The Syllabus
Curriculum Designed by Experts
• Overview of big data concepts and challenges
• Introduction to AWS cloud computing services
• Understanding the AWS big data ecosystem
• Architectural considerations for big data on AWS
• Overview of AWS storage services (S3, EBS, EFS, Glacier)
• Designing data storage solutions for big data workloads
• Data ingestion and data transfer methods
• Data lifecycle management and versioning
• Introduction to AWS compute services (EC2, EMR, Lambda)
• Batch processing with AWS Elastic MapReduce (EMR)
• Real-time processing with AWS Lambda and Kinesis
• Serverless computing for big data workloads
• Introduction to AWS data warehousing services (Redshift, Athena, Glue)
• Designing and optimizing data warehouse architectures
• Querying and analyzing big data with AWS services
• Integration with business intelligence (BI) tools
• Introduction to AWS streaming services (Kinesis, Kafka)
• Real-time data ingestion and processing pipelines
• Real-time analytics with AWS services (Kinesis Analytics, Amazon Managed Streaming
for Apache Kafka)
• Monitoring and scaling real-time analytics solutions
• Introduction to AWS orchestration services (Step Functions, Data Pipeline)
• Designing and managing big data workflows on AWS
• Automating data pipelines and ETL processes
• Error handling and fault tolerance in data workflows
• Understanding data governance challenges in big data
• Data security and compliance considerations on AWS
• Identity and access management (IAM) for big data workloads
• Encryption and data protection mechanisms on AWS
• Overview of data visualization tools and services
• Integrating AWS big data solutions with visualization tools (QuickSight, Tableau)
• Designing interactive dashboards and reports
• Data storytelling and effective visualization practices
• Cost optimization strategies for big data workloads on AWS
• Selecting the right AWS services based on cost and performance requirements
• Monitoring and optimizing resource utilization
• Scalability and performance tuning techniques
• Advanced analytics with AWS machine learning services (SageMaker, Comprehend,
Rekognition)
• Big data processing with AWS serverless technologies (Glue, Athena, Lambda)
• Exploring emerging trends in big data and AWS services
• Industry use cases and best practices
refers to the utilization of AWS services and infrastructure for storing, processing, and analyzing large volumes of data. AWS provides a comprehensive set of tools and services specifically designed to handle big data workloads efficiently and effectively. Some of the key services offered by AWS for big data include:
- Amazon S3 (Simple Storage Service): AWS S3 is a highly scalable object storage service that allows you to store and retrieve large amounts of unstructured data. It is often used as a data lake to store raw data before processing.
- Amazon EMR (Elastic MapReduce): EMR is a managed big data processing service that enables you to run distributed frameworks such as Apache Hadoop, Spark, and Presto on AWS. It simplifies the deployment and management of these frameworks and enables processing of large datasets in a scalable manner.
- Amazon Redshift: Redshift is a fully managed data warehousing service that provides high-performance analytics for large-scale data sets. It is optimized for online analytical processing (OLAP) workloads and allows you to query and analyze data using SQL.
- AWS Glue: Glue is a fully managed extract, transform, and load (ETL) service that helps you prepare and transform your data for analytics. It automatically generates ETL code and provides a serverless environment for data preparation tasks.
- AWS Athena: Athena is an interactive query service that allows you to analyze data directly from Amazon S3 using standard SQL queries. It eliminates the need to set up and manage infrastructure and enables ad-hoc querying of large datasets.
- Amazon Kinesis: Kinesis is a platform for real-time streaming data processing. It allows you to ingest, process, and analyze streaming data at any scale. Kinesis offers multiple services like Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics for different streaming use cases.
- AWS Lambda: Lambda is a serverless computing service that allows you to run code without provisioning or managing servers. It can be used for data processing and integration tasks, such as transforming and enriching data as it flows through various AWS services.
- AWS Data Pipeline: Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services and on-premises data sources. It simplifies the creation, scheduling, and management of data workflows.
These are just a few examples of the many AWS services available for handling big data. AWS provides a scalable and flexible platform for storing, processing, and analyzing large datasets, allowing organizations to leverage the power of big data for various purposes, including business intelligence, machine learning, and predictive analytics
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Why Enroll for Big Data on AWS ?
