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AWS Data Architect Bootcamp – 43 Services 500 FAQs 20+ Tools Download Now

AWS Data Architect Bootcamp - 43 Services 500 FAQs 20+ Tools

AWS Data Architect Bootcamp - 43 Services 500 FAQs 20+ Tools

AWS Databases, EMR, SageMaker, IoT, Redshift, Glue, QuickSight, RDS, Aurora, DynamoDB, Kinesis, Rekognition & way more

What you’ll study

  • Confidently architect AWS options for Ingestion, Migration, Streaming, Storage, Massive Data, Analytics, Machine Studying, Cognitive Options and extra
  • Be taught the use-instances, integration and price of 40+ AWS Services to design price-financial and environment friendly options for quite a lot of necessities
  • Reply detailed technical questions of your design and growth groups concerning implementation and construct
  • Observe fingers-on labs on complicated AWS companies like IoT, EMR, SageMaker, Redshift, Glue, Comprehend and lots of extra


  • A pc with admin entry, web, and AWS Account to apply labs. Some labs could price $$.
  • Primary working information of AWS like AWS Console, S3, EC2, VPC and related primary ideas.
  • Expertise of working with at the very least one database, primary SQL, conceptual understanding of subjects like replication, streaming, backups, key-worth, index and so on.
  • None of those are present-stoppers. Having this pre-requisite information will make your journey by means of the course smoother with lesser questions


Hello! Welcome to the AWS Data Architect Bootcamp course, the solely course you could study every little thing about knowledge structure on AWS and play the function of an Enterprise Data Architect. That is essentially the most-complete AWS course associated to AWS knowledge structure in the marketplace. Right here’s why:


  • That is the one on-line course taught by an Enterprise Cloud Architect, who leads giant groups of junior architects in the true world, who has an trade expertise of near twenty years within the IT trade, who’s a printed creator, and leads expertise structure of XXX million greenback initiatives on cloud for multi-nationwide purchasers. Data Architects draw a wage within the vary of $150K – $250K on a median. This course trains you for that job! That is my tenth course on Udemy, third on AWS subjects (earlier 2 are finest-sellers).
  • Typical AWS classroom trainings on knowledge structure which accommodates a fraction of the subjects lined on this course, prices $3000 – $5000. And this course teaches you 5 to 7 occasions extra subjects than AWS Coaching (40+ AWS Services) within the fraction of the associated fee.
  • All the things lined on this course is saved newest. Services that are in Beta and launched in Re-invent (final Nov) are already lined within the course . AWS innovates and provides options to their stack very quick, and I hold my course continually up to date with these adjustments. Consider this course as a Structure Updates subscription.
  • Builders have questions, Architect’s have questions, Purchasers have questions – All technical curious minds have questions. And this course additionally has 500+ questions and solutions (FAQs) curated from AWS FAQs, to equip you with as many prepared-to-use solutions as you would wish in your architect function.
  • Your complete course is fashioned of 40+ companies. Each service consists of the beneath listed sections, with their proportion in every part / service.


  • Structure (12%) – Diagrams, Integration, Terminology
  • Use-Circumstances (6%) – Whether or not and When to make use of the AWS Service
  • Pricing (2%) – Value estimation strategies to evaluate total answer price
  • Labs (75%) – To-the-level labs for architectural understanding protecting all main and vital options
  • Steadily Requested Questions (5%) – Chosen query from AWS FAQs defined concisely. (Whole 500+)
  • Aside from AWS Services, we’ll use numerous shopper instruments to function on AWS Services, Databases and different expertise stack. Here’s a record of the instruments that we might be utilizing:

    1. EC2          2. Putty          3. Cloud9,         4. HeidiSQL        5. MySQL Workbench         6. Pgadmin      7. SSMS

    8. Oracle SQL Developer         9. Aginity Workbench for Redshift            10. SQL Workbench / J

    11. WinSCP          12. AWS CLI              13. FoxyProxy            14. Oracle Virtualbox              15. Linux Shell Instructions

    16. FastGlacier              17. Rstudio             18. Redis Consumer               19. Telnet                20. S3 Browser

    21. Juypter Notebooks

    Beneath is an in depth description of the curriculum as AWS Services we shall be studying to grasp how they match within the total cloud knowledge structure on AWS and deal with varied use-instances. You probably have any questions, please don’t hesitate to contact me.

