![]() ![]() The prototype also leaves room for future performance improvements, which could improve the accelerator to eliminate this slightly decreased compression ratio. Simulation results show that the hardware accelerator is capable of compressing data up to 100 times faster than software, at the cost of a slightly decreased compression ratio. A working prototype of the compression accelerator is designed and programmed, then simulated to asses its speed and compression performance. This work explores the possibility of using dedicated hardware to accelerate the same general-purpose compression algorithm normally run at the warehouse-scale computer level. Extract the year value from the date field using the Presto function substr(“date”,1,4).In the exa-scale age of big data, file size reduction via compression is ever more important. The table created in Step 1 has a date field with the date formatted as YYYYMMDD (e.g. You can add new data to this table using the INSERT INTO command. For the purpose of this blog, the initial table only includes data from 2015 to 2019. All these actions are performed using the CTAS statement. Now, convert the data to Parquet format with Snappy compression and partition the data on a yearly basis. 's3://aws-bigdata-blog/artifacts/athena-ctas-insert-into-blog/' Use CTAS to partition data and convert into parquet format with snappy compression This subset of data is available at the following S3 location:ĬREATE EXTERNAL TABLE `blogdb`.`original_csv` ( This example uses a subset of NOAA Global Historical Climatology Network Daily (GHCN-D), a publicly available dataset on Amazon S3, in this example. Add more data into the table using an INSERT INTO statement.Use a CTAS statement to create a new table in which the format, compression, partition fields and location of the new table can be specified.Create a table on the original dataset.Here is an overview of the ETL steps to be followed in Athena for data conversion: CTAS and INSERT INTO statements can be used together to perform an initial batch conversion of data as well as incremental updates to the existing table. If the source table’s underlying data is in CSV format and destination table’s data is in Parquet format, then INSERT INTO can easily transform and load data into destination table’s format. INSERT INTO statements insert new rows into a destination table based on a SELECT query statement that runs on a source table. As part of the execution, the resultant tables and partitions are added to the AWS Glue Data Catalog, making them immediately available for subsequent queries. You can also partition the data, specify compression, and convert the data into columnar formats like Apache Parquet and Apache ORC using CTAS statements. This example optimizes the dataset for analytics by partitioning it and converting it to a columnar data format using Create Table as Select (CTAS) and INSERT INTO statements.ĬTAS statements create new tables using standard SELECT queries to filter data as required. This blog post discusses how to use Athena for extract, transform and load (ETL) jobs for data processing. The main use case is to apply compression before writing data to disk or to network (that usually operate nowhere near GB/s). To learn more about best practices to boost query performance and reduce costs, see Top 10 Performance Tuning Tips for Amazon Athena. First, let's discuss what they have in common They are both algorithms that are designed to operate at 'wire' speed (order of 1 GB/s per core) when compressing and decompressing. You can reduce your per-query costs and get better performance by compressing, partitioning, and converting your data into columnar formats. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Amazon Athena is an interactive query service that makes it easy to analyze the data stored in Amazon S3 using standard SQL. ![]()
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