The gradual progress towards a digital society is also leading to the creation of massive amounts of data. Where does the data go? Nowhere! The data piles up, and recently the rate of growth in data is increasing exponentially. Traditional analytical tools fail to cope up with such large volumes of data that also present challenges of complexity. Therefore, solutions such as AWS Big Data come to the picture for bridging the gap between data creation and efficient data analysis.
Big data tools and technologies provide multiple opportunities alongside challenges for efficient data analysis. The need for data analysis is evident in the benefit of better understanding regarding customer preferences and gaining a competitive advantage. The candidates aspiring to build a career in Big Data keen to get the AWS Data Analytics certification to bring their career one level up.
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Data management frameworks have come a long way from the conventional data warehousing models to complex frameworks. The contemporary applications of data management frameworks include real-time and batch processing as well as high-velocity transactions. The following discussion would focus on the advantages of using AWS and Big data. The discussion would also reflect briefly on the different AWS tools that help in realizing big data objectives.
Big data on AWS
AWS provides various managed services for assistance in building, security, and seamless scaling of end-to-end big data applications. The speed and ease of development is a prominent advantage of AWS big data. Applications could have various requirements, such as batch data processing and real-time streaming. However, AWS provides all the necessary infrastructure and tools for addressing big data projects.
Furthermore, AWS does not imply the need for any hardware or maintenance and scaling of infrastructure. Furthermore, the wide range of analytical solutions with AWS provides an added advantage inherently through their design. So, what more advantages do AWS and Big data offer for businesses? The response to this question can develop the foundation for this guide for working on Big Data with AWS.
The analysis of large volumes of data could demand substantial compute capacity. Furthermore, the compute capacity would also vary based on the amount of input data alongside the type of analysis. Therefore, big data workloads on AWS follow the pay-as-you-go model that is the rationale of cloud computing.
Scalability upon demand is not an issue with AWS Big Data Services. You don’t have to wait for additional hardware or investments in the improvement of computing capacity. The scaling on AWS does not take substantial time and also provides optimal efficiency, thereby ensuring the productivity of working with Big data on AWS.
Furthermore, the availability of resources is never an issue with the diverse Availability Zones by AWS. Furthermore, services such as Amazon S3 (Simple Storage Service) can help in the storage of data while AWS Glue can help in orchestration. The next important service with AWS Big data is the transfer of data to the cloud as it increases gradually.
Furthermore, the use of Big Data services on AWS also involves the collection of data regarding mobile app usage. All these capabilities show how Big data with Amazon Web Services can be very productive. So, the next agenda on our discussion would be the different services on AWS for collection, processing, storage, and analysis of Big Data.
The first among AWS Big Data services is Amazon Kinesis, which is an ideal platform for streaming data on AWS. Therefore, it provides the option for building custom streaming data applications for specific needs. Kinesis can help in entering real-time data like application logs into databases, data warehouses, or data lakes.
Subsequently, The AWS Big data functionalities of Kinesis are evident in building real-time applications using data collected by Kinesis. The real-time processing capabilities of Kinesis show that you can start processing and analysis of data before data collection is complete.
Another Amazon Big data service is AWS Lambda. AWS Lambda helps in running code without the need for server provisioning or management. Users have to pay only for the compute time they use, and there is no charge for the time when code does not run. The use of Lambda helps in running code on almost any type of application or backend service without any administration.
All you have to do is upload the code, and Lambda takes care of the rest. The functionalities of Lambda are evident in triggers by other AWS services. The use of Lambda in the AWS big data landscape involves prominent references to real-time file and stream processing and processing of AWS events.
The next prominent entry among Amazon Big data services to work with big data on AWS is Amazon EMR. It is a highly distributed computing framework. The applications of Amazon EMR are evident in easier processing and storage of data with better speed and cost-effectiveness.
