Build a Fantastic Big Data Solution on Microsoft Azure with Four Simple Steps

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Build a Fantastic Big Data Solution on Microsoft Azure with Four Simple Steps 10/5/2018 0 Comments

Big data is substantially changing the way data warehousing has been working up until now. It is a generic term that describes a large volume of data. However, Big Data refers to a large set of data that is analyzed by a set of technologies to reveal patterns or trends in the context of data analytics, artificial intelligence, and machine learning. For the growth in Big Data, the proliferation of the Internet and specifically cloud services is directly responsible.

From technologies dealing with data sources, integration and data stores, to technologies that assist with the creation of data models, Big Data solution needs a variety of different tools for presenting these through visualization and reporting. Talking about Microsoft Azure, to build and manage a Big Data solution, this technology has a comprehensive offering covering all requirements.

Moreover, building this solution on Azure to create a comprehensive Big Data offering, which integrates seamlessly and collectively requires the deployment of a suite of complementary product technologies.

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In this piece of article, we will be discussing a few steps to build a fantastic Big Data Solution on Microsoft Azure.

Big Data solution starts with data sources For required processing of the consolidated datasets, a large volume of data is much need to source and store in order to build a solution. However, from any corner of the world data can be sourced and be both structured and unstructured. For instance, take a real-time traffic management system, where data source can be sensor data installed on the actual road network, video surveillance data, and even GPS data from vehicles using the road network. To build accurate models, Big Data solutions need a vast amount of related data from different sources.

Integration and Data Storage They need to be processed and stored when data sources are identified. To meet the diverse needs of a Big Data solution, Microsoft Azure has a wide variety of integration and data storage solutions. As each Big Data solution is unique, the right set of technologies required to be chosen to align with the answer being built.

Microsoft Azure HDInsight is a Microsoft’s Big Data solution, and in the Azure cloud, it is a 100% Apache Hadoop-based service. It is a fully managed cloud service making processing massive amounts of data accessible, fast, and cost-effective. As a result, it allows you to widely accepted Big Data open source frameworks like Hadoop, Spark, Hive, and R among others.

Data Models and Analytics The next step is to perform an analysis using data models and analytics, once the Big Data solution’s data storage and integration services are defined and implemented. Over 50 different functions, Azure’s range of offerings with of analytics is vast dedicated to analytics, artificial intelligence, and IoT. Rigel Networks designs and develops automated systems for massive scale data analytics. Leveraging advanced Big Data Technologies, we convert complex, large-scale, disparate data sets into predictive reports and visualizations. We ensure efficient read-write inmemory solutions by quickly processing relational and non-relational databases. To get the nerve of the market, we offer social media sentiment analysis so that business can convert leads into customers.

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Author A Technical Content Writer skilled in creating industry-specific content. With a flair for writing, and an inclination towards technology trends, I have been writing technical articles and blogs for more than 5 years.

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