AWS Data Lake Fast Track - Build in a day with source code
What you'll learn
- An accelerated approach for building a foundational data lake and techniques for customizing it
- Architecture, Internals of solution components and operational details of the solution
- Solution details including source code that can be used to readily deploy the foundational AWS Data Lake
- Implementation details including user-authentication, front-end, storage, apis, micro-services, flexible configuration, auditing and more
Requirements
- Working knowledge of Cognito, S3, CloudFront, DynamoDB, API Gateway, Lambda, Glue and Elasticsearch
- An AWS Account to deploy the solution and execute labs
- Some basic experience of working with a mix of application and data related services in AWS.
Description
In the AWS Data space, arguably AWS Data Lake is the corner stone of any AWS Data Architecture. Implementing a data lake requires many components working in an integrated manner like the ones mentioned below:
End-user authentication
Front-end for end-user to interact with the data lake
Storage repositories to store structured, semi-structured and unstructured data
APIs and microservices for a service based architecture
Logging and Auditing
Integrated Metadata Tracking and Search
And many other components
Typically to respond to RFPs / RFIs, developing prototypes, validating the foundation architecture, or accelerating the initialization of the data lake build, often a lot of time is spent on the initial setup. Experienced architects often use templates and techniques from their experience, to stand-up data lakes foundation in an accelerated manner that can be extended or customized by different teams working to build an integrated data lake.
This course covers one such approach to accelerate building your data lakes, such that it can be stood up in a day. The course explains the architecture, internals of the services used, and shows step by step how to work with the solution. In addition, the entire source code is available too, so that you can customize the solution as per your needs.
You can be a star in your team by single-handedly setting up the data lake foundation in a day by learning the approach from this course and rally your team to start customizing the source code as well ! This course does not need you to spend hours and hours to learn the approach. Assuming you already work with AWS on data services, within 2 hours you will learn the approach in detail, get access to source code, deploy the solution by the end of the day, and enable working basic data lake on your AWS account. All this in just a Day !
I hope your would enroll in the course and hope to see you soon in the class !
Who this course is for:
- AWS Professionals seeking to launch a extensible foundational data lake instead of building it from scratch
- Experienced AWS Data Developers and Architects
- Course is NOT recommended for Beginners in AWS
Course content
- Preview01:40
- Preview04:15
- Preview03:02
Instructor
![Siddharth Mehta](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.2 Instructor Rating
- 5,204 Reviews
- 42,536 Students
- 11 Courses
Udemy's Top 10% of most engaging instructors
My name is Siddharth Mehta. I have career experience of more than 15 years in the IT Industry and am presently working as Enterprise Cloud Architect. I am published author on many online and print-media publications. I have taught thousands of students on Udemy and have number of courses on Data and Analytics.
Would you consider learning from just any hobbyist who knows programming or someone who just teaches programming without practically using it in the real world, or someone who has experience of using the technology in real world on multi-million dollar large-scale projects globally ? I will teach you everything I know about the subject, from my years of practical experience in the field of BI, Data, Analytics, Cloud and Data Science.
If you are interested in learning more about me, below are some of my career highlights:
I have career experience of more than 15+ years and am presently working in New York Metro region as Enterprise Architect for a life-sciences proprietary multi-tenant product technology portfolio, managing an ecosystem of ISVs and tenants. Below are some of my career highlights:
-|- International experience of working across geographies (US, UK, Singapore) for multi-national clients in Banking, Logistics, Government, Media Entertainment, Products, Life Sciences and other domains
-|- Lead architecture of multi-million dollar portfolios containing apps in Cloud, web, mobile, BI, Analytics, Data warehousing, Reporting, Collaboration, CMS, NoSQL and other categories.
-|- Official inventor of a patented application
-|- Published author/reviewer of whitepapers for Microsoft MSDN Library, Manning publication, Packt publication and others.
-|- Certifications: AWS Certified Solution Architect, TOGAF 9, CITA-F, HCAHD and more
In my present role, I remain responsible for Estimations like AO, IO, SI, IC & Security, Architecture Design, Technology Stack selection, Infra design, 3rd party products evaluation and procurement, and Performance engineering. Hands-On Technology experience of below tech:
-|- OS: Win, Linux
-|- Cloud: GCP, Azure, AWS
-|- Databases: Neo4j, AWS Neptune, Redis, Memcached, MongoDB, Cassandra, HBase, SQL Server, MariaDB, Postgres, Aurora, MySQL, SSAS, AWS Redshift, Google BigQuery, Azure Data Lake, AWS RDS, DynamoDB, Athena, AWS Elasticache
-|- Big Data: Google DataProc, AWS EMR, Kafka, Spark, Hive, Oozie
-|- Search: AWS Elasticsearch
-|- Web: Node.JS, Angular, jQuery, REST APIs, React
-|- ESB/ETL: AWS Lambda, Step Functions, AWS Kinesis, AWS Glue, Mulesoft, SSIS, AWS Data pipeline
-|- Data Science: R , Python, GGPlot 2, Numpy, Seaborn, Pandas, Skikit-learn, Spark ML, Data Mining, Regression & Classification algorithms
-|- Reports / Dashboards : Tableau, Qlikview, SSRS, AWS Quicksight, D3