In-Demand Skills
Mastering Big Data on AWS is crucial in today’s data-driven world. This course covers in-demand skills like data storage, processing with Amazon EMR, analytics using Redshift, and real-time insights with Kinesis. Enroll to gain practical expertise and propel your career in cloud-based data management and analytics.
Career Opportunities
The Big Data on AWS course opens doors to roles like Data Engineer, Big Data Analyst, and Cloud Architect. With expertise in AWS tools like EMR, Redshift, and Kinesis, you’ll be equipped to drive data solutions. Enroll to build a career in the fast-growing field of cloud-based big data analytics.
Cloud Adoption
Accelerate your Cloud Adoption journey with our comprehensive BIGDATA ON AWS Course Training! Gain hands-on expertise in managing and processing massive data sets with AWS tools, enhancing your skills in data analytics, secure storage, and real-time insights. Enroll today to master essential cloud-based Big Data skills, designed for modern data professionals.
Scalability and Flexibility
Achieve unparalleled Scalability and Flexibility with our BIGDATA ON AWS Course Training! Dive deep into AWS tools that empower you to handle vast data sets, adjust seamlessly to workload demands, and drive impactful insights. Enroll today to gain essential skills for scalable, adaptable data solutions in the cloud.
Cost Management
Master Cost Management with our BIGDATA ON AWS Course Training! Learn strategies to optimize spending, maximize AWS resource use, and manage Big Data workloads efficiently. Enroll now to gain essential skills for cost-effective data solutions on AWS, empowering your cloud journey. Join us today!
Security and Compliance
Enhance Security and Compliance with our BIGDATA ON AWS Course Training! Gain in-depth knowledge to secure vast data, ensure regulatory compliance, and safeguard cloud infrastructure. Enroll now to master essential AWS security skills for managing Big Data with confidence. Join us today for a secure future!
Course benefits
Who Can Apply for Red Hat Linux
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- Developers looking to master Machine Learning and Predictive Analytics
- Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
- Aspirants who are looking to work as Machine Learning Experts, Data Scientists, etc.
- Anyone who wants to learn machine learning, artificial intelligence, data visualization, data analytics, data structures, and algorithms (DSA).
Why Big Data on AWS ?
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At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalized resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career. Join us and let your journey to a successful future begin with the right support.
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Red Hat Linux Course Projects
Infrastructure Provisioning
Implementing automated infrastructure provisioning and configuration management using Ansible. This may include setting up servers, networking devices, and other infrastructure components using playbooks and roles.Â
Applications Deployment
Automating the deployment and orchestration of applications across development, testing, and production environments. This could involve deploying web servers, databases. middleware, and other application components using Ansible
Continuous Integration
Integrating Ansible into CI/CD pipelines to automate software. build, test, and deployment processes. This may include automating the creation of build artifacts, running tests, and deploying applications to various environments.
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Big Data on AWS FAQs
Big Data on AWS refers to the use of Amazon Web Services’ tools and infrastructure for storing, processing, and analyzing vast amounts of data. It’s important because AWS provides scalable, cost-effective, and secure solutions that empower businesses to gain insights and make data-driven decisions.
Key AWS services for big data processing include Amazon EMR for Hadoop and Spark-based processing, Amazon Redshift for data warehousing, Amazon S3 for storage, AWS Glue for ETL processes, Amazon Kinesis for real-time streaming, and Amazon SageMaker for machine learning.
Amazon S3 offers durable, scalable, and secure storage for large datasets. With virtually unlimited storage capacity, S3 integrates seamlessly with other AWS services, supports data lifecycle management, and provides cost-effective options like S3 Glacier for archiving.
Amazon Kinesis enables real-time data streaming, allowing businesses to collect, process, and analyze data as it’s generated. This is crucial for applications like clickstream analytics, IoT data processing, and log monitoring, where immediate insights are needed.
Amazon EMR (Elastic MapReduce) is a cloud-based big data platform that simplifies running Hadoop, Spark, and other distributed data processing frameworks. It’s used to process and analyze large datasets, supporting tasks like data transformation, ETL, and advanced analytics.
Amazon Redshift is a fast, scalable data warehousing service that allows users to run complex queries on petabyte-scale data. It uses columnar storage and advanced query optimization techniques, making it ideal for big data analytics and business intelligence.
AWS Glue is a fully managed ETL (Extract, Transform, Load) service that simplifies data preparation for analytics and machine learning. It automates data discovery, cleaning, and cataloging, making it easier to transform raw data into valuable insights.
