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AWS Data Engineering in Hyderabad
Currently, there is a very high demand in the market for qualified professionals in AWS data engineering because of the rapidly changing technological environment. Our complete AWS Data Engineer training in Hyderabad helps you gain the necessary know-how for a competitive edge in today’s fast-paced field.
Why do we call it AWS data engineering?
The profession of skilled data handling and processing is known as Data Engineering for AWS. Such a field is crucial to organizations that would like to make sense out of collected information, conserve space for storage, and employ a data-driven approach. With more companies moving their operations to the cloud platforms, it is imperative that the work of AWS data engineers be effective for streamlined managing and utilizing of the data. Industry professionals design AWS Data Engineering training in Hyderabad. The participants are taught basics of AWS services such as provisioning of database functionality, storage and data analysis comprising of AWS glue, Amazon Redshift, and ML tool integration respectively. The theory is integrated with practice through practical projects and case studies that prepare learners to professionally integrate.
Why should you attend Version IT AWS data engineer training in Hyderabad?
The modern-day AWS Data Engineers’ course is offered by Hyderabad’s IT Version at a Center that offers quality, industry-relevant material and a networked platform for knowledge sharing. Participation in this course equips individuals with the skills they need as competent professionals who will address data-oriented challenges hence leading to career promotion or new paths in data engineering.
Topics You will Learn
- The rise of big data as a corporate asset
- The challenges of ever-growing datasets
- Data engineers – the big data enablers
- Understanding the role of the data engineer
- Understanding the role of the data scientist
- Understanding the role of the data analyst
- Understanding other common data-related roles
- The benefits of the cloud when building big data analytic solutions
- The evolution of data management for analytics
- Databases and data warehouses
- Dealing with big, unstructured data
- A lake on the cloud and a house on that lake
- Understanding data warehouses and data marts –fountains of truth
- Distributed storage and massively parallel processing
- Columnar data storage and efficient data compression
- Dimensional modeling in data warehouses
- Understanding the role of data marts
- Feeding data into the warehouse – ETL and ELT pipelines
- Building data lakes to tame the variety and volume of big data.
- Data lake logical architecture
- Bringing together the best of both worlds with the lake house architecture
- Data lakehouse implementations
- Building a data lakehouse on AWS
- Hands-on – configuring the AWS.
- Command Line Interface tool and creating an S3 bucket.
- Installing and configuring the AWS CLI
- Creating a new Amazon S3 bucket
- Overview of Amazon Database Migration Service (DMS)
- Overview of Amazon Kinesis for streaming data ingestion
- Overview of Amazon MSK for streaming data ingestion
- Overview of Amazon AppFlow for ingesting data from SaaS services
- Overview of Amazon Transfer Family for ingestion using FTP/SFTP protocols
- Overview of Amazon DataSync for ingesting from on-premises storage
- Overview of the AWS Snow family of devices for large data transfers
- Overview of AWS Lambda for light transformations
- Overview of AWS Glue for serverless Spark processing
- Overview of Amazon EMR for Hadoop ecosystem processing
- Overview of AWS Glue workflows for orchestrating Glue components
- Overview of AWS Step Functions for complex workflows
- Overview of Amazon managed workflows for Apache Airflow
- Overview of Amazon Athena for SQL queries in the data lake
- Overview of Amazon Redshift and Redshift Spectrum for data warehousing and data lakehouse architectures
- Overview of Amazon Quick Sight for visualizing data
- Understanding data sources
- Data variety
- Data volume
- Data velocity
- Data veracity
- Data value
- Questions to ask.
- Ingesting data from a relational database
- AWS Database Migration Service (DMS)
- AWS Glue
- Other ways to ingest data from a database.
- Creating a Lambda layer containing the AWS Data Wrangler library
- Creating new Amazon S3 buckets
- Creating an IAM policy and role for your Lambda function
- Creating a Lambda function
- Configuring our Lambda function to be triggered by an S3 upload
Let Your Certificates Speak
- Certified in AWS Data Analytics: Mastering Data Insights with Cloud-based Solutions.
- Certificates are globally recognized & they upgrade your programming profile.
- Certificates are generated after the completion of course.
All You Need to Start this Course
- Engaging and interactive course content.
- Expert-led instruction for a deeper understanding.
FAQ's
An AWS Data Engineer is in charge of developing, deploying, and maintaining AWS data processing systems and infrastructure. Data ingestion, storage, transformation, and analysis are examples of such jobs.
AWS offers a variety of data engineering services, including Amazon S3 (Simple Storage Service), Amazon Redshift (data warehouse), AWS Glue (ETL service), and Amazon EMR (Elastic MapReduce).
Amazon S3 is a scalable object storage service used to store and retrieve massive volumes of data. S3 is frequently used by data engineers as a data lake for storing raw and processed data.
An entirely managed extract, transform, and load (ETL) service is AWS Glue. Data engineers use ETL operations to automate the process of preparing and converting data for analysis using Amazon Glue.
