AWS Certified Big Data - Specialty (BDS-C00) Exam Prep 2026
AWS BDS-C00 Big Data Specialty. Hadoop, EMR, Kinesis, and large-scale data processing.
Course Description
This course prepares you for the AWS Certified Big Data Specialty (BDS-C00) exam. We cover Hadoop, EMR, Kinesis, Redshift, and data processing at scale.
You will work through hands-on labs, real big data scenarios, and practice exams. The course includes video lessons and downloadable resources.
Course Curriculum
5 sections • 14.00 hours total length
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Understanding the BDS-C00 Exam Blueprint (12m)
We'll break down the exam domains, question formats, and scoring so you know exactly what to expect on test day.
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AWS Identity and Access Management (IAM) for Big Data (18m)
Learn how to configure secure IAM roles and policies for data pipelines and analytics services.
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Amazon S3: Storage Classes and Lifecycle Policies (22m)
Master S3 best practices for cost-effective data storage, including Intelligent-Tiering and lifecycle management.
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VPC Fundamentals for Data Services (15m)
A step-by-step guide to setting up VPCs, subnets, and security groups to isolate your data workloads.
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Data Transfer and Network Optimization (9m)
We'll show you how to minimize costs and latency when moving large datasets into and out of AWS.
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AWS CloudTrail and CloudWatch for Data Auditing (14m)
Learn to monitor data service activity and set up essential alerts for your analytics environment.
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Cost Management for Big Data Workloads (11m)
A practical look at budgeting, cost allocation tags, and using the Pricing Calculator for data projects.
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Amazon Kinesis Data Streams vs. Kinesis Firehose (25m)
A deep dive into when to use each Kinesis service for real-time data ingestion with a streaming case study.
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Ingesting Data with AWS Snowball and Snowmobile (16m)
Learn the physical data transfer options and how to plan a large-scale data migration project.
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Using AWS DataSync for Hybrid Cloud Transfers (13m)
We'll demonstrate how to automate and accelerate data movement between on-premises storage and AWS.
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Streaming Ingestion with Amazon Kinesis Data Analytics (21m)
A problem-solving session on processing streaming data in real-time with SQL and Java applications.
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Batch Ingestion Patterns with AWS Glue (19m)
Learn to use Glue crawlers and jobs to catalog and move data from various sources into your data lake.
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Amazon MSK for Apache Kafka Workloads (28m)
Understand the architecture of Managed Streaming for Kafka and how it fits into enterprise data pipelines.
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Ingesting IoT Data with AWS IoT Core (17m)
A practical guide to connecting IoT devices and routing their data to services like S3 and Kinesis.
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Database Migration with AWS DMS (24m)
Learn to migrate on-premises databases to AWS cloud data stores with minimal downtime.
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Designing a Resilient Data Ingestion Strategy (20m)
A capstone lesson combining concepts to design a fault-tolerant ingestion pipeline for a retail company.
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Amazon Redshift: Cluster Architecture and Distribution Styles (32m)
A deep dive into Redshift internals, helping you choose the right distribution and sort keys for performance.
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Optimizing Redshift with Spectrum and Concurrency Scaling (26m)
We'll show you how to use Redshift Spectrum for S3 queries and Concurrency Scaling for peak loads.
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Amazon DynamoDB: Partitioning, Indexes, and Capacity Modes (30m)
Master NoSQL design patterns for high-performance applications on DynamoDB.
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Building a Data Lake with AWS Lake Formation (23m)
A step-by-step guide to creating a secure, centralized data lake with fine-grained access controls.
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Amazon DocumentDB and MongoDB Compatibility (15m)
Learn when to use DocumentDB for your document database workloads on AWS.
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Comparing HDFS vs. S3 for Big Data Storage (11m)
A practical comparison to help you decide when to use EMR's HDFS vs. S3 as your primary storage.
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Data Encryption at Rest and in Transit (18m)
A real case study analysis of using KMS to encrypt data across S3, Redshift, and RDS.
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Amazon EMR: Cluster Sizing and Instance Selection (29m)
Learn to optimize EMR clusters for cost and performance for Spark, Hive, and Presto workloads.
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Running Spark Jobs on EMR with EMRFS (27m)
A hands-on lesson covering EMRFS, consistent views, and running Spark applications on your cluster.
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AWS Glue ETL: PySpark and DynamicFrames (31m)
We'll write a practical Glue ETL job using PySpark to transform and clean complex data formats.
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Amazon Athena: Querying S3 Data Lakes with SQL (24m)
A problem-solving session on optimizing Athena queries, partitioning, and using SerDe libraries.
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AWS Step Functions for Orchestration (20m)
Learn to build resilient, visual workflows to coordinate multiple AWS services into a data pipeline.
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AWS Batch for Event-Driven Processing (18m)
Understand how to run batch computing workloads at any scale without managing servers.
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Amazon Kinesis Data Analytics for Streaming ETL (22m)
We'll transform streaming data in real-time using SQL applications in Kinesis Data Analytics.
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Choosing the Right Processing Service (12m)
A decision-making framework for when to use EMR, Glue, Athena, or Kinesis for your processing needs.
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Amazon Redshift Data Warehouse Design (30m)
Learn to design a star schema, optimize queries, and manage workloads in a Redshift data warehouse.
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Amazon QuickSight: SPICE, Analysis, and Dashboards (25m)
A practical guide to building interactive dashboards and using QuickSight's in-memory SPICE engine.
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Integrating Machine Learning with Amazon SageMaker (35m)
We'll show you how to build, train, and deploy ML models to gain insights from your big data.
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Using Amazon Comprehend for Natural Language Processing (19m)
A real case study on analyzing customer feedback text to extract sentiment and key phrases.
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Amazon Elasticsearch Service (OpenSearch) for Log Analytics (28m)
Learn to ingest, index, and visualize log data for operational intelligence and monitoring.
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Amazon Neptune for Graph Data Analysis (16m)
A problem-solving session on using graph databases to model complex relationships like social networks.
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Amazon Forecast for Time-Series Predictions (21m)
Learn to use machine learning to generate accurate forecasts from your historical time-series data.
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Amazon Textract for Document Text Extraction (14m)
A practical lesson on extracting text, forms, and tables from scanned documents for analysis.
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Building a Complete Analytics Solution (26m)
A capstone project where you'll design an end-to-end solution from ingestion to visualization for a use case.
Course Details
- Duration: 14.00 hours
- Level: Adaptative
- Language: English
- Lessons: 40+ video lessons
- Categories: IT Certifications
- Access: Lifetime access
- Device: Mobile & Desktop
- Certificate: Yes. After completion and Exam
The course is totally free. Seriously appreciated attribution
