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Data Pipelines Presentation

It's important for many to have basic knowledge about Data pipelines, the presentation covers all the basics you need to know about the same.

Shine27
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Data Pipelines Presentation

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Presentation Transcript


  1. Data Pipelines Author - Shine V S

  2. 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 Next Slide

  3. 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, Next Slide

  4. 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 Next Slide

  5. 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 Next Slide

  6. 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 Next Slide

  7. 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 • Learn all these tools in detail with our Data Science Course in Kerala Next Slide

  8. Data Pipeline Architecture Example • Source → Kafka → Spark → Data Lake → DBT → Warehouse → BI Tools Next Slide

  9. Challenges in Data Pipelines • Data quality issues • Schema changes and data drift • Scalability and performance • Monitoring and failure recovery • Cost optimization in cloud pipelines. • Although there are some challenges here, there’s none for learning Data Science, if you’re interested checkout our Data Science Course in Kerala Next Slide

  10. 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.

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