    1. AWS Switch for SFTP (Nov 2018 Launch– We’ll begin our journey on this course with this service and discover ways to ingest information in self-service method utilizing an sFTP server on AWS and sFTP instruments on-premise to ingest file based mostly knowledge on AWS.
    2. AWS Snowball – Giant knowledge volumes spanning tons of of TBs will not be ideally suited for ingestion through community. Utilizing this service, we’ll discover ways to ingest mega quantity knowledge utilizing system based mostly offline knowledge transport mechanism to AWS cloud.
    3. AWS Kinesis Data Firehose – One of many knowledge ingestion mechanism is streaming. We’ll discover ways to channel streamed knowledge from Kinesis Data Streams to AWS Data Storage & Analytics Repositories like S3, Redshift, ElasticSearch and extra utilizing this service.
    4. AWS Kinesis Data Streams – Purchasers can have streaming infrastructure and even units (IoT) which can stream knowledge repeatedly. Utilizing this service we’ll discover ways to accumulate streaming knowledge and retailer it on AWS.
    5. AWS Managed Streaming for Kafka (MSK) (Nov 2018 Launch) – AWS just lately added Kafka to their expertise stack, which has lot of similarities with Kinesis. Be taught comparative options in addition to the tactic of standing up Kafka cluster on AWS to simply accept streaming knowledge in AWS.
    6. AWS Schema Conversion Device – Database migration is a posh course of and might be homogeneous (for ex. SQL Server on-premise to SQL Server on AWS) or heterogeneous ( for ex. MySQL to PostgreSQL). We’ll use this offline instrument to find out about assessing migration complexities, generate migration evaluation studies, and even carry out schema migration.
    7. AWS Database Migration Service (DMS) – Database Migration / Replication is a quite common want for any federated knowledge answer. We’ll use this service to discover ways to migrate and/or replicate on-premise knowledge from databases to AWS hosted relational databases on AWS RDS.
    8. AWS Data Sync (Nov 2018 Launch) – Steady synchronization of information from on-premise to cloud hosted knowledge repositories turns into a key requirement in environments the place knowledge is generated or adjustments very quick. We’ll use to service to study the way it can resolve this requirement.
    9. AWS Storage Gateway – This service has placing resemblance with AWS Data Sync, and is among the alternate options for standing cached volumes and saved volumes on AWS to construct a bridge between on-premise knowledge storage and AWS. We’ll briefly study similarities between AWS Data Sync and AWS Storage Gateway.
    10. AWS ElastiCache ( Memcached ) – After protecting many of the mechanisms of information ingestion, we’ll shift deal with caching knowledge earlier than shifting on the databases. We’ll begin studying about caching with Memcached taste of this service which provides highly effective caching capabilities for easier knowledge sorts.
    11. AWS ElastiCache ( Redis ) – We’ll study comparative distinction between Memcached and Redis for caching, and discover ways to use Redis taste of caching which may construct cache clusters and may host complicated knowledge sorts.
    12. AWS S3 (Superior) – AWS S3 is the premise of information storage and knowledge lake in AWS. We’ll study superior ways like locking knowledge for authorized compliance, cross-area world replication, knowledge querying with S3 Choose function, Life-cycle administration to maneuver knowledge to chilly storage and so on.
    13. AWS Glacier – Data hold accumulating on cloud and may enhance storage prices dramatically. Sometimes used knowledge is appropriate for chilly storage, the place this service comes into play. We’ll studying archival, archive retrieval and archive querying utilizing this service.
    14. AWS Relational Database Service (MariaDB) – We shall be focusing closely on AWS Service, which consists of 6 several types of databases. We’ll study primary ideas of AWS RDS utilizing MariaDB, stand-up an occasion and question it with a shopper instrument.
    15. AWS Relational Database Service (SQL Server) – Data must be imported and exported between knowledge-facilities and cloud hosted database cases. We’ll study such ways for coping with backups and restores throughout cloud utilizing SQL Server database on RDS with a shopper instrument.
    16. AWS Relational Database Service (Oracle) – We’ll spend a while to discover ways to rise up Oracle on AWS RDS, particularly for Oracle professionals.
    17. AWS Relational Database Service (MySQL) – After spending time on working towards primary ideas, with MySQL database on AWS RDS, we’ll begin working towards superior ideas for Excessive-Availability and Efficiency, like Learn Replicas and Efficiency Insights options.
    18. AWS Relational Database Service (PostgreSQL) – There might be use-instances the place there could also be must convert one database to a different on cloud, for instance convert PostgreSQL to MySQL. We’ll find out about some compatibility options the place we will create a MySQL learn reproduction from a PostgreSQL occasion and make a learn reproduction as an unbiased database.
