Just think about Big data analytics without the Cloud technology!
Overhead of data warehouses is truly a big effort for the team of analytics. Thus it is a challenge for them to analyze the results out of those terribly huge data sets. Moreover, if you have location dependency then accessing those data is time-consuming no doubt! It is the joint contribution of big data analytics and cloud computing that enables companies to utilize cloud-based software and services to tap Big data. This is indeed a low cost,more flexible and more scalable solution for the companies.

Big data analytics and cloud computing are no more a buzz word, and we have seen many advancements over the decade in this space. Like every business needs analytics for its business growth measurement. Similarly, the cloud makes life easy with cloud storage which removes the overhead of Big data storage solutions.

Besides,cloud services helps to track, analyze and get the insights of those huge and cumbersome data. Hence, comes the concept of cloud analytics which offers tremendous value as a combined technology of big data and cloud computing. Not only restricted in terms of easy processing but also in a less expensive way! This is a transformation which has turned into a new technology AaaS – Analytics as a Service.

Read more – How does Cloud DataProc simplify Big data technologies

A technical overview of Cloud technology

What are the cloud solutions available for Big data analytics?

Cloud solution for big data can come in three ways –

-Public cloud

-Private cloud

-Hybrid cloud

The public cloud service offers Big Data deployments with below benefits like –

-pay-as-you-go model

-self-service

-agility

-elasticity

On the other hand, hybrid cloud as the name suggests, the combination of the public and dedicated cloud enables more data security. However, hybrid cloud strategy put some restrictions in data movement which causes performance issues sometimes.

Mainly three types of cloud services can be used for Big data –

Infrastructure as a service(IaaS)

Platform as a service (PaaS)

Software as a Service(SaaS)

-IaaS for analytics – It can be utilized as on-premise service or virtualized. However, this is a costly solution particularly for big data analytics as the cloud provider is not responsible for installation or managing Big data software.

-PaaS for analytics – It is ideal for advanced analytics. As it provides operational support for big data analytics to build, test and deploy the applications with tools and libraries.

-SaaS for analytics – This is very much application specific and can be used as a standalone application.

Data – from data sources to Cloud

The data analytics solution of the cloud enables instant integration with all data sources. However, like all other cloud-based services, big cloud analytics is no exception. It is too is dependent on the service provider for all software related issues.

Now, let’s come to the point how that enormous amount of data is transferred to cloud from data sources? Well, for that first we need cloud data storage. It could be anything either Amazon’s S3, Microsoft Blob storage or Google Cloud storage. Also, data can be collected as

-The format of log files

-Text format

-Directly imported from databases using database adapters.

Giant vendors of Cloud services and Big data analytics

The big giants in cloud technology market like AWS or Microsoft offer Hadoop clusters which are auto scalable. For example, Amazon Web services Elastic MapReduce, or Microsoft Azure HDInsight allows scalable Hadoop clusters. Not to mention, customers need to handle the cluster as per their requirements explicitly.

However, when it comes to real-time data analysis and processing Google beats the other two competitors AWS and Azure.The primary reason behind it is high-speed services. Google’s BigQuery, Datalab or Dataproc are the unbeatable ones as they take less than a minute time to start and can easily scale the cluster.

Similarly, AWS offers Kinesis Streams for real-time stream data analytics which enables thousands of data streams processing per second. Azure is no behind in this completion. Their Stream Analytics, Data Lake Analytics, Data Lake Store are a widely used solution for real-time data analytics and storage.

Here is the table that shows the different products offered by these vendors for big cloud analytics solution –

Governance plan – a critical criterion for Cloud based data

Privacy and security are two major concerns when your confidential data is in the cloud. Hence, this is a major concerning area for most of the organizations. As per the Cloud Security Alliances, top big data-specific privacy and security challenges are:

-Computations in distributed system frameworks provide a single level of protection which is not an ideal scenario.

– Security best practices for non-relational databases (NoSQL) are actively evolving. Hence, it makes difficult for security solutions to keep the pace.

-In the case of automated data transfer, additional security measures are needed which are not always available.

-For large inflow of big data, accurate and real-time validation are often not possible.

-Cryptographic encryption for access control and connections often create the problem in secure connection.

-Lack of routine audit on a huge amount of Big data.

-Lack of real-time monitoring on Big Data which is a compliance issue.

To conclude, Cloud-enabled Big data helps to reduce the big effort of managing those enormous data sets. However, to make Cloud technology a competitive advantage for Big data analytics the data governance and security should be strengthened. Hence, it must be a part of the cloud solution.

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