Yes, AWS offers Amazon SageMaker, a fully managed machine learning service that enables data scientists to build, train, and deploy machine learning models on large datasets. SageMaker integrates well with other AWS services, enabling scalable and efficient machine learning on big data.
A data lake is a centralized repository that stores structured and unstructured data at any scale. AWS supports data lakes with services like Amazon S3 for storage, AWS Lake Formation for lake setup and management, and AWS Glue for data cataloging and processing.
Amazon QuickSight is a business intelligence service that allows users to create interactive dashboards and visualizations for big data. It connects to multiple data sources, including Amazon Redshift and S3, providing quick insights through customizable dashboards.
Best practices include encrypting data at rest and in transit, implementing AWS Identity and Access Management (IAM) for access control, using Amazon S3 bucket policies, enabling logging and monitoring with AWS CloudTrail, and regularly auditing for compliance.
AWS supports ETL processes with AWS Glue, a managed ETL service that automates data extraction, transformation, and loading. Other tools, such as Amazon EMR and AWS Data Pipeline, can also be used for more complex or customized ETL workflows.
Common use cases include real-time customer analytics, predictive maintenance, fraud detection, personalized recommendations, genomics research, financial risk assessment, supply chain optimization, and climate modeling. AWS’s big data services support diverse industry applications.
Yes, AWS can handle unstructured data. Services like Amazon S3 and Amazon DynamoDB are optimized for storing and querying unstructured data. With AWS Glue and Amazon EMR, unstructured data can be processed and transformed into usable insights.
AWS offers a pay-as-you-go pricing model, meaning you pay only for the resources you use. For big data solutions, AWS also offers cost-optimization options, such as Amazon S3 storage classes and Reserved Instances, allowing you to manage costs efficiently based on your data needs.
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Most Probable Interview Questions Asked for Big Data on AWS
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About Big Data on AWS Certification
In today’s data-driven world, companies across industries are leveraging big data to make informed decisions, predict trends, and gain competitive advantages. Amazon Web Services (AWS) offers a comprehensive suite of tools and services for big data management, processing, storage, and analytics, enabling businesses to scale their data capabilities and derive actionable insights with ease. Let’s dive into the details of how big data on AWS can transform your data strategy and provide you with a competitive edge.
Why Choose AWS for Big Data Solutions?
AWS is the leading cloud provider that brings robust infrastructure and cutting-edge technology to support large-scale data management. With AWS, businesses can access highly scalable, flexible, and secure big data solutions tailored to meet a variety of use cases, from real-time analytics and batch processing to machine learning and artificial intelligence. AWS’s big data services are ideal for both small startups and large enterprises, offering on-demand, pay-as-you-go pricing that adapts to your specific data needs.
Key Components of Big Data on AWS
AWS provides a well-rounded set of tools for the entire big data lifecycle. Here are some of the critical components to know:
Data Ingestion and Collection
Efficient data ingestion is crucial to manage big data workflows. AWS offers services like Amazon Kinesis, AWS IoT, and AWS Data Pipeline to collect, transform, and load data from a variety of sources. With these tools, data from applications, logs, IoT devices, and clickstream data can be seamlessly integrated into your data lake or warehouse.
Data Storage
Storage is a foundational aspect of any big data strategy. Amazon S3 (Simple Storage Service) is the go-to solution for durable, secure, and highly scalable storage. For businesses dealing with large amounts of unstructured data, Amazon S3 offers virtually unlimited storage and integrates well with other AWS analytics and machine learning services. Additionally, Amazon Glacier provides low-cost, long-term storage for archival data, helping you optimize costs.
Data Processing and Analytics
Processing large datasets efficiently is key to unlocking insights. AWS offers Amazon EMR (Elastic MapReduce), a powerful platform for big data processing with Hadoop, Spark, and Presto, making it ideal for large-scale data transformation and analytics tasks. For real-time data streaming analytics, Amazon Kinesis Analytics helps analyze data on the fly, while AWS Lambda enables serverless data processing, reducing the need for managing infrastructure.
Data Warehousing
For high-performance data warehousing, Amazon Redshift provides fast querying capabilities for structured data. Redshift allows businesses to run complex queries on petabyte-scale data, making it ideal for data warehousing and big data analytics tasks. By connecting with popular BI tools, Amazon Redshift facilitates data visualization, enabling better decision-making.
