9/13/2023 0 Comments Redshift spectrum parquetWhen you are ingesting sources near real-time to S3 from structured databases using Change Data Capture methodology, you do need to merge the real-time changes with the previous data sync – so there is some data preparation involved. Anyone with decent SQL skills can analyze big datasets. Query results are delivered extremely fast and you can avoid the nuisance of complex ETL jobs to make your data ready for analytics. To use Athena, simply point to your data on S3, define schema and start querying using standard SQL. Athena is easy to use but requires some amount of data preparation Athena supports various S3 file formats including CSV, JSON, PARQUET, ORC and AVRO and allows for partitioning of data which is key to smooth large volume data queries.ĭownload our eBook: How to get siloed data to AWS Athena in just a few clicks. There is no infrastructure to manage and you only pay for the queries you run – pretty cost-effective if you ask us. You do not need to load data into Athena, you can use Athena to query your data on S3. It is simple to use since you can analyze data using standard SQL. Read about BryteFlow for AWS ETL AWS AthenaĪWS Athena is a fast, serverless, interactive query service. Before we go into details, here is a quick rundown about both of them. Both are part of the AWS environment so it is quite natural to be a bit confused about which one you should use. Amazon Athena and Redshift Spectrum are both AWS services that can run queries on Amazon S3 data.
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