    19. AWS Relational Database Service (Aurora) – Aurora on AWS RDS is a local database service from AWS. It is available in two flavors – cluster hosted and serverless, which is appropriate for various use-instances. Additionally the storage structure of Aurora is shared by varied different AWS companies like AWS Neptune and DocumentDB. We’ll study this service in-depth.
    20. AWS Neptune – Relational databases is simply one of many forms of databases within the trade in addition to on AWS. Graph is particular use-case for very densely related knowledge the place the worth of relationships is far larger than regular. We’ll study graph principle of RDF vs Property Graph, and find out how Neptune matches on this image, stand-up a Neptune Server in addition to shopper, and function on it with question languages like Gremlin ( Tinkerpop ) and SPARQL.
    21. AWS DocumentDB (Nov 2018 Launch) – MongoDB is among the trade chief in NoSQL Doc Databases. AWS has just lately launched this new service which is a local implementation of AWS to supply an equal database with MongoDB compatibility. We’ll study particulars of the identical.
    22. AWS DynamoDB – Key-worth databases are vital for housing voluminous knowledge usually logs, tokens and so on. We’ll study doc database implementation in depth with superior options like streaming, caching, knowledge expiration and extra.
    23. AWS API Gateway – REST APIs are at the moment’s normal mechanism of information ingestion. We’ll discover ways to construct knowledge ingestion and entry pipeline with APIs utilizing this service with AWS DynamoDB.
    24. AWS Lambda – Microservices are sometimes tied with APIs, and are the cornerstone of any programmatic integration with AWS Services, usually AWS’s Synthetic Intelligence and Machine Studying Services. We’ll study creating Lambda features
    25. AWS CloudWatch – System logging is on the middle of all programmatic logic execution, and it ties very intently with microservices and metrics logging for quite a lot of AWS Services. We’ll discover ways to entry and log knowledge from microservices in CloudWatch logs.
    26. AWS Web of Issues (IoT) – Right now IoT is among the quickest rising areas, and from a knowledge perspective, its some of the valued supply of information. The primary problem enterprises section is the mechanism of ingesting knowledge from units after which processing it. With prime deal with ingestion, we’ll discover ways to answer this utilizing an finish-to-finish sensible instance which reads knowledge from a tool and sends textual content messages in your mobile phone.
    27. AWS Data Pipeline – With Data Lakes already overflowing with knowledge, shifting knowledge inside cloud repositories and from on-premises to AWS requires an orchestration engine which may transfer the information round with some processing. We’ll discover ways to resolve this use-case with this service.
    28. Amazon Redshift and Redshift Spectrum – All saved knowledge in relational or non-relational format must be analyzed and warehoused. We’ll discover ways to cater the requirement for a peta-byte scale, massively parallel knowledge warehouse utilizing this service.
    29. AWS ElasticSearch – ElasticSearch is among the market leaders in search framework together with its various Apache Solr. AWS offers its personal managed implementation of ElasticSearch, which can be utilized as one of many choices to look knowledge from completely different repositories. We’ll discover ways to use this service for addressing search use-instances, and perceive how instruments like Logtash and Kibana matches within the total answer.
    30. AWS CloudSearch – Standing up an AWS ElasticSearch wants some ElasticSearch particular understanding. To be used-instances which wants a extra managed answer, AWS offers an alternate packaged answer for search based mostly on Apache Solr. We’ll discover ways to rise up this service and use if for standing up search options in an categorical method.
    31. AWS Elastic MapReduce (EMR) – After spending adequate time on Ingestion, Migration, Storage, Databases, Search and Processing, now we’ll enter the world of Massive Data Analytics the place we’ll spend vital period of time studying standup a Hadoop based mostly cluster and course of knowledge with frameworks like Spark, Hive, Oozie, EMRFS, Tez, Jupyter Notebooks, EMR Notebooks, Dynamic Port Forwarding, RStudio on EMR, Learn and Course of knowledge from S3 in EMR, Combine Glue with Hive, Combine DynamoDB with Hive and way more.
    32. AWS Backup (Nov 2018 Launch) – Creating backup routines of assorted knowledge repositories is a Customary Working Process of manufacturing environments. AWS made this job simpler for assist crew with this model new service. We’ll study in regards to the particulars of this service.