Data Analytics and Machine Learning
AWS enhances big data analytics by incorporating machine learning models that drive predictive and prescriptive analytics. Amazon SageMaker is an end-to-end solution that simplifies machine learning processes by automating model building, training, and deployment. Paired with AWS Glue, a fully managed ETL (Extract, Transform, Load) service, businesses can clean, categorize, and process data to uncover deeper insights.
Data Visualization
Presenting data in a clear and actionable way is essential for making business decisions. Amazon QuickSight is a powerful business intelligence tool that enables users to create interactive dashboards and visualize big data insights quickly. QuickSight integrates with other AWS data services and supports real-time analytics, providing a holistic view of your data.
Benefits of Big Data on AWS
Using AWS for big data brings numerous benefits that can streamline data workflows and maximize value:
- Scalability: AWS’s cloud infrastructure scales on demand, handling everything from terabytes to petabytes of data without requiring additional hardware.
- Cost Efficiency: With AWS’s pay-as-you-go model, you only pay for what you use, making big data processing cost-effective, especially for businesses with fluctuating data needs.
- Security and Compliance: AWS prioritizes security with features like data encryption, identity management, and network isolation, helping businesses meet stringent compliance requirements.
- Flexibility and Integration: AWS offers a broad range of big data tools that can integrate with open-source frameworks, supporting diverse data environments and reducing the need for custom development.
Applications of Big Data on AWS
Big Data on AWS provides businesses with an extensive toolkit to capture, analyze, and process vast volumes of data efficiently. AWS’s cloud infrastructure supports diverse applications across multiple industries, enabling organizations to leverage data-driven insights for growth, optimization, and strategic decision-making. Here, we explore key applications of Big Data on AWS and how they are transforming business operations in meaningful ways.
1. Real-Time Data Analytics for Customer Insights
AWS enables companies to capture and process real-time data streams, helping them understand customer behavior instantly. Using services like Amazon Kinesis and AWS Lambda, e-commerce businesses, for example, can track website activity and purchase behaviors to provide personalized recommendations and improve the customer experience. This real-time analytics approach is invaluable for sectors like retail, media, and online services, where quick adaptation to customer needs directly impacts satisfaction and revenue.
2. Predictive Maintenance in Manufacturing
Manufacturing companies are increasingly adopting AWS big data solutions to monitor and maintain equipment performance. With AWS IoT for device data collection and Amazon SageMaker for machine learning, manufacturers can predict potential failures before they happen. By analyzing sensor data and other machine indicators, companies can schedule preventive maintenance, reducing costly downtimes and extending the lifespan of critical equipment. This application is crucial for industries like automotive, aerospace, and heavy machinery, where unexpected disruptions can be highly expensive.
3. Fraud Detection and Financial Risk Analysis
Financial institutions use Big Data on AWS to analyze and assess risk factors, as well as to detect fraudulent activities. Services such as Amazon Redshift for data warehousing and Amazon QuickSight for visualization enable real-time analysis of transactional data, helping institutions identify patterns indicative of fraud or risk. Additionally, with AWS Glue and Amazon EMR for data processing, banks and insurers can streamline their risk assessments, analyze credit scores, and automate compliance checks, enhancing operational security and compliance.
4. Enhanced Patient Care in Healthcare
AWS big data solutions are instrumental in healthcare, where patient data from electronic health records (EHRs), medical imaging, and even genomic data can be used to improve diagnostics and treatment plans. Using Amazon S3 for data storage and Amazon SageMaker for machine learning, healthcare providers can analyze patient histories and health trends, enabling personalized care. Hospitals can also leverage big data to manage patient influx, optimize staff allocation, and improve overall service quality. The ability to securely handle sensitive data while adhering to regulatory standards makes AWS a trusted partner in healthcare transformation.
5. Data-Driven Marketing and Personalization
Marketing teams rely on Big Data on AWS to run data-driven campaigns that resonate with their audiences. With Amazon Redshift and Amazon Athena for data queries, along with Amazon QuickSight for visualization, marketers can segment customers based on behavior, preferences, and purchasing history. This approach allows for highly targeted advertising and effective personalization, improving customer retention rates. Additionally, AWS big data tools enable A/B testing and analysis of campaign performance metrics in real time, refining marketing strategies to maximize engagement.
