AWS Migration/Transfer, Analytics and Machine Learning Services for Cloud/DevOps Engineers

📝Introduction

In this post, we will cover the main Technologies from AWS Migration/Transfer, Analytics and Machine Learning Services.

📝Migration and Transfer Services

• AWS Data Migration Service (DMS) -> It is a managed migration and replication service that helps move your database and analytics workloads to AWS quickly, securely, and with minimal downtime and zero data loss.

  • Supports homogeneous and heterogeneous database and analytics engines migration (i.e. Oracle to Amazon Aurora MySQL-Compatible Edition, MySQL to Amazon RDS for MySQL, etc)

  • Multi-ZA data replication and monitoring

  • Low cost to migrate Terabyte-sized databases

  • Automated migration

  • DMS in the Real World Scenarios:

    • Migrate an on-premises Oracle database to Aurora MySQL

    • Migrate an on-premises Oracle database to Oracle on EC2

    • Migrate an RDS Oracle database to Aurora MySQL

      

• AWS Server Migration Service (SMS) -> It allows you to migrate on-premises servers to AWS.

  • Migrates on-premises servers to AWS

  • Server saved as a new Amazon Machine Image (AMI)

  • Use AMI to launch servers as EC2 instances

• AWS Snow Family -> It allows you to transfer large amounts of on-premises data to AWS using a physical device.

  • Snowcone

    • The smallest member of the data transport device.

    • 8 terabytes of usable storage

    • Offline shipping

    • Online with DataSync

      

  • Snowball and Snowball Edge

    • Petabyte-scale data transport solution

    • Transfer data in and out

    • Cheaper than Internet transfer

    • Snowball Edge supports EC2 and Lambda

      

  • Snowmobile

    • Multi-petabyte or exabyte scale

    • Data loaded to S3

    • Securely transported

      

• AWS DataSync -> It allows for online data transfer from on-premises to AWS storage services like S3 or EFS.

  • Migrates data from on-premises to AWS

  • Copy data over Direct Connect or the internet

  • Copy data between AWS storage services

  • Replicate data cross-Region or cross-account

• Data Warehouse -> It is a data storage solution that aggregates massive amounts of historical data from disparate sources.

  • Supports querying, reporting, analytics, and business intelligence. They are not used for transaction processing.

  • AWS has specific Analytics Services focused on Data Warehouse that we will check below.

• AWS RedShift -> It is a scalable data warehouse solution.

  • Improves speed and efficiency

  • Handles exabyte-scale data

  • RedShift in the Real World Scenarios:

    • Data Consolidation when you need to consolidate multiple data sources for reporting

    • Relational DBs when you want to run a database that doesn't require real-time transaction processing (insert, update, and delete)

      

📝Analytics

• Analytics -> It is the act of querying or processing your data in real time.

• AWS Athena -> It is a query service for Amazon S3.

  • Analyze S3 data using SQL

  • Pay based on data processed or compute used

  • Considered Serverless

• AWS Glue -> It prepares your data for analytics.

  • Extract, transform, load (ETL) service

  • Prepare and load data

  • Helps to better understand your data

• AWS Kinesis -> It allows you to analyze data and video streams in real time.

  • Supports video, audio, application logs, website clickstreams, and IoT

• AWS Elastic MapReduce(EMR) -> It helps you process large amounts of data.

  • Analyze data using Hadoop

  • Works with big data frameworks

• AWS DataPipeline -> It helps you move data between compute and storage services running either on AWS or on-premises.

  • Moves data at specific intervals

  • Moves data based on conditions

  • Sends notifications on success or failure

• AWS QuickSight -> It helps you visualize your data.

  • Build interactive dashboards

  • Embed dashboards in your applications

• Analytics in the Real World Scenarios:

  • Search Data in S3, you can use Athena to help you query historical data stored in S3 as if they were relational data using standard SQL

  • Log Analytics, you can use Kinesis to help you analyze logs in near

    real-time for application monitoring or fraud detection

    

📝Machine Learning

• Machine Learning -> Businesses leverage AI and Machine Learning to add intelligence to their applications and leverage trends and patterns in data.

• AWS Rekognition -> It allows you to automate your image and video analysis.

  • Identify custom labels in images and videos

  • Face and text detection in images and videos

  • Rekognition in the Real World Scenarios:

    • Use facial comparison and analysis in your user onboarding and authentication workflows to remotely verify the identity of opted-in users.

• AWS Comprehend -> It is a natural-language processing (NLP) service that finds relationships in a text.

  • Natural-language processing (NLP) service

  • Uncovers insights and relationships

    • Comprehend in the Real World Scenarios:

      • Index and search product reviews focused on context by equipping your search engine to index key phrases, entities, and sentiment, not just keywords.

• AWS Polly -> It turns text into speech.

  • Mimics natural-sounding human speech

  • Several voices across many languages

  • Can create a custom voice

    • Polly in the Real World Scenarios:

      • Add complementary audio, converting a text to a blog post to speech.

• AWS SageMaker -> It helps you build, train, and deploy machine learning models quickly.

  • Prepare data for models

  • Provides Deep Learning AMIs

    • SageMaker in the Real World Scenarios:

      • Recommendation Engine, Netflix and Amazon use machine learning models to recommend movies and products to buy.

• AWS Translate -> It provides language translation.

  • Provides real-time and batch language translation

  • Supports many languages

  • Translates many content formats

    • Translate in the Real World Scenarios:

      • Add localization to websites or applications, to support your diverse user base. Translate supports several popular languages.

• AWS Lex -> It helps you build conversational interfaces like chatbots.

  • Recognizes speech and understands language

  • Build highly engaging chatbots

  • Powers Amazon Alexa

    • Lex in the Real World Scenarios:

      • Integrate voice into a device, Amazon used the same technologies that power Lex to integrate Amazon Alexa with the Echo device.

Thank you for reading. I hope you were able to understand and learn something helpful from my blog.

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