5 Courses - Master AWS, Analytics, Machine Learning, Bigdata

Learn how data lakes unify structured and unstructured enterprise data, enable searchable catalogs, and support analytics and machine learning with scalable, cost-efficient storage and real-time processing via EMR and Spark.

Amazon Athena enables interactive query service to analyze data directly in storage with ad hoc queries and fast results, paying only for the queries you run, without complex extract-transform-load steps.

Amazon CloudSearch is a fully managed cloud service that makes it easy to set up, index, and search large data collections with low latency, high throughput, and built-in scalability.

Explore how Amazon Elasticsearch Service enables real-time search and log analytics by ingesting, indexing, and querying data within a secure, scalable domain, with no minimum fee or usage required.

Explore Amazon Kinesis data streams to collect, process, and visualize real-time web traffic for insights. Build stream processing applications with producers and consumers, using analytics, Firehose, and downstream data stores.

Learn to set up Amazon Kinesis data streams using the AWS CLI and console. Create streams, configure access, monitor data, and decode streamed content to understand real-time processing.

Explore Amazon Redshift's data warehousing capabilities with massively parallel processing and columnar storage, enabling fast deployment, automated provisioning and backups, unified data warehouse security, and real-time analytics.

Explore Amazon QuickSight, a business analytics intelligence service that connects to data sources, uploads files, and enables ad hoc analysis to create interactive dashboards accessible on any device.

Explore how Amazon Data Pipeline automates data movement between on-premises sources and cloud services, enabling extract, transform, and load processes, and scheduling for repeatable, scalable data workflows.

Explore how AWS Glue provides a managed, serverless ETL service that extracts and transforms data using crawlers, a data catalog, and pay-as-you-go pricing.

Explore big data, its high volume, velocity, and variety, and how predictive analytics and ecosystems transform vast data into actionable insights for business.

Trace the history of big data from the three Vs (volume, velocity, variety) through veracity and evolving architectures like MapReduce and Hadoop, including real-time, multi-source data.

Explore how the big data ecosystem blends analytics technologies to enable better decision making. See how big data powers IT operations, IoT, and industries with data storage and real-time insights.

Discover the five big data characteristics—volume, velocity, variety, veracity, and value—and how they influence near real-time data, multi-format sources, and advanced analytics for manufacturing insights.

Explore how big data applications drive cost savings, productivity, and innovation across manufacturing, health care, education, media, retail, and more through analytics and predictive tools.

Explore how a data lake acts as a centralized, scalable store for structured and unstructured data, enabling analytics, visualization, and machine learning across platforms using Hadoop and Spark.

Explore data science as an interdisciplinary field uniting statistics, data analysis, and machine learning to extract insights from structured and unstructured data.

Discover how Hadoop, an open source framework, stores and processes big data across distributed clusters with core components like HDFS, MapReduce, and YARN.

Discover hdfs, the open source hadoop distributed file system inspired by Google file system, designed for high-throughput access to large data sets on commodity hardware with replication and security.

Explore the master-slave Hadoop architecture, where the name node manages the file system namespace and coordinates data nodes across HDFS and the MapReduce framework.

Explore Hadoop architecture with assumptions and goals for large-scale data handling, emphasizing high throughput batch processing, real-time streaming access, fault tolerance, data-local computation, and a scalable distributed file system.

Download the Hadoop 3.1.0 release, verify integrity with signatures and hashes, ensure Linux with Java and passwordless SSH, noting Windows is supported but Linux is preferred.

Install and configure Hadoop and Java, set up SSH keys for passwordless login, and run in standalone and pseudo-distributed modes, then access DFS via browser and troubleshoot.

Learn how to access Hadoop via browser, view data node and security logs for troubleshooting, browse the file system, upload files, and review metrics and configurations.

Discover how machine learning enables computers to learn from data and build predictive models. Explore supervised and unsupervised learning, algorithms, and real-world applications like spam filtering and handwriting recognition.

Explore machine learning algorithms across regression, classification, clustering, and neural networks, and learn how decision trees, SVMs, deep learning, and dimensionality reduction power predictive models.

Explore machine learning softwares and open source tools, featuring Microsoft Cognitive Toolkit CNTK and deep learning frameworks, with Apache incubating projects and cloud platforms enabling scalable training and deployment.

Explore AWS on-demand cloud computing, analytics, and ML services. Learn to train and deploy models with SageMaker and use EMR, Athena, and Redshift for analytics.

Discover how amazon web services enables big data analytics with emr, hadoop, spark, presto, and kinesis for scalable storage, real-time processing, and visual insights.

Launch and scale a fully managed Hadoop platform on Amazon EMR to process big data analytics, using Spark, Presto, and real-time streaming with Kinesis for cloud analytics.

discover how amazon emr simplifies processing large data sets with hadoop and spark. learn about cluster lifecycles, master and task nodes, and steps that run on demand.

Explore the Amazon EMR architecture across storage, resource management, and data processing. See how EMRFS enables S3 as a file system, and how YARN, MapReduce, and Spark run on EMR.

Explore how Amazon EMR enables scalable, cost-efficient big data processing with spot instances, integrated AWS services for networking and storage, secure IAM policies, and CloudWatch monitoring.

Launch and configure an Amazon EMR cluster to process big data with Hadoop frameworks, upload data to S3, select applications, set cluster size and security, and monitor provisioning and steps.

Explore TensorFlow, the open source data-flow machine learning framework, and learn how it powers neural networks across research and production, with hands-on setup via Google notebooks and Jupyter across platforms.

Explore Amazon SageMaker as a fully managed platform to train, tune, and deploy machine learning models using TensorFlow, with built-in notebook instances, scalable hosting, and end-to-end workflow integration.

Explore Amazon SageMaker TensorFlow workflows from notebook setup and data preparation to training, deploying, and validating models with endpoints and inferences, then clean up artifacts.

Explore AWS deep learning AMIs that provide preconfigured cloud environments with popular deep learning frameworks, enabling quick launches, notebook access, and pay-as-you-go experimentation.

Explore AWS Translate, a deep learning driven, pay-as-you-go natural language translation service that supports 21 languages, enabling real-time translations and easy integration for localization and sentiment analysis.

Explore Amazon Polly text-to-speech: natural-sounding voices across languages, real-time streaming, and lexicon customization with pay-per-character pricing for content creation.

Explore Apache MXNet, an open-source deep learning framework for training and deploying neural networks, with multi-language support and cloud compatibility from AWS and Microsoft, incubating at the Apache Software Foundation.