing, sourcing, and sharing messy and diverse data sets that  accelerate data’s usefulness for analytics. 

10.Data quality: The technology that conducts data  cleansing and enrichment on high-velocity, large data sets. It utilizes  parallel operations on distributed databases and data stores. 

Big data technologies: things to note 

All of these tools contribute to real-time, predictive, and integrated  insights; exactly what big data customers want now. To gain the  competitive edge that big data offers, you need to infuse analytics  everywhere, exploit value in all types of data, and make a speed  differentiator. All of this requires an infrastructure that can manage  and process massive volumes of structured and unstructured data.  Big data technologies must support search, governance, development, and  analytics services for data that ranges from transaction and  application data to machine and sensor data, to geospatial, social, and  image data.


Original Article published by Allerin Tech Pvt Ltd/ Naveen Joshi/ 2018

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