AWS Certified Big Data - Specialty

Master the basics of Amazon S3, including buckets, objects, storage classes, and bucket policies. Learn how versioning, encryption, and access controls keep data durable and secure.

Explore S3 permissions and encryption at bucket and object levels, including bucket policies, principals, and IAM roles, and learn to enforce encryption at rest and secure transit.

Explore storage classes, lifecycles, and performance in AWS S3, including standard, intelligent tiering, glacier, and deep archive. Learn how lifecycle rules move data between tiers.

Explore DynamoDB essentials, a serverless key-value or document database with near real-time reads and writes. See how auto-scaling, streams, lambda triggers, global tables, and the accelerator enable scalable data workflows.

Explore DynamoDB read and write operations, covering provisioned throughput, on-demand pricing, and auto scaling. Learn how read and write capacity units, strongly and eventually consistent reads, and pricing affect throughput.

Explore how DynamoDB indexes enable efficient queries using partition and sort keys, global and local secondary indexes, and projected attributes for optimized data access.

Explore DynamoDB global tables and conditional writes across multiple regions, using streams to synchronize multi-master databases. Learn how atomic counters and conditional expressions enable safe, first-come, first-served updates.

Turn on DynamoDB streams and wire them to Lambda to react to table changes with new and old images, enabling event-driven big-data workflows.

Discover how AWS elastic map reduce provides a managed cluster for big data workloads, enabling Spark, Hadoop, Hive, and Presto with on-demand scaling and S3-based storage.

Learn how to use Hue and Hive with an EMR cluster to load CSV data from S3, create Hive tables, and run Hive SQL queries and aggregations.

Access spark on an EMR cluster using EMR notebooks to run PySpark code and a map-reduce style pi estimation; explore data, visualize results, and store notebooks in S3.

SageMaker essentials as machine learning as a service that trains models from datasets using Jupyter notebooks, with built-in algorithms, labeling, and scalable notebook environments.

Explore training with SageMaker notebooks by building a dataset and deploying models as endpoints. Learn how MNIST k-means training runs in notebooks and exposes a model via an endpoint.

Explore AWS Lambda essentials, a function as a service platform that runs code on triggers from S3, SQS, or Kinesis, enabling scalable ETL, processing, and notifications without server management.

Transform data with Lambda to adjust longitude from -180–180 to 0–360, streaming through Kinesis Firehose into S3, and learn how to trigger and test the pipeline.

Learn data pipeline essentials in AWS, configuring input and output data nodes across S3, Dynamo, Redshift, RDS, or JDBC, and using shell commands with EMR for scheduled processing and logs.

Learn how to migrate data from RDS to DynamoDB using the Database Migration Service; set up replication instance, endpoints, and tasks, and configure permissions, logging, and schema mappings.

Master AWS Glue essentials by exploring the data catalog, databases and tables, building crawlers and classifiers, and creating Spark-based ETL jobs with triggers and notebooks.

Learn how AWS provides a fully managed Elasticsearch service powering the ELK stack for searchable logs, Kibana dashboards, and real-time analytics, with Kinesis as a Logstash alternative.

Learn to provision the Elasticsearch service on AWS, access Kibana, and push data via Kinesis Firehose for real-time visualization, indexing, and dashboards.

Explore Athena essentials: learn to query S3 data with a managed Presto via Glue data catalog, optimize with partitioning and columnar formats, and understand per-query pricing.

Learn Redshift essentials: a massively parallel data warehouse with columnar storage, compression, and query analysis for petabyte-scale data. Explore pricing, cluster creation, and integration with S3 and Spectrum.

Explore how to load and unload data in Amazon Redshift, using copy and unload commands, S3 integration, IAM roles, and external clients to manage large-scale data queries.

Master real-time streaming analytics with AWS Kinesis analytics essentials using sql. Build an analytics application that ingests Kinesis data streams or Firehose and routes results to S3, Lambda, or Elasticsearch.

Discover how Amazon QuickSight turns data into insight with the SPICE engine, building dashboards from sources like S3, Athena, RDS, and Redshift, with author and reader roles.

Visualize AWS big data from S3 using JavaScript and Chart.js by hosting a static web page in S3. Build time-series charts from JSON data.

Master identity and access management basics for big data in AWS. Explore users, groups, roles, and policies, enforce least privilege with MFA, and manage S3 bucket permissions.

Learn how CloudTrail provides an audit trail of AWS actions by logging management and data events, storing logs in S3 with lifecycle policies to Glacier, and enabling trails for compliance.

Learn how to enable encryption at rest for databases and storage in AWS using KMS keys, including AWS managed vs customer managed keys, rotation, and key policies.