Data Pipelines
                     Data 
Pipelines
Author - Shine V S
Contents
• Data Pipeline definition & it’s Importance
• Key Components of Data Pipeline
• ETL vs ELT Comparison
• Types of Data Pipelines
• Data Pipeline Tools & Technologies
• Data Pipeline Architecture Example
• Challenges in Data Pipelines
• Conclusion 
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Data Pipeline definition & it’s 
Advantages
• Data Pipeline is defined as series of steps used to move data from source to destination
• It ingests data from various sources, processes it through steps like cleaning and 
reformatting and delivers it to a destination for analysis.
• Advantages of Data Pipelines are - it enables automation and consistency in data 
handling, supports real time analytics and decision making, reduces manual effort and 
human errors, scales with increasing data volume. If you’re interested in Data Science as 
career do check out our Data Science Course in Kerala, 
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Key Components of Data Pipeline
• Data Sources: which consists of Databases, APIs, loT, files
• Ingestion Layer which collects data from data sources. It consists of ETL/ELT tools and 
streaming services
• Processing Layer transforms raw data into usable format using processes - 
Transformation, Cleaning, Enrichment
• Storage Layer stores data for later analysis, acting as destination after data is ingested 
and processed
• Orchestration for Scheduling and Monitoring for performance tracking
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ETL vs ELT Comparison
• ETL stands for Extract, Transform, Load while ELT stands for Extract, Load, Transform
• ETL transforms before loading while ELT transforms after loading
• ETL is best for structured data while ELT is best for Large + Mixed data
• Tools  used for ETL are informatica, Talend, for ELT are Snowflake, BigQuery, Every tool 
is a big topic which you’ll learn in our Data Science Course in Kerala
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Types of Data Pipelines
• Batch Pipelines for Scheduled, Periodic processing
• Real - time/Streaming Pipelines for Continous data flow
• Hybrid Pipelines for Combine batch + streaming
• There are lot to learn about each pipeline, if you’re interested do check out our 
Data Science Course in Kerala
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Data Pipeline Tools & Technologies
• For Ingestion: Kafka, AWS Kinesis, Airbyte
• For Processing: Apache Spark, DBT, Apache Beam
• For Storage: Snowflake, Data Lake, BigQuery, Redshift
• For Orchestration: Airflow, Prefect, Dagster
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Data Pipeline Architecture Example
• Source → Kafka → Spark → Data Lake → DBT → Warehouse → BI Tools
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Challenges in Data Pipelines
• Data quality issues
• Schema changes and data drift
• Scalability and performance
• Monitoring and failure recovery
• Cost optimization in cloud pipelines. 
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Conclusion
Data pipelines are essential for handling large data and to help orgainizations become data 
driven. While it offers many advantages like enabling automation and conmsistency, 
aupporting real time decision making thereby reducing manual efforts. Some challenges are 
alos there like data quality issues, cost optimization which requires further optimization.
That’s all for this presentation on Data pipelines, hope you liked it. If you’re interested in Data 
Science and want to learn and start career in it, join our Data Science Course in Kerala.