AWS Big Data: Options You Should Consider

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What is AWS Big Data?

By “big data” in AWS, we mean the gathering, storing, and analyzing of massive amounts of information in Amazon Web Services. Analytics, highly scalable storage, and extensive support for compliance regulations are just some of the services and capabilities it relies on.

This is just one installment in a comprehensive data security manual series.

What you’ll pick up from this article is:

  • 6 Big Data Analytics Options on AWS
    • Amazon Kinesis
    • Amazon EMR
    • Amazon Glue
    • Amazon Machine Learning (Amazon ML)
    • Amazon Redshift
    • Amazon QuickSight
  • To what end do AWS’s Big Data services contribute?
    • Collection
    • Storage
    • Processing and Analysis
    • Consumption and Visualization
  • ONTAP Cloud Volumes on Amazon Web Services for Analyzing Large Data Sets

Six Amazon Web Services for Big Data Analyses

The analytics solutions offered by AWS are the most impressive part of their support for big data implementations. The vendor provides a wide range of tools for automated data analysis, data manipulation, and knowledge extraction.

Amazon Kinesis

Using Kinesis, you can gather and examine streaming data in real time. IoT telemetry data, website clickstreams, and application logs are all examples of the types of streams that can be processed. Information gathered by Kinesis can be sent to other Amazon Web Services (AWS) like Redshift, Lambda, Elastic MapReduce (Amazon EMR), and S3 storage.

The Kinesis Client Library can be used to create bespoke streaming data applications (KCL). This library helps you make use of dynamic content, generate alerts, and display data in real time dashboards.

Amazon EMR

With EMR, you have access to a scalable and reliable distributed computing platform for managing and storing your data. Apache Hadoop and a group of EC2 nodes form the basis of this system. To process and analyze large amounts of data, Hadoop is a tried and true framework.

By taking care of the Hadoop infrastructure provisioning, management, and maintenance, EMR frees you to focus on analytics. Spark, Pig, and Hive are just a few of the most popular Hadoop tools, and EMR supports them all.

Amazon Glue

A data processing and ETL (extract, transform, and load) service, Glue is at your disposal. It can help you organize your data and move it around between different databases with ease. You pay only for the resources you use with Glue, and there’s no need to worry about setting up servers or other infrastructure because it’s a serverless service.

Amazon Machine Learning (Amazon ML)

The Amazon Machine Learning service helps those without ML knowledge create machine learning models. It has built-in wizards, visualization tools, and example models to speed up the learning curve. The service will assist you in assessing data for training and in tailoring your trained model to your company’s specific requirements. After finishing your model, you can get at the results via batch exports or an API.

Amazon Redshift

Redshift is a service that provides a fully managed data warehouse, which can be used for BI analysis. It excels at processing SQL queries over sizable datasets, whether those datasets are fully or partially structured. Several analytics services, including SageMaker, Athena, and EMR, can consume the information that has been gathered from a query and stored in S3 data lake storage.

Spectrum, a feature built into Redshift, lets you query S3 data without resorting to ETL procedures. This function analyzes your data storage and needs for the query, then adjusts the procedure to read as little as possible from S3. This reduces expenses and shortens the time it takes to run queries.

Amazon QuickSight

QuickSight is an ad-hoc data analysis and visualization service for business analytics. It can import information from a wide variety of on-premises databases, Excel or CSV files, and Amazon Web Services (AWS) services like S3, RDS, and Redshift.

QuickSight’s “super-fast, parallel, in-memory calculation engine” (SPICE). This engine makes use of machine code generation to produce interactive queries based on columnar storage. In order to make future queries as fast as possible, the engine stores the results of each one until they are explicitly deleted by the user.

To what end do AWS’s Big Data services contribute?

Your entire big data management life cycle can be addressed with the help of AWS’s many available services. The collection, storage, and analysis of your data sets is now feasible and inexpensive thanks to these tools and technologies. The available tools facilitate the entire lifecycle of big data, from collection to analysis to consumption.


Data collection services aid in the gathering of both structured and unstructured data. Whether a solution is built for native AWS integration or for importing data from exports, either option is viable.

The following services and capabilities are available in AWS to aid in big data collection:

  • Ingesting real-time data streams with Kinesis Streams and Kinesis Firehose
    Manual import and API integration with a variety of services and data sources


The storage of large amounts of data necessitates highly scalable solutions that can manage data both before and after processing. These solutions are typically tiered to help you decrease storage costs, and they are available to a wide range of processing and analytics services.

Services in AWS that aid in big data storage include:

  • When storing objects, S3 and Lake Formation are both good options.
    For storage and backup purposes, we use S3 Glacier and Backup.
    Binding and Lake-forming for Data Exchange of External Data

Processing and Analysis

With the help of processing and analysis tools, raw data can be converted into information suitable for analytical use. This usually involves applying new data schemas or translating data into different formats, but it can also include sorting, aggregating, and joining data.

A variety of services in AWS help with processing and analysis, such as:

  • Services in Elasticsearch for Use in Enterprise Analytics
  • Athena: Dynamic Analytic Engine
  • Using Redshift as a data warehouse
  • Processing Massive Amounts of Data with Electronic Medical Records
  • In-Stream Analytics with Kinesis

Traditional databases are used for data processing by some AWS big data projects. Examine our AWS MySQL, AWS Oracle, and AWS SQL Server articles for more information.

Ingestion and Pictorial Representation

Solutions for data consumption and visualization aid in the discovery and dissemination of insights. These tools let you sift through your data and find the insights and analysis that will help you make the best decisions.

