You simply point Athena to your data stored on Amazon S3 and you’re good to go. Setting up Amazon AthenaĪmazon Athena is easy to set up it is a serverless service which can be accessed directly from the AWS Management Console with a few clicks. Please read our blog Face off: AWS Athena vs Redshift Spectrum – which service you should use and when. Your AWS ETL options with AWS Glue are explained in our blog if you need to ingest or transform your data on S3. You may need to access data on S3 via an API or via Redshift Spectrum or other means. Hence, if you have several users needing to do interactive queries or using dashboards on data on Amazon S3, this may not be the solution for you. There are service limits imposed by AWS – you need to refer to these before deciding if this works for you. There are certain restrictions imposed by AWS on user access to Athena, which you should be aware of. Read about BryteFlow for AWS ETL Things to know regarding user access on Amazon Athena Create an S3 Data Lake in Minutes (turorial with vidoes) You also can run queries in parallel, Athena simply scales up without a fuss and results are lightning-fast even with huge datasets. And you pay only for the queries you run which makes it extremely cost-effective. Being a serverless service, you can use Athena without setting up or managing any infrastructure. You can query data on Amazon Simple Storage Service (Amazon S3) with Athena using standard SQL. Need to query data on Amazon S3 directly? Amazon Athena is the interactive AWS service that makes it possible. Let’s first understand about Athena and then dive into performance tuning.ĭownload our eBook: How to get siloed data to AWS Athena in just a few clicks. It is important to understand how Amazon Athena works, and the tweaks you can make now, so that you can derive the best performance and lower your costs. See Editing a package in the Quilt docs for more.How do you tune your Amazon Athena query performance? p.push( "akarve/heroes", message="annotate dataset", registry="s3://YOUR_BUCKET" ) import quilt3 as q3 p = q3.Package() p.set_meta(meta_sample) #. Here’s how it all comes together to land your data and its metadata to S3. It’s a one-liner to attach metadata to a package in Quilt, p.set_meta(meta_sample). Quilt packages unify data and metadata as an immutable bundle so that datasets are more durable, meaningful, and reusable. Some businesses are so tired of meaningless data that they automatically delete unlabeled data after 30 days. How to associate metadata with a datasetĭatasets without metadata (as labels and documentation) quickly become meaningless. If you’re curious to understand what’s happening under the hood, put a SELECT * in place of SELECT heroes.name, heroes.powers, heroes.id. We then select only the heroes.WHATEVER columns to hide record and the unnested heroes column (which contains row objects that look like maps). UNNEST transposes our array into a column with three values (one for each array element). Now our query produces a proper table from our JSON input: name powers id Thor 1 Iron Man 2 Spider-Man 3 But how does that CROSS JOIN work?ĬROSS JOIN generates a cartesian product, but what are the two sets in play? On the left we have raw with a single column, record, and a single row. The full query solution WITH raw AS ( SELECT CAST(json_extract(user_meta, '$.hero_table') AS ARRAY(ROW(name VARCHAR, powers ARRAY(VARCHAR), id INTEGER))) AS record FROM "YOUR_DATABASE_HERE" WHERE json_extract_scalar(user_meta, '$.longitude') LIKE 'athena%' ) SELECT heroes.name, heroes.powers, heroes.id FROM raw CROSS JOIN UNNEST(record) AS t(heroes) Append CROSS JOIN UNNEST(record) AS t(heroes) to our query and voila. You’ll typically see UNNEST with a CROSS JOIN. We use UNNEST to transpose our array into a column. Suppose we have the following metadata: meta_sample = ] UNNEST unpacks arrays and maps into relations. It’s common to annotate datasets with JSON. Convert JSON objects in S3 to rows in Athena or Presto SQL
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