Big Data is a generic term which describes a large volume of data. However, in the context of data analytics, artificial intelligence, and machine learning, Big Data refers to a large set of data which is analyzed by a set of technologies to reveal patterns or trends.

The proliferation of the Internet and specifically cloud services is directly responsible for the growth in Big Data. In the past, data was created in smaller volumes in isolated environments for specific purposes. Today, large sets of data are available for public consumption thanks to the digital disruption brought about by social media, the Internet of Things (IoT), and other online-based software applications which have created vast amounts of publicly accessible data.

 

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There are three characteristics which define Big Data known as the three V’s, namely Volume, Velocity, and Variety.

 

Volume

Big Data solutions consume data from a wide variety of complementary sources which result in large data sets of both structured and unstructured data. The larger the data set, the more accurate the data model, so Big Data solutions consume vast quantities of data to improve the reliability of the predictive models they create.

 

Velocity

Another characteristic of Big Data is velocity as data is being streamed and created at high speed. Think of news and social media content which is created at a fast pace and only relevant for a short time.

 

Variety

Variety refers to the fact that Big Data solutions draw their data from multiple disparate but complementary sources which come in many different forms. Traditional databases, media files, text documents, in fact, any kind of data could be a source for a Big Data solution.

 

 

What are the business benefits of Big Data?

The proliferation of Big Data has created a platform for predictive analytics through machine learning which unlocks benefits for all types of businesses.

The advantage Big Data has over traditional analytics is due to the three V’s discussed previously. The larger the volume of data, the greater the accuracy of the predictive analytics of machine learning algorithms. If we add real-time data processing and multiple data sources, we can build a solution which can predict business trends in real time with the precision needed to make useful, timely decisions.

In business, we all know that we cannot manage what we cannot measure. Big Data helps with this as it provides accurate information we can use to make informed business decisions. This can result in cost savings through efficient analysis of existing spend patterns and improved agility due to the real-time relevance of the generated information. Also, accurate information can mitigate risk and help businesses improve sales and retention through personalizing and tailoring services to their customers.

 

 

Big Data and Analytics on Azure

A Big Data solution needs a variety of different tools which range from technologies dealing with data sources, integration and data stores, to technologies which help with the creation of data models, presenting these through visualization and reporting.
Microsoft Azure has a comprehensive offering covering all requirements needed to build and manage a Big Data solution. Building this solution on Azure requires the deployment of a suite of complementary product technologies which integrate seamlessly and collectively to create a comprehensive Big Data offering.

 

Step 1: Data Sources

Any Big Data solution starts with data sources. To build a solution, large volumes of data need to be sourced and stored for the necessary processing of the consolidated datasets.
Data sources can be both structured and unstructured and can be sourced from anywhere. To illustrate this let’s take the example of a real-time traffic management system. Data sources could be video surveillance data, sensor data installed on the actual road network, and even GPS data from vehicles using the road network. Big Data solutions need a vast amount of related data from different sources to build accurate models.

 

Step 2: Integration and Data Storage

When the data sources are identified, they need to be processed and stored. Azure has a wide variety of integration and data storage solutions to meet the diverse needs a Big Data solution requires. As each Big Data solution is unique, the right set of technologies need to be chosen to align with the solution being built.

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

HDInsight amalgamates both the integration and data storage services needed for a Big Data solution and as such is the preferred platform for building these types of solutions. It is a native-cloud solution which is globally available and meets the necessary measures for security and compliance. It also allows you to use a variety of productivity tools ranging from Microsoft Visual Studio to Eclipse and IntelliJ and supports the Scala, Python, R, Java, and .Net platforms.

 

Standalone Integration Services

In addition, to HDInsight, Azure offers a wide range of integration services which can be used to build Big Data solutions. These range from the standard SQL Server Integration Services to a wide variety of other Azure Integration Services including Service Bus. Also, Azure also offers specialist integration solutions such as Logic Apps and Event Hubs which are services purposely built for integrating IoT Big Data solutions.

Standalone Data Storage Solutions

Microsoft Azure has a wide range of data storage solutions which can be used as the data store for Big Data solutions. These solutions range from Azure SQL Database which extends to a full data warehousing solution with SQL Data Warehouse. If the solution requires a NoSQL key-value store, then Azure Table Storage is also available. Azure also offers storage solutions for Big Data on non-Microsoft platforms ranging from Azure Cosmos DB to Redis Cache, Azure Database for MySQL, and Azure Database for PostgreSQL.

 

Step 3: Data Models and Analytics

Once the Big Data solution’s data storage and integration services are defined and implemented, the next step is to perform analysis using data models and analytics.
Azure’s range of offerings with of analytics is vast with over 50 different services dedicated to analytics, artificial intelligence, and IoT. Naturally, one would not use all 50 services on a specific Big Data analysis solution. As mentioned previously, Big Data solutions consist of a suite of relevant technologies which are integrated to form a solution platform. So, the analysis service you choose depends entirely on what type or form of analysis you are performing on the collected data.

Azure Analysis Services is Microsoft’s enterprise-grade analytics engine as a service for generic analysis services. Log Analytics can collect, search, and visualize machine data from on-premises and cloud services whereas Stream Analytics analyzes real-time data streams from IoT devices. If your solution requires an Apache Spark-based analytics platform, Azure Databricks would be the right choice, and Data Lake Analytics can run massive parallel processing programs in a variety of coding languages over petabytes of data stored in Azure Data Lake.

The services mentioned are just a few of the many different types of analysis services available on Microsoft Azure. As Big Data is such a wide and varied field, you need to tailor the analytics service you choose to the solution you have created. With Azure, these choices, options, and variations are endless.

 

Step 4: Visualization and Reporting

The final piece you need to complete a Big Data solution is the visualization and reporting platform. As with other parts of a Big Data solution, there are numerous options available, and you need to choose the services which best align with the objectives of your solution.

Azure, and by extension Microsoft, has a variety of reporting and visualization tools for this purpose. You could opt to display reports using SQL Server Reporting Services or simply extract the data and display it in Microsoft Excel. You could also choose Microsoft Power BI if you wish to have the ability to generate business intelligence dashboards, and you could ultimately display all of these through Microsoft SharePoint, either on-premise or via the Office 365 offering of SharePoint Online.

 

Bringing it all together

Big Data has definite benefits for business. However, building a Big Data solution to realize these benefits involves selecting, configuring and integrating many moving parts.
From choosing data sources to implementing data storage, integration, analytics, visualization and reporting, your choices need to align with your specific solution requirement.
Microsoft Azure has multiple data storage and integration services available which range from generic solutions to specialized solutions built for specific applications. In addition, the wide range of analytics, AI and IoT service options, and the many different reporting and visualization possibilities allow you to tailor Big Data solutions to your precise requirements.

 

Written by Alexandre Painchaud Content Marketing Specialist @ SherWeb

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