
Sales Analytics Pipeline Using Microsoft Fabric
To build an end-to-end data pipeline using ingestion, transformation, and visualization of sales data.
Case Studies and Projects15+
Hours of Practical Training80+
Placement Assurance100%
Expert Support24/7
Support & AccessLifetime
CertificationYes
Skill LevelAll
LanguageEnglish / Tamil
To build an end-to-end data pipeline using ingestion, transformation, and visualization of sales data.
Creating a unified customer profile system with real-time data Ingestion from various data sources.
Ingestion and analysis of IoT sensor data to detect anomalies and monitor system performance.
In-demand skills – will be covered during the course to make you a perfect AWS Data Engineering
In-demand Tools – will be covered during the course to make you a perfect AWS Data Engineering
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AWS data pipeline architecture typically includes the following stages:
Ingestion:
Processing: AWS Glue, Amazon EMR, AWS Lambda, Kinesis Data Analytics
Storage:
AWS IAM secures data pipelines by:
Feature | Batch Processing | Real-time Processing |
---|---|---|
Latency | High (minutes to hours) | Low (ms to seconds) |
Tools | AWS Glue, EMR, Data Pipeline | Kinesis, Lambda, Flink, Firehose |
Use Cases | Reports, backups, ETL | Fraud detection, live dashboards |
Cost | Generally cheaper | May be higher due to always-on |
Complexity | Lower | Higher (requires monitoring) |
Amazon Kinesis handles real-time data streaming.
Use Cases: Fraud detection, dashboards, IoT analytics
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Join NowAWS Data Engineering focuses on designing, building, and managing large-scale data processing systems using Amazon Web Services. This involves:
Data engineers use AWS services to build scalable, cost-effective, and secure data pipelines.
Yes, you can attend free demo classes which help you understand the AWS Data Engineering syllabus, course structure, and class sessions.
To book our AWS Data Engineering course, Call Us:
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It’s not mandatory, but basic coding knowledge in Python and SQL is highly recommended. These are commonly used in scripting, ETL logic, and data transformation in AWS Data Engineering.
The core AWS services used by data engineers include:
Amazon S3 (Simple Storage Service) is a highly scalable object storage service used in data pipelines as a data lake for raw, processed, and curated data.
S3 is often the landing zone for incoming data and integrates with services like Glue, EMR, Redshift Spectrum, and Athena. It supports formats like CSV, JSON, Parquet, Avro, and ORC.
Feature | AWS Glue | AWS Data Pipeline |
---|---|---|
Type | Serverless ETL service | ETL and data movement orchestration |
Language Support | Python, PySpark, Scala | Shell script, SQL |
Use Cases | Data cataloging, ETL, schema inference | Moving data between AWS services, batch jobs |
Serverless | Yes | No (EC2/EMR provisioning required) |
Modern Use | Preferred for modern ETL | Older, slowly replaced by Glue/MWAA |
Amazon EMR is a cloud big data platform that processes large datasets using Hadoop, Spark, Hive, HBase, Presto, Flink, etc. It automatically scales and runs distributed jobs.
Use Cases:
AWS Glue supports structured and unstructured data formats like:
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Credo Systemz AWS Data Engineering certification training in Chennai offers industry recognized certification for those who complete this course successfully. Our AWS Data Engineering training showcases the in-built knowledge of AWS Data Engineering to stand out in the job market.
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