AWS Data Engineering with Data Analytics Online Training in Ameerpet

1. AWS Data Engineering with Data Analytics: From Basic Concepts to Advanced Techniques Amazon Web Services(AWS) offers a comprehensive set of tools for data engineering and analytics, allowing organizations to efficiently handle data at scale. Whether you're building data pipelines, transforming raw data, or analyzing large datasets, AWS provides the foundation to architect data-driven solutions. This guide will explore AWS data engineering concepts and progress to advanced data analytics techniques. AWS Data Engineer Training Basic Concepts in AWS Data Engineering 1. Amazon S3: Centralized Data Storage At the heart of AWS data engineering is Amazon S3 (Simple Storage Service). S3 is a scalable object storage solution designed to hold structured and unstructured data. It’s the primary storage for raw data, often acting as the data lake for organizations. •Use case: Data lakes, log storage, backups, and raw data ingestion. •Key features: Durability, scalability, and integration with analytics tools like Amazon Athena and Redshift. 2. AWS Glue: Data Preparation and ETL

2. AWS Glue is a fully managed ETL (Extract, Transform, Load) service that prepares and transforms data before analysis. Glue simplifies building data pipelines by automating the process of discovering, cleaning, and cataloguing data from different sources. AWS Data Engineering Training in Hyderabad •Use case: ETL pipelines, data cataloguing, and data transformation. •Key feature: Serverless ETL with auto-scaling capabilities. 3. Amazon RDS: Relational Databases For structured data that requires SQL-based querying, Amazon RDS (Relational Database Service) provides managed relational databases like MySQL, PostgreSQL, and SQL Server. RDS is commonly used for operational databases in data pipelines, storing transactional data, and performing structured queries. •Use case: Storing processed data, real-time transactional data. •Key feature: Automated backups, scaling, and replication. Intermediate Data Engineering Techniques 4. Data Pipelines with AWS Data Pipeline AWS Data Pipeline is a service for orchestrating data workflows across multiple AWS services. It automates the movement and transformation of data between different services like S3, RDS, DynamoDB, and on-premise data sources. With Data Pipeline, you can schedule tasks and build complex data workflows without the need for manual intervention. •Use case: Data transformation, scheduled data migrations, and workflow automation. •Key feature: Built-in fault tolerance, retries, and scheduling. 5. Amazon Kinesis: Real-Time Data Streaming For real-time data ingestion and processing, Amazon Kinesis is a fully managed service designed for streaming large volumes of data in real time. It’s commonly used for processing log data, IoT device data, and clickstreams in near real-time. Kinesis can stream data directly to S3, Redshift, or even trigger AWS Lambda functions for further processing. •Use case: Real-time analytics, log processing, and event-driven architectures.

3. •Key feature: High throughput with low latency, auto-scaling for varying data streams. Advanced-Data Engineering and Analytics Techniques 6. Amazon Redshift: Data Warehousing for Analytics Amazon Redshift is AWS’s managed data warehouse service designed for running complex queries on large datasets. Redshift is highly optimized for online analytical processing (OLAP) and is commonly used for running analytics queries on structured data, particularly in business intelligence (BI) and reporting. AWS Data Engineering Course •Use case: Large-scale data analytics, business intelligence, and reporting. •Key feature: Massively parallel processing (MPP) for fast query performance on large datasets. 7. AWS Lake Formation: Building Data Lakes AWS Lake Formation simplifies the process of building secure and scalable data lakes on Amazon S3. It allows you to ingest, catalogue, and secure data from multiple sources, transforming raw data into a centralized, searchable repository. With built-in permissions and encryption features, Lake Formation provides governance and security for data lakes. •Use case: Centralizing data storage, combining structured and unstructured data, and creating secure data lakes. •Key features: Unified security controls, data cataloguing, and built-in data governance. 8. Advanced Analytics with Amazon Athena Amazon Athena is a serverless query service that enables you to analyze data stored in Amazon S3 using standard SQL. It’s ideal for running ad-hoc queries on large datasets without the need to set up or manage infrastructure. Athena integrates with AWS Glue for cataloguing data, making it easier to query large- scale datasets directly from S3. •Use case: Ad-hoc queries, interactive data analysis, and log analytics. •Key feature: Serverless architecture with pay-as-you-go pricing. 9. Machine Learning with Amazon SageMaker

4. For advanced predictive analytics, Amazon SageMaker provides a fully managed machine learning service. Data engineers can integrate SageMaker into their pipelines to build, train, and deploy machine learning models on large datasets. •Use case: Predictive analytics, recommendation systems, and anomaly detection. •Key feature: Fully managed machine learning pipeline from data preparation to model deployment. AWS Data Engineering Training Institute Conclusion: AWS offers a powerful ecosystem for data engineering and analytics, allowing you to build scalable and automated data pipelines that deliver meaningful insights. From basic storage and ETL processes to advanced real-time analytics and machine learning, AWS data engineering tools enable organizations to unlock the full potential of their data. By mastering these concepts and techniques, you can ensure efficient data management and drive smarter business decisions across your organization. Visualpath is the Best Software Online Training Institute in Hyderabad. Avail complete AWS Data Engineering with Data Analytics worldwide. You will get the best course at an affordable cost. Attend Free Demo Call on - +91-9989971070. WhatsApp: https://www.whatsapp.com/catalog/917032290546/ Visit blog: https://visualpathblogs.com/ Visit https://www.visualpath.in/aws-data-engineering-with-data-analytics- training.html