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What's the best way to send real-time data to Amazon Redshift?

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I need an analysis tool for a product that sends logs or data about advertisements on the product's website. The product sends around 100,000 events per minute or more. All of the data is important for the analysis, and therefore, I can't afford the loss of data. What is the best way to send this data to Amazon Redshift, considering factors, such as performance efficiency, data consistency, and cost optimization?

2 Answers
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Accepted Answer

If the data is streamed through Amazon Kinesis Data Streams (KDS) , choose one of the following options:

  1. Kinesis Data Streams --> Lambda using the Redshift Data API --> Redshift
  2. Kinesis Data Streams --> Kinesis Firehose --> Redshift
  3. Kinesis Data Streams --> Kinesis Firehose --> Amazon S3 (partitioned) <-- Redshift Spectrum (run AWS Glue crawler periodically)

With all of these options, the data can be queried as soon as it's received. However, you might have to occasionally wait for a considerable amount of time before querying the data.

For a more cost-effective approach, do the following: First, write the data to Amazon S3 through Kinesis Data Streams --> Kinesis Firehose --> S3 --> Lambda --> S3 (optimized to Parquet or ORC). Then, run the AWS Glue crawler at periodic intervals (example: every hour) to refresh the AWS Glue Data Catalog. Query the data from Redshift with Amazon Spectrum Spectrum using the AWS Glue Data Catalog.

answered 2 years ago
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Alternate approach, use Redshift federated query feature to access, analyze and join real-time data from operational / transactional DB such as, Amazon Aurora or Amazon RDS PostgreSQL and MySQL DB with data warehouse and data lake dataset. For more details, refer to the documentation https://docs.aws.amazon.com/redshift/latest/dg/federated-overview.html.

answered a year ago

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