Complete AWS Big Data and Analytics Certification

Explore how Amazon Web Services enables scalable cloud solutions across compute, storage, analytics, database, and applications, and build practical projects to pursue the AWS certification.

Create an Amazon Web Services account by providing email, password, and contact details, then verify identity with an OTP and phone number to access the dashboard.

Install the AWS CLI and configure it with IAM user credentials. Create an IAM user, obtain access key and secret key, and verify CLI access.

Explore the AWS CLI command reference, navigate service documentation, and execute Lambda and CloudWatch commands, including creating functions with runtime, role, timeout, and deployment zip.

Learn to import and upload datasets into quick site, create datasets from csv or excel, and build visualizations like pie, bar, and map charts using group and value fields.

Create and customize a tree map chart by mapping size to margin and color to price, with color tweaks for charts, then export the dashboard as a PDF.

learn data preparation by editing datasets, creating calculated fields, applying filters, and cleaning data before visualizing, using csv uploads or connectors, with support for multiple sheets and data type changes.

Create and edit calculated fields in quick sight to add profit per unit and price per unit, using the ceiling function for data preparation and visualization.

Create filters and excluded lists to refine datasets, including exclude fields, country and date range filters, and time granularity, then apply and visualize for actionable insights.

Create map charts using area maps, apply conditional formatting to color countries by profit, and adjust tooltips. Tune filters and themes to achieve color harmony across the dashboard.

learn to create and customize pivot tables to summarize large data in a tabular format, using rows, columns, and values, with conditional formatting to highlight insights in dashboards.

Apply conditional formatting and pivot tables to highlight top performing states in a data visualization dashboard, using color-coded ranges to tell a clear data story.

Create a word cloud visualization on a quick site by grouping by product name and sizing by order quantity or profit. Customize color and layout to highlight category insights.

Create and configure an Amazon S3 bucket to store datasets, selecting a region, managing public access and encryption, and uploading files as objects.

Create an AWS Glue crawler to scan data in S3 and other sources, generating a table with an inferred schema in the Glue data catalog for queries.

Configure the crawler’s output database by naming a binary database to store crawled data, set the frequency, data source, authentication, and optional advanced options, then schedule and manage the crawler.

Explore amazon elastic compute cloud (ec2) and its elastic, pay-as-you-go compute capacity across regions with auto scaling, load balancing, and secure, scalable infrastructure.

Learn how Amazon Elastic Container Service runs Docker containers in a managed cluster with scalable, portable deployment, private images, and secure API-driven control for big data analytics.

Configure Amazon ECS by creating clusters, defining task definitions, and deploying container definitions with images, port mappings, and resource limits, then monitor logs and scaling.

Elastic Beanstalk acts as an orchestration service for deploying and managing scalable applications. It supports Docker, multiple languages, and automatically handles deployment, capacity provisioning, monitoring, and load balancing.

Develop hands-on experience deploying applications with AWS elastic beanstalk by creating an environment, selecting a platform, and uploading sample or custom code; monitor health and logs while managing configurations.

Create and manage batch processing using Amazon Batch on Amazon EC2, configuring compute environments, job queues, and definitions, and monitor job status under a pay as you go model.

Launch and configure an AWS EKS cluster from the compute category, set the cluster name, IAM role, and subnets, and explore hybrid cloud migrations and use cases.

Explore how Amazon EMR provides a managed Hadoop framework for big data analytics, enabling scalable MapReduce, HDFS, Hive, Spark, and seamless integration with S3, DynamoDB, and CloudSearch.

Explore the AWS analytics landscape, including Athena and other cloud services, and learn how to set up a free-tier AWS account, install the AWS CLI, and configure credentials.

Explore Amazon Athena, a serverless SQL query service on S3 that analyzes data with schemas and views. Pay only for data scanned, and optimize with partitioning, compression, and columnar formats.

Learn how to create and organize S3 buckets, upload and structure files into folders, set permissions and policies, enable versioning and logging, and prepare data for Amazon Athena analysis.

Learn how to use the Athena sql editor to create databases and tables, run and save queries, add columns, and review query history to manage big data in AWS.

Create a new database in Amazon Athena named population, and import data from an S3 bucket by selecting a dataset and defining the table columns.

Create a table in Athena and bulk add columns by mapping attributes to data types, including location, age group, and population, with virtual columns and optional partitions.

Explore Athena queries using select statements, create and drop tables, and manage table properties, location, and performance—for practical data analysis in AWS big data workflows.

Write a query to compute total population and total female population by time and location, filtering years 1980 to 2000 using a group by statement in Athena.

The lecture demonstrates using the create statement to create a database and tables, select the database, handle errors, and rename or recreate tables.

Explore using alter statements to modify column data types, convert integers to decimals, and observe effects on query results, with examples on select, ordering, and Athena queries.

Learn how to use the drop statement to remove a table or database, apply cascade, and understand the implications of deleting database objects.

Explore Amazon CloudSearch, a fully managed, scalable search service with auto-complete, highlighting, geospatial search, and range and boolean full-text search.

Create and configure an AWS cloud search domain, name your domain, set up the index and access policies, and review the deployment to enable search functionality.

Upload 5000-document dataset with actor attributes, generate a document batch, and run a test search in cloud search to retrieve movie details, genres, and actors for embedding on a website.

Explore CloudSearch configuration options for domains, including access policies, indexing settings, scaling options, expressions, and monitoring, then manage tags and endpoints to tailor search results.

Explore amazon elastic search, a managed service that searches, analyzes, and visualizes data in real time with easy apis, security, and scalable performance for log analytics and text search.

Learn how Amazon Kinesis enables fast, real-time data ingestion, processing, and analysis of streaming data—video, logs, and click streams—driving timely insights and actions.

Kinesis Video Streams provides fully managed, secure video streaming from connected devices for analytics and machine learning, with encrypted data, durable storage in S3, and accessible APIs.

Discover AWS Kinesis Firehose, a fully managed, auto-scaling service that captures, transforms, and loads streaming data into S3, Redshift, Elasticsearch Service, and Splunk for near real-time analytics.

Explore how Amazon Kinesis Data Analytics processes streaming data in real time, without learning new programming frameworks, by querying streams and applying transformations with a fully managed, scalable service.

Learn how Amazon Kinesis Data Streams ingest gigabytes per second for real-time analytics, with durable, scalable, encrypted streaming across data centers and integration with Lambda, EMR, and S3.

Explore creating and configuring Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose, set throughput and destinations, enable enhanced fan out, and monitor stream status in real time.

Learn to set up AWS Kinesis streams with Python boto3, including creating streams, describing them, listing streams, and putting records with partition keys, while managing permissions and basic encryption options.