Managing Big Data in Amazon Cloud, Google Cloud, Microsoft Cloud

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With the world generating stupendous amounts of data every second, Big Data analytics took prominence as more and more companies leveraged the power of data to improve their business processes. The technology also fuelled a growth in career opportunities for new entrants and existing professionals. The rise of the internet and the advantages brought about by cloud computing, on the other hand, started a mass migration of companies towards the use of cloud-based services. Cloud computing also brought about large-scale career opportunities for upskilled IT professionals and new entrants alike.Lets find out more about Managing Big Data.

It wasn’t long before Big Data and Cloud Computing came to be used together as many extensive cloud services started offering big data analytics. With Software as a Service (SaaS) growing in popularity, it became necessary for organizations to stay up-to-date with the cloud infrastructures and offerings in data analytics. Fast forward to today, and companies expect professionals working with big data to possess cloud computing experience. With both the skill sets highly sought after, big data and cloud computing bridge the cross-domain gap and prepare the professionals for the future.

What does it mean to manage big data on the cloud?

Big Data is large sets of data output by several programs or a large variety of different types of data. The data sets are often too large to query or use on a single computer. Simplistically, the cloud refers to a network of high-end servers offered by Amazon, Google, and Microsoft. The cloud platforms can easily query large data sets as they have both memory and processing power.

The cloud service providers offer their cloud as a SaaS model to enable customers to process their data. The consoles are designed to take specialized commands and can even be used from the site’s user interface. Several products or features are part of this package, such as database management systems, identity management systems, cloud-based virtual machines and containers, machine learning capability, and many more.

Big Data often comes from massive, network-based systems. It could be user-generated data or information such as coded numbers or primary data generated from various devices running on networks. The data can be in the form of a standard or a non-standard format. Standard format data is easy to handle, while non-standard data requires some structuring and cleaning before any useful information can be extracted from it. Cloud computing services offer artificial intelligence or machine learning capabilities to standardize non-standard data. From here, other products offered by the services can be used to harness the data and utilize it to glean insights, make projections or search for specifics.

Cloud infrastructures also offer real-time Big Data processing and handle vast volumes of incoming data and interpret it instantly. The tremendous processing capabilities at the disposal of cloud services also enable Big Data analytics to occur in real-time, thus serving a range of industries that use the products every day.

The majority of the market share of cloud-based services that cater to Big Data is taken up these three providers:

Amazon Cloud

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The Amazon cloud, also known as the Amazon Web Services (AWS), offers a range of cloud computing services on demand which can be used as a pay-as-you-go model. AWS is the most widely used cloud platform globally and is the most comprehensive platform. It offers a range of valuable solutions for analysts, developers, and marketers to leverage their big data. The fields in which AWS offers solutions include:

  • Data Ingestion: Data ingestions involve collecting raw data from different sources such as mobile devices, logs, transaction records, and other places. Since the data received every day is massive, the platform can easily handle it.
  • Data Storage: The collected data is stored on the AWS cloud to be retrieved for further use. Companies making use of the storage get a secure, reliable, and scalable storage solution where data can be accessed seamlessly over the network.
  • Data Processing: Data processing is a complicated task that involves various functions such as aggregating, sorting, and combining along with other features. The processed data can be stored for future use as per the need of the client.
  • Visualization: Visualization is the last part of the usage of data sets that were prepared at the processing stage. This is where the actual value of data can be seen literally, as it can be used to gain actionable insights that add value to the companies. Data can be represented in various forms such as charts, maps, graphs, and more.

Microsoft Cloud

The Microsoft Cloud, also known as Azure, offers all the features that AWS offers for Big Data analytics. The eight data analytics options available on it are:

  • Azure Synapse Analytics: Used for data visualizations and analyzing data in the SQL language.
  • Azure Databricks: It is an analytics service based on Apache Spark.
  • Azure HDInsight: Offers extensive data analysis on any volume of data using Hadoop.
  • Azure Data Factory: It is an ETL tool for data processing.
  • Azure Machine Learning: Massive library of machine learning algorithms.
  • Azure Stream Analytics: Based on serverless technology, users can build an end-to-end pipeline for streaming events.
  • Data Lake Analytics: Helps develop data transformation programs.
  • Azure Analysis Services: Indispensable tool for high-performance Business Insights.

Google Cloud

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The Google Cloud, the third most used platform, makes the technology more accessible. The five most deployed solutions in big data include:

  • Business Intelligence: It is a suite of data analytics tools that offers flexibility, scalability, and collaboration to businesses.
  • Data Lake: The data lake is a highly cost-effective and flexible storage facility that stores all the data gathered to be seamlessly retrieved or migrated.
  • Stream Analytics: This powerful tool instantly converts non-standardized data into structured data that is actionable and ready to use.
  • Internet of Things: This IoT product offers tools that help to connect, process, store, and analyze data from IoT sources both outside and within the cloud.
  • Marketing Analytics: This solution allows for integrating marketing tools to the platform as part of marketing support.

Conclusion

To conclude, both Big Data and Cloud Computing have played significant roles in this evolving digital era. The majority of organizations are migrating to cloud services, such as AWS, Azure, GCP, etc., thereby increasing the demand for cloud computing professionals. Due to the emergence of big data analytics, various businesses now find it easier to analyze and utilize data in order to boost their businesses. The organizations adopting these tools can make them stay ahead of the competition.

References:

https://www.computer.org/publications/tech-news/trends/big-data-and-cloud-computing

https://www.simplilearn.com/aws-big-data-article

https://www.dataversity.net/eight-big-data-analytics-options-on-microsoft-azure/#

https://blog.datumize.com/google-has-made-big-data-solutions-more-accessible-than-ever

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