ETL | Incremental Data Load from Amazon RDS MySQL to Amazon Redshift Using AWS Glue | Datawarehouse

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🚀 Dive into the world of seamless data integration with our step-by-step guide on performing incremental data loads from Amazon RDS MySQL to Amazon Redshift using AWS Glue! 🔄💡

In this comprehensive tutorial, we walk you through the entire process of setting up incremental data loads, ensuring that only the changes in your dataset are transferred, optimizing performance, and minimizing the impact on resources.

Key Highlights:
🔗 Understanding Incremental Data Loading: Learn the importance of incremental data loading and how it enhances the efficiency of your data pipeline.
🛠️ Configuring AWS Glue: Follow along as we guide you through the setup and configuration of AWS Glue for seamless data transformation and transfer.
🔄 Incremental Load Strategies: Explore different strategies for incremental data loading and choose the one that best fits your use case.
📊 Monitoring and Troubleshooting: Gain insights into monitoring your data pipeline and troubleshooting common issues to ensure a smooth and reliable operation.

Whether you're a data engineer, analyst, or anyone dealing with data integration, this tutorial provides valuable insights and practical tips to enhance your AWS Glue skills and optimize your data workflows.

👩‍💻 Don't miss out on the latest advancements in data management! Hit the play button now and elevate your AWS Glue expertise. Subscribe for more tutorials and stay ahead in the world of data engineering! 🚀🔗💻

#awsglue #rds #amazonredshift #DataIntegration
#amazonrdsmysq #dataintegration #dataengineering #incrementaldataload #aws #techtutorial #cloudquicklabs
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Thank you so much for the session. Its really helpful for the beginner like me..

JothiLakshmi-jv
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The reason it got appended into the target table is because, the "Matching Keys" involves all of the column. Had it been just the "industry_name_anzsic" in matching keys. It would have updated it. Actually, I think you assumed that the just the leftmost column is the Matching key which happens most of the time as left is usually the primary key column and we do merges and joins on it. Hence, This was a honest mistake happened due to old habits. Old habits die hard.

abhishekanand
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In your case, in each job run, it will grab all data(instead of new data only) from the rds table to redshift and then do the merge. Let's say the table is very big ---over several hundreds of Gigbytes, the operation will be very expensive. Correct? Can you add a sql filter transformation step in between to grab only the new data changed since the last job run, so that only the new data is merged?

liubrian
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hello, will it move the whole data from rds to Redshift or only a copy of rds data to Redshift?

preetybisht
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This is not a incremental approach infact, you are picking eevrything from source and target and comapring the keys.. Too too expensive... Anyhow your videos are very helpuful

beingYoda
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please tell me what are the policies you have attach in iam role

akshaygarg
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much thankful video, can you please share the script/code which was generated in etl glue

udaynayak-of
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hi brother
im able to collect data one by one through table but when im trying to establish connection through crowler its says unable to connect or establish connection then its unable to connect is that possible to add all tables at a time

ashishkamble
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Can we do the opposite way, that is, load data from Redshift to RDS postgreSQL? I tried but it doesn't work. Can you make it work and make a video?

senhuayu
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Please make a video on the pyspark script.

faisalmali
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That clicking sound from windows 98 is very distracting.

dimba
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