Use Redshift when
- Traditional data warehouse
- When you need the data relatively hot for analytics such as BI
- When there is no data engineering team
- When your queries require joins
- When you need a cluster 24X7
- When you data type are simple, i.e not Arrays, or Structs
- When data has no nested jsons
- When you have petabyte scale database
- When you want analize massive amount of data (spectrum)
- When you need update/delete
- When you require and ACID DBMS
- When you need a transient cluster, for night or hourly automation
- When compute elasticity is important (auto scaling on tasks)
- When cost is important: spot instances.
- When you data scales until a few hundred TB’s
- When you want to decouple compute and storage (external table + task node + auto scaling). this is cloud architecture best practice.
- When you require more flexibility
- Complex partitions + dynamic partitioning + insert overwrite. click on the link for an example.
- Complex data type
- Arrays <–> nested json
- Orchestration built in such as Oozie, although Airflow is more common.
- Notebook built in – mix your code with SQL via Zeppelin
Watch this meetup video to understand in depth Big Data Architecture conciderations in AWS.
Please check below Redshift specific faq:
Q: When would I use Amazon Redshift vs. Amazon EMR?
Q: Can Redshift Spectrum replace Amazon EMR?
Q: Can I use Redshift Spectrum to query data that I process using Amazon EMR?
— Reference : Redshift faq
Please check below EMR specific faq:
Q: What can I do with Amazon EMR?
Q: Who can use Amazon EMR?
Q: What can I do with Amazon EMR that I could not do before?
Q: What is the data processing engine behind Amazon EMR?
Q: What is Apache Spark?
Q: What is Presto?
— Reference : EMR faq
** Point 2. I am listing other resources which can help to understand RDS and EMR use cases better.
— Reference :
AWS redshift related case studies > Look for case study section :
— Reference :
AWS EMR related case studies > Look for case study section :
** Point 3. I have tried to check some of AWS blogs which shows how EMR and RDS can be used together in specific use cases.
— How I built a data warehouse using Amazon Redshift and AWS services in record time
— Build a Healthcare Data Warehouse Using Amazon EMR, Amazon Redshift, AWS Lambda, and OMOP
— Powering Amazon Redshift Analytics with Apache Spark and Amazon Machine Learning
Hope this information helps in understanding EMR and Redshift use cases better.
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