6. Supply Chain Optimization and Logistics Management
Companies involved in logistics and supply chain management use Big Data on AWS to enhance transparency and efficiency in their operations. With Amazon Kinesis for real-time data streaming and AWS IoT for data collection from sensors on vehicles and warehouses, companies can track shipments, predict delays, and optimize routing. Amazon EMR allows for rapid data processing, helping logistics managers make real-time adjustments to inventory management and transport planning. This application is essential for industries like retail, automotive, and food services, where timing and efficiency are crucial to profitability.
7. Genomic Research and Personalized Medicine
The field of genomics requires processing vast datasets, often containing terabytes of genetic information. AWS enables researchers to store, process, and analyze this data at scale. With AWS Batch for handling large-scale batch processing and Amazon S3 for secure storage, scientists can sequence genomes more quickly and affordably. Genomic data can be processed with Amazon EMR and analyzed using Amazon SageMaker for machine learning insights. This capability accelerates advancements in personalized medicine, allowing for treatments tailored to individual genetic profiles.
8. Smart City Initiatives and Urban Planning
AWS big data solutions are helping cities become smarter and more efficient by analyzing data from IoT devices, transportation systems, and citizen feedback. AWS IoT and Amazon Kinesis enable the collection and processing of data in real time, providing insights into traffic patterns, public safety, and energy consumption. Urban planners can use Amazon Redshift and QuickSight for data analysis and visualization, making it easier to develop strategies for reducing congestion, lowering pollution, and optimizing resource usage. The result is improved quality of life for residents and more sustainable urban development.
9. Educational Insights and Student Performance Tracking
Educational institutions use Big Data on AWS to enhance learning experiences and track student progress. With AWS Lambda and Amazon EMR, schools and universities can analyze academic performance, attendance records, and even engagement in online platforms. These insights can inform personalized learning programs, detect students who may need extra support, and improve curriculum planning. Additionally, Amazon QuickSight allows educational leaders to visualize data in meaningful ways, supporting data-driven decision-making across educational environments.
10. Environmental and Climate Research
Organizations working in environmental research use AWS to analyze data related to climate patterns, wildlife tracking, and natural resource management. Using Amazon S3 for data storage and Amazon SageMaker for model training, researchers can process large datasets from satellites, sensors, and historical climate records. By analyzing this data, scientists can predict environmental changes, understand ecological impacts, and develop strategies for conservation. AWS’s big data capabilities help streamline the processing of complex environmental data, supporting informed decision-making in conservation and climate research.
Big Data on AWS Course Certification with Training in Bangalore
Welcome to Radical Technologies, the leading institute in Bangalore for Big Data on AWS Training. With a reputation for excellence and an industry-oriented approach, we specialize in delivering comprehensive AWS Big Data Training designed to equip professionals with the skills and knowledge needed to excel in today’s data-driven world. Our courses cover the entire spectrum of Big Data on AWS concepts, ensuring you gain hands-on experience with real-world applications.
At Radical Technologies, we offer a range of courses tailored to meet the diverse needs of our students and corporate clients. Our flagship programs include Big Data on AWS Course in Bangalore, AWS Big Data Certification Training, and Amazon Big Data Certification courses. We also provide flexible learning formats, including Big Data on AWS Online Classes in Bangalore and Big Data AWS Certification Online options, allowing students to learn at their own pace and convenience.
Our training modules are led by industry experts with years of experience in AWS with Big Data solutions, offering practical insights and hands-on guidance. Whether you’re looking for Big Data on AWS Corporate Training in Bangalore for your organization or aiming for an AWS Certified Big Data Certification to advance your career, Radical Technologies provides the expert guidance and comprehensive resources you need.
Join us at Radical Technologies to gain in-depth expertise through our Big Data on AWS Classes in Bangalore, backed by a curriculum aligned with the latest industry standards. With our dedication to quality education, Radical Technologies is the ideal destination for those pursuing Big Data AWS Certification and career growth in the field of Big Data on AWS.
For more details about our Big Data on AWS Training in Bangalore or to enroll in our Big Data AWS Course, contact us today and start your journey toward becoming an AWS-certified expert in Big Data!
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Course Features
- Lectures 0
- Quizzes 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