    33. AWS Glue – AWS has centralized Data Cataloging and ETL for any and each knowledge repository in AWS with this service. We’ll discover ways to use options like crawlers, knowledge catalog, serde (serialization de-serialization libraries), Extract-Remodel-Load (ETL) jobs and lots of extra options that addresses quite a lot of use-instances with this service.
    34. AWS Athena – Serverless knowledge lake is fashioned utilizing 4 main companies : S3, Glue, Redshift, Athena and QuickSight. This service is on the tail finish of the method, and acts like a question engine for the information lake. We’ll study the way it serves that goal and completes the image.
    35. AWS QuickSight – AWS stuffed the hole of a cloud-native reporting service in 2017 with the launch of this service. We’ll study the way it matches within the Serverless Data Lake image and permits to create studies and dashboards.
    36. AWS Rekognition – We’ll begin our journey into the world of cognitive companies powered by Synthetic Intelligence with this service. Photos and Video are very important supply of information, and extracting data from these knowledge sources and processing that knowledge in a programmatic method has varied purposes. We’ll discover ways to carry out this integration with Rekognition.
    37. AWS Textract (Nov 2018 Launch) – Optical Character Recognition is one other very important supply of information, for ex. we’re very a lot used to scanning of bar codes, tax types, ebooks and so on. We’ll discover ways to extract textual content from paperwork utilizing this AI powered model new service kind AWS.
    38. AWS Comprehend – Pure Language Processing (NLP) is a really large apply space of information analytics, usually carried out utilizing knowledge science languages like R and Python. AWS makes the job of NLP simpler by wrapping up a AI powered NLP service. We’ll study using this service and perceive the way it enhances companies like Textract and Rekognition.
    39. AWS Transcribe – One main supply of information that we have now not touched to date is Speech to Textual content. We’ll discover ways to use this AP powered service to extract textual content from speech, and the way it may be successfully used for numerous use-instances.
    40. AWS Polly – We’d have lined many use-instances of processing textual knowledge from one kind to a different, however processing textual content to speech, which is the precise reverse operate of Transcribe, we’ll study to carry out that with this AI powered service from AWS. We will even study using Speech Synthesis Language to regulate the small print of the speech that will get generated.
    41. AWS SageMaker – After comfortably utilizing AI powered service, which abstracts the complexity of machine studying fashions from finish-customers, we’ll now enterprise on the planet of machine studying with this service. We’ll execute a machine studying mannequin finish-to-finish and discover ways to entry knowledge from S3, create a mannequin, create notebooks for executing code to discover and course of knowledge, prepare – construct – deploy machine studying mannequin, tune hyper-parameters, and at last accessing it from a load balanced infrastructure utilizing API endpoints.
    42. AWS Personalize – Advice Engines requires constructing a bolstered deep studying neural community. Amazon has been within the enterprise of recommending merchandise to clients since a long time. They’ve packages their methodology of advice as a product and launched it as a service, which is making a debut within the type of Personalize. We’ll carry out an finish-to-finish train to grasp use this service for producing suggestions.
    43. AWS Lake Formation (Nov 2018 Launch) – As forming knowledge lakes is a tedious course of, AWS has introduce a set of orchestration steps within the type of service to expedite the era of Data Lakes. As this service is in early preview (Beta) and is topic to vary, we’ll have a look at a preview of the GUI of this service earlier than concluding the curriculum of this course.

    In case you are unsure whether or not this course is best for you, be happy to drop me a message and I shall be completely happy to reply your query associated to suitability of this course for you. Hope you’ll enroll within the course. I hope to see you quickly within the class !

    Who this course is for:

    • Database professionals who’re beginning new on the AWS platform or need to study quite a lot of AWS Services to widen their information
    • Newbie or Skilled Data Architects who need to enhance the breadth of their information on AWS to start out working on the subsequent stage
    • Expertise Executives who need to rapidly assess the suitability of any given AWS companies for his or her use-instances
    • AWS Professionals who’re getting ready for Massive Data Specialty Certification or getting ready for a technical interview

    Created by Siddharth Mehta
    Final up to date 7/2020

    Dimension: 13.26 GB

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