AWS provides a large selection of analytics services that fit many of your data analytics needs and enable organizations to reinvent their business with data. From data movement, data storage, data lakes, big data analytics, and machine learning (ML) to anything in between, AWS offers purpose-built services that provide the best price-performance, scalability, and lowest cost.
Data Lake Analytics on AWS
Data Lakes are easily accessible online data lake platforms that provide a single, integrated source for data collection and processing. These data lake platforms enable organizations to quickly ingest and organize data into new or existing analytical products by combining Amazon EC2 and Amazon Kinesis.
AWS also provides many other tools and services that you can apply to your data in your Data Lakes, including Kafka, Storm, Amazon Elastic MapReduce (EMR), Amazon S3, Amazon Aurora, Amazon Elastic Compute Cloud (EC2), Amazon DynamoDB, Amazon EMR, Amazon Redshift, Amazon Redshift Data Warehouse, Amazon Aurora, Amazon Kinesis, Amazon Solr, and Amazon Elastic Block Store.
ML on AWS
Amazon SageMaker for Machine Learning is a dedicated machine learning platform on AWS that will simplify creating and training machine learning models. SageMaker combines a self-service deployment platform with an elastic self-service data management system.
Amazon EMR is a fully managed, GPU-optimized, relational database service that provides scale and performance for millions of rows of SQL and Hadoop data, making it easy to run data science and analytics solutions.
Amazon Simple Workload Service (AWS S3) is the most widely used cloud storage service globally, as it provides petabyte-scale Storagestorage with unmatched reliability.
Event Streaming on AWS
Amazon SNS (Simple Queue Service) is a fully managed messaging and coordination service designed for web and mobile developers and system integrators. Amazon SNS makes it easy to manage notifications between systems and provides a set of convenient task-handling APIs, as well as SNS Publish/Subscribe and simple scheduling APIs.
Amazon Simple Notification Service (Amazon SNS) is a fully managed messaging and coordination service designed for web and mobile developers and system integrators. Amazon SNS makes it easy to manage notifications between systems and provides a set of convenient task-handling APIs, as well as SNS Publish/Subscribe and simple scheduling APIs. Amazon Kinesis Streams is an Amazon cloud data processing platform that provides real-time processing, streaming, and batch processing in massively scalable systems, including GPU-accelerated processing, globally distributed batch jobs, and messaging.
Amazon Elastic MapReduce (Amazon EC2) is the most widely used Hadoop-based HPC platform globally, with over 50,000 instances in production in over 1,600 data centers. Amazon EC2 provides:
- Guaranteed resource availability.
- Easy scaling.
- Performance and control.
- Automatic hot-spot managing in a fully managed fashion.
Amazon SQS (Simple Queue Service) is a massively scalable, global asynchronous messaging queue for inter-process communication across distributed systems. Amazon SQS provides guaranteed resource availability, fast failover, and guaranteed delivery, as well as easy scaling and control.
Amazon SQS provides guaranteed resource availability, fast failover, and guaranteed delivery, as well as easy scaling and control. Amazon DynamoDB is a highly available in-memory data storage service that provides a highly durable, indexable, and distributed database that supports billions of writes and hundreds of thousands of reads in terabytes per day. DynamoDB is optimized for scale and is cloud-native, and can quickly scale across the Amazon Web Services (AWS) region, Amazon AWS East (N. Virginia), Amazon AWS West (Oregon), or Amazon AWS Government (Washington).
Working With Redshift
Amazon Redshift is a fully managed, cloud-based data warehouse service for storing, processing, analyzing, and joining large volumes of data. With its built-in columnar Storagestorage, Redshift data is durable across multiple writes. With integrated analytical capabilities, data is stored using standard data structures, which reduces the number of data components required to process the data.
Amazon Glacier is the most widely used cloud storage service globally, with more than 10 billion data objects in production and more than 15,000 customers in 20 countries. With the cloud’s ability to deliver petabytes-scale, Amazon Glacier provides in-transaction with guaranteed availability, unlimited sustained volume storage, and protected data in-transaction with Amazon Elastic Block Store (EBS) snapshotting protects data at rest or in-transaction from accidental or planned data breaches.
Amazon Simple Storage Service (Amazon S3)
Amazon Simple Storage Service (Amazon S3) is the most widely used cloud storage for scalable, high-performance online data storage. Amazon S3 offers customers global archiving, disaster recovery, data warehousing, data processing, backups, and copy-on-write data storage to meet any requirement. Amazon S3 is highly available in-region, with 99.999999999 percent availability across all Amazon Regions.
Amazon CloudFront provides scalable, high-performance content distribution services that are massively parallel. It is one of the most widely used Internet content distribution services and supports millions of simultaneous content requests. Amazon CloudFront uses a robust network infrastructure to deliver content directly to visitors from remote data centers without intermediaries. It is also available as a service from Amazon’s Elastic Compute Cloud (EC2) instances or via the API.
Using AWS Data Lake and Storage Together
The first step of a data lake’s creation is to define the underlying architecture. The data lake must be constructed so that the data can persist in a database without deduplication. When data is stored as a table-based structure in a relational database, data linearly moves from the table storage tier to the data ingestion layer (e.g., OData) and eventually to the relational database storage tier. Not all data should be stored in tables. Table storage is an excellent way to organize the initial, fragile, and fluctuating data but does not support large volumes of history. It does not address unstructured data and is not ideal for data representing knowledge or knowledge systems.
The data lake management platform should also identify the existing systems that will store and integrate new data into the data lake.
Machine Learning Integration on AWS
Using AWS, data scientists and developers can connect their machine learning models with the wide variety of Hadoop and big data tools, data connectors, and ETL tools available on AWS. A data scientist can build and deploy machine learning models on AWS by leveraging the complete set of Hadoop distributions, including Spark, HBase, and Hive.
By using the AWS Machine Learning Interface, developers can:
- Develop a model from an AWS elastic machine learning cluster
- Build an AWS-connected machine learning model
- Deploy an Amazon Machine Learning Engine from a pre-trained model
- Run machine learning workloads on AWS
Amazon Elastic MapReduce (Amazon EMR)
Amazon Elastic MapReduce (Amazon EMR) is an Amazon EC2 service that provides highly parallel distributed computing infrastructure for data analytics and machine learning. Amazon EMR is a cluster computing environment that supports major parallel programming languages, including Scala, Clojure, Haskell, Python, and R.
Amazon EMR is optimized for distributed, parallel computing and allows you to configure clusters to perform application analysis and machine learning.
The Amazon EMR client interface is a single-page web application that allows users to map a cluster configuration file to Amazon EC2 instances or S3 storage. Clustering is configured by using user-defined commands or via client-side components of an Amazon Machine Learning Agent. Amazon EMR clusters are secured using a top-level Amazon EMR cluster security group and assigned an instance type in the Amazon EC2 Key Management Service.
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With all of the different tools available for data scientists and developers to get started with machine learning, organizations can rapidly get to the point where they are genuinely leveraging machine learning and not just hoping it’s going to work. The same tools used to analyze data from the Internet of Things and mobile devices can also be leveraged to extract meaning from structured, unstructured data sources. Ultimately, businesses should look to use machine learning to handle all the large volumes of data they have, both inside and outside of their organizations.
Simplilearn offers courses to help you deepen your understanding of data lakes and your skills in machine learning. The Data Engineering Certification Program in partnership with Purdue University covers managing Big Data, including data lakes, on AWS. The AIML Course and the Deep Learning Certification Training with Keras and TensorFlow will cover skills you need to use ML to manage Big Data analytics.