verified
Verified Information • Last Updated Mar 2026
Real-Time Data Pipelines & Analytics on AWS
In today’s digital economy, data shouldn’t stand still — neither should you. This course, Real-Time Data Pipelines & Analytics on AWS, provides you with the necessary skills to process streaming data and make it business-ready. Taught with a focus on actual use cases, you get hands-on practice with AWS’s most popular tools, including Amazon Redshift, Kinesis, Glue, Athena, EMR, QuickSight, OpenSearch, and more.
Whether you are new to cloud data engineering or have experience and want to learn the latest, this course offers a curated curriculum featuring practical demos, guided videos, and examples. You’ll discover ways to increase the performance of your Redshift cluster, secure Kinesis streams, and integrate Spark with various AWS services. You’ll also be able to design analytics pipelines that provide real-time results.
By the time you are finished with this course, you will be able to build production-ready data pipelines that shine in today’s high-demand tech industry.
Enroll now and begin your path towards the data engineer that every company is looking for.
Disclaimer: AWS and Amazon Web Services are trademarks of Amazon.com, Inc. or its affiliates. This course is not affiliated with or endorsed by AWS.
Duration
8 Months
Institution
LearnKartS
Format
Online
Eligibility Criteria
school
Academic Foundation
A recognized Bachelor’s degree or high school equivalent required for admission into LearnKartS.
language
Language Proficiency
English proficiency required. IELTS, TOEFL, or standard medium-of-instruction certificates accepted.
Detailed Fees Breakdown
Base Tuition Fee
$129
Total Est. Investment
$129
Scholarships and early-bird waivers may apply. Contact admissions for exact institutional fees.
Academic Trajectory
Program Outcome
Graduates of the Real-Time Data Pipelines & Analytics on AWS program at LearnKartS are equipped with global perspectives, ready to excel in international markets and top-tier career opportunities.