Amazon EMR leverages the open-source framework, Apache Hadoop, for the distribution of data and processing. EMR also helps in using general Hadoop tools such as Hive, Spark, and others. EMR provides the perfect instrument for using Big data with AWS through support for running big data processing and analytics.
The interesting factor, in this case, refers directly to the provisioning, management, and maintenance of infrastructure and software in the Hadoop cluster. The primary applications of Amazon EMR include log processing and analytics, genomics, predictive analytics, ad targeting analysis, and threat analytics.
Amazon KMS (AWS Key Management) is a managed service that is integrated with various other AWS Services. You can use it in your applications to create, store and control encryption keys to encrypt your data. Learn AWS KMS Key Management Service.
The next entrant among reliable AWS Big data tools is AWS Glue that is a fully managed ETL service. ETL implies extraction, transformation, and loading, and it is ideal for the classification of data. Furthermore, it also helps in refining the data, improvise it, and ensure its migration between data stores with security. AWS Glue can help in significant reductions in cost, time, and complexity for the creation of ETL jobs.
Since Glue does not depend on servers, the burden of setting up and managing infrastructure is null. AWS Glue provides automatic data crawling, thereby generating code for execution, data transformation, and loading processes. It also integrates effectively with other AWS services like Athena, RedShift, and EMR, thereby providing the flexibility of use. The ETL code developed in AWS Glue is highly customizable and is portable, as well as reusable.
Amazon Machine Learning
Well, this is probably the winner among all AWS Big Data tools. The Amazon Machine Learning service helps in easier use of machine-learning technology and predictive analytics. Amazon ML can provide exceptional visualization tools and wizards for guidance on the process of creating machine learning models. After the preparation of machine learning models, Amazon ML provides the ease of obtaining predictions for an application through API operations.
The good thing here is that you don’t have to implement any custom code for generating predictions. Also, you don’t have to deal with infrastructure management. Amazon ML provides effective use of Big Data with Amazon Web Services through features for creating ML models from data on Amazon S3, RedShift, or RDS. The potential benefit of Amazon ML is the availability of built-in wizards that can help in interactive data exploration.
Also, Amazon ML can help in training the ML model alongside the evaluation of the model quality and modification of outputs for alignment with business goals. Once a model is ready, users could request predictions through the real-time API or in batches. The applications of Amazon Machine learning help in discovering various patterns in your data.
As a result, users can create machine learning models that help in deriving predictions from new datasets. For example, it helps applications to identify and provide notifications about suspicious transactions. The other uses of Amazon ML in the context of Big data include personalization of application content, user activity prediction, social media listening, and product demand forecasts.
The other notable AWS big data tools which can contribute to the effective use of Big data on AWS are as follows.
- Amazon DynamoDB
- Amazon Elasticsearch Service
- Amazon Redshift
- Amazon Athena
- Amazon QuickSight
All of these services have unique applications concerning the use of Big data on AWS. For example, DynamoDB provides a NoSQL database service for cost-effective and simpler storage and retrieval of data. The applications of Amazon Redshift include prominent references to online analytical processing through existing business intelligence tools.
The uses of Redshift include prominent references to the analysis of global sales data, social trends analysis, or storage of historical stock trade data. Subsequently, the Amazon Elasticsearch Service helps in querying and searching large amounts of data. The uses of Amazon ES include analysis of activity logs and analysis of data stream updates from other AWS services. Amazon QuickSight provides the advantage of business intelligence functionality through creating visualizations to obtain insights on data.
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Based on the observations from the discussion mentioned above, AWS big data seems readymade for users. It’s like you don’t have to do anything with everything served right at your table. You need to look for diverse opportunities in using big data to your advantage with the unique functionalities of AWS.
The different AWS tools and services that help in achieving big data functionalities would require comprehensive training. If you want to put your first step in Big data on AWS, then get a free tier account on AWS. Try out the different services outlined in this discussion and then witness their functionalities on your own. As they say, learning and practice lead to perfection!
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