AWS Data Engineering Online Training - Visualpath

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Data Engineering leveraging AWS Analytics features
91-9989971070
www.visualpath.in
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• Leveraging AWS analytics features is essential
  for effective data engineering in the cloud. AWS
  offers a wide range of services that enable data
  engineers to collect, store, process, and analyse
  data efficiently. Here's how you can use AWS
  analytics features for data engineering
• Data Ingestion
• Amazon Kinesis Use Kinesis to collect and
  process real-time streaming data. Kinesis Data
  Streams, Data Firehouse, and Data Analytics
  provide various options for data ingestion and
  analysis.

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• Data Storage
• Amazon S3 Store data in Amazon S3, which is a
  scalable object storage service. It serves as a
  cost-effective and durable data lake for various
  data types, including structured,
  semi-structured, and unstructured data.
• Data Transformation
• AWS Glue AWS Glue is a managed ETL service that
  automates the process of extracting,
  transforming, and loading data. You can use it to
  prepare data for analytics and machine learning.

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• Big Data Processing
• Amazon EMR EMR is a big data platform that
  allows you to run distributed data processing
  frameworks like Apache Hardtop and Apache Spark
  to analyse large datasets.
• Data Warehousing
• Amazon Redshift Amazon Redshift is a fully
  managed data warehousing service that enables you
  to run high-performance analytics on structured
  data.
• Server less Data Analysis
• Amazon Athena Athena allows you to query data
  stored in Amazon S3 using standard SQL without
  the need to set up servers. It's particularly
  useful for ad-hoc querying and analysis.

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• Managed Databases
• Amazon RDS and Amazon Aurora These services
  provide managed relational databases, which can
  be used for storing structured data.
• Real-Time Analytics
• Amazon Kinesis Data Analytics This service
  allows you to analyse streaming data in
  real-time. It's ideal for applications that
  require immediate insights from data streams.
• Data Catalog and Metadata Management
• AWS Glue Data Catalog The data catalog stores
  metadata information about your data assets,
  making it easier to discover and manage data.

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• Data Visualization
• Amazon Quick Sight Use Quick Sight to build
  interactive dashboards and reports for data
  visualization and sharing insights.
• Machine Learning Integration
• Amazon Sage Maker Sage Maker simplifies the
  process of building, training, and deploying
  machine learning models using your data.
• Data Governance and Security
• Leverage AWS Identity and Access Management (IAM)
  for access control, AWS Key Management Service
  (KMS) for data encryption, and AWS Cloud Trail
  for auditing and compliance.

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• Data Pipeline Orchestration
• AWS Step Functions You can use Step Functions to
  coordinate multiple AWS services into server less
  workflows for data processing and analytics.
• Cost Optimization AWS offers tools like Cost
  Explorer and AWS Budgets to monitor and manage
  your data analytics costs
• Scaling and Auto-Scaling Use AWS Auto Scaling to
  automatically adjust the capacity of resources in
  response to changing demand for data processing.
• Managed Serverless Compute Serverless services
  like AWS Lambda can be used for data processing
  tasks, triggered by events or scheduled jobs.
• Streaming Data Analytics For complex stream
  analytics, you can use Amazon Kinesis Data
  Analytics and AWS Lambda to process and transform
  streaming data in real-time.

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CONTACT
For More Information About AWS Data Engineering
Training Address- Flat no 205, 2nd Floor,
Nilgiri Block,
Aditya Enclave,
Ameerpet, Hyderabad-16 Ph No
91-9989971070 Visit www.visualpath.in E-Mail
online_at_visualpath.in
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THANK YOU
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