AWS provides the following services to aid in the consumption and visualization of big data:

  • Rapid insight for data displays and monitoring
    Sagemaker, a machine learning and analytics platform, and Deep Learning Application Management Interfaces

ONTAP Cloud Volumes on Amazon Web Services for Analyzing Large Data Sets

The most trusted name in enterprise-grade storage management, NetApp Cloud Volumes ONTAP, now offers its secure, tried-and-true storage management services on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). High availability, data protection, storage efficiencies, Kubernetes integration, and more are just some of the features that make Cloud Volumes ONTAP a great choice for any enterprise workload, including file services, databases, DevOps, and more.

To be more specific, Cloud Volumes ONTAP helps bridge the gap between your cloud-based database’s capabilities and the public cloud resources it runs on, which is a common problem for cloud-based workloads.

Advanced features for managing SAN storage in the cloud are supported by Cloud Volumes ONTAP, including support for NoSQL databases and NFS shares that can be accessed directly from cloud big data analytics clusters.

There is a direct correlation between the storage efficiency features built into NoSQL and the low cost with which it can be deployed in the cloud. Thanks to tools like snapshots and data cloning, NoSQL database administrators and big data engineers can confidently handle massive amounts of data.

Find Out More About Amazon Web Services’ Big Data Offering

AWS Big Data Options You Should Consider

Cloud-Based, End-to-End Workflow with AWS Data Lake

A data lake is a scalable, low-cost data storage solution that can accommodate massive amounts of both structured and unstructured information. Businesses can keep data in its raw form and then search and analyze it as needed without having to worry about converting it first. Discover how Data Lake Formation, Glue, Lambda, Elastic MapReduce, and other AWS services can automate your data lake from ingestion to analysis.

Data Analytics on AWS: Deciding Which Is Right for You

By streamlining data storage, cataloging, searching, and analysis, big data solutions benefit businesses. There is a wide variety of services available through AWS, each with its own set of advantages. Common AWS Data Analytics services are described, and evaluation questions are included.

ElastiCache on Amazon Web Services (AWS) and the AWS Redis Service

Redis is an open-source database and caching technology that is gaining in popularity among enterprise DevOps deployments, and AWS ElastiCache for Redis is the fully managed service for it. Engineers deploying Redis clusters on AWS can use AWS ElastiCache for Redis to efficiently manage the clusters, including monitoring, maintenance, backing up data, recovering from failures, and updating software, all at a reduced operational cost. Here we’ll examine Redis and AWS ElastiCache for Redis in greater detail, providing a comprehensive step-by-step guide to getting started with both as integral components of an AWS database deployment.

Managed Service vs. Self-Managed for MongoDB on Amazon Web Services

When it comes to big data workloads on AWS, the NoSQL database MongoDB can be a crucial enabler. While AWS provides a managed service that is compatible with MongoDB (Amazon DocumentDB), users also have the option of self-managing their MongoDB database by constructing it on native AWS EC2 compute instances.

This post examines the differences between Amazon’s managed DocumentDB service and the self-managed, EC2-based MongoDB deployment option, and demonstrates how NetApp’s Cloud Volumes ONTAP data management platform can bridge these gaps and improve MongoDB on AWS deployments.

AWS Elasticsearch: 5 Lessons Learned from Putting the Popular Analytics Engine into Production

This article details the author’s experience putting Amazon Elasticsearch, a fully managed Elasticsearch service, into production, and offers five takeaways useful for anyone considering using the service for the first time. Examine the costs and performance of the AWS managed service in comparison to the open-source version, as well as the operational and management overhead effort that is involved with both.

Managed Service vs. Self-Managed Deployment of Cassandra on Amazon Web Services.

Originally developed by Facebook to search through messages in users’ inboxes, Apache Cassandra is now a popular open-source NoSQL database with a strong focus on performance and availability. How will this change impact your large-scale data processing tasks on AWS?

Determining between Amazon’s managed Cassandra service (Amazon Keyspaces) and deploying your own Cassandra database on AWS-native EC2 instances is a key part of answering that question. Read this article to learn about the benefits of Cloud Volumes ONTAP and the drawbacks of each method.

What You Can Do, How You Should Do It, and What Works Best with the AWS Snowball Bundle

The AWS Snowball is a tool in the AWS Snow Family that can be used for edge computing, edge storage, and data migration. Find out how the AWS Snowball Family can help you succeed, what choices you have for Snowball devices right now, what to expect from the data import and export process, and more.

Amazon Web Services (AWS) Snowmobile, the largest hard drive in the world, for data migration.

If you need to upload an enormous amount of data to the AWS cloud, you can use the Exabyte-scale data transfer service known as AWS Snowmobile. A semi-trailer truck pulls a shipping container called a Snowmobile, which is 45 feet long. Up to 100 petabytes of data can be transferred with each Snowmobile.

Data Shipping and Local Computing with AWS Snowball Edge

AWS Big Data Options You Should Consider

The AWS Snowball service manages the AWS Snowball Edge, a data transfer and edge computing device that includes both compute and on-board storage. Find out how the AWS Snowball Edge device can facilitate the transfer of massive amounts of data to the cloud and the execution of compute operations in disconnected edge locations.

Data Migration Options on Amazon Web Services: Snowball vs. Snowmobile

Use the storage and computing power of AWS in edge environments with the help of AWS Snowball, a line of secure, rugged devices offered by AWS. In order to move data at the exabyte scale, you can use AWS Snowmobile. Learn how they vary and make an informed decision about which method of data migration is best for you.