![]() ![]() You will need four items to create an Athena Data Source: Choose ‘Amazon Athena’ from the list of available Database Drivers. You can configure the Athena Driver further, using the Options and Advanced tabs.įrom P圜harm’s Database Tool Window, select the Data Source dialog box to create a new connection to your Athena instance. Instructions for creating an Athena Driver starts on page 23.įrom P圜harm’s Database Tool Window, select the Drivers dialog box, select the downloaded Athena JDBC Driver JAR. The guide, as well as the Athena JDBC and ODBC Drivers, are produced by Simba Technologies (acquired by Magnitude Software). Considering Java 8 was released six years ago (March 2014), most users will likely want the AthenaJDBC42-2.0.9.jar is compatible with JDBC 4.2 and JDK 8.0 or later.ĪWS also supplies a JDBC Driver Installation and Configuration Guide. There are two versions, based on your choice of Java JDKs. To start, download the Athena JDBC Driver from Amazon. ![]() Utilizing the extensibility of the JetBrains suite of professional development IDEs, it is simple to add Amazon Athena to the list of available database drivers and make JDBC (Java Database Connectivity) connections to Athena instances on AWS. Both these IDEs also support many common SQL-based technologies, out-of-the-box, and are easily extendable to add new technologies. Although I work in a variety of IDEs, my go-to choices are JetBrains P圜harm for Python (including for PySpark and Jupyter Notebook development) and JetBrains IntelliJ for Java and Scala (including Apache Spark development). Within the domains of data science, big data analytics, and data analysis, languages such as SQL, Python, Java, Scala, and R are common. According to the PYPL Index, the ten most popular, current IDEs are: The choice of IDE may depend on one’s predominant programming language. Full-Featured IDEĪlthough the Athena Query Editor is fairly functional, many Engineers perform a majority of their software development work in a fuller-featured IDE. Access to AWS Glue data sources is also available from within the Editor. The Editor can convert SELECT queries to CREATE TABLE AS ( CTAS) and CREATE VIEW AS statements. Queries can be run directly from the Editor, saved for future reference, and query results downloaded. The Athena Query Editor has many of the basic features Data Engineers and Analysts expect, including SQL syntax highlighting, code auto-completion, and query formatting. In the previous post, Getting Started with Data Analysis on AWS using AWS Glue, Amazon Athena, and QuickSight, we used the Athena Query Editor to construct and test SQL queries against semi-structured data in an S3-based Data Lake. In addition to Presto, Athena also uses Apache Hive to define tables. Athena is ideal for quick, ad-hoc querying, but it can also handle complex analysis, including large joins, window functions, and arrays. According to AWS, the Athena query engine is based on Presto 0.172. The underlying technology behind Amazon Athena is Presto, the popular, open-source distributed SQL query engine for big data, created by Facebook. Amazon Athena supports and works with a variety of popular data file formats, including CSV, JSON, Apache ORC, Apache Avro, and Apache Parquet. Executing Amazon Athena Queries from JetBrains P圜harmĪccording to Amazon, Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. ![]()
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