ERROR 33 com.simba.athena.exceptions.ExceptionConverter.toSQLException: Error message not found: GENERAL_ERROR_WITH_STREAM_DETAIL. Can't find bundle for base name com.simba.athena.athena.jdbc42.messages, locale en_USjava.sql.SQLException: Error message not found: GENERAL_ERROR_WITH_STREAM_DETAIL. Can't find bundle for base name com.simba.athena.athena.jdbc42.messages, locale en_US

Please note, I am able to connect to Athena using my aws access id and secret access key and able to see databases and corresponding objects. The above error only shows up when I try to execute queries from tableau.

Tableau Athena Jdbc Driver Download

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Working on a Mac (intel chip), and as thread author, completed the driver installation steps as outlined here ( -us/examples_amazonathena.htm). Successfully able to connect to Athena and enter my custom query, but receiving the above error when trying to create an extract/executing the query.

Thanks for the update. Verified my access as well and S3 access looks good. However, I am curious about lambda access, why would we need it for tableau? Would you be able to share lambda access you provisioned?

I gave AWSLambda_FullAccess to the user as well. However, haven't tested if this made the difference. I think the S3 access was missing for me. I would just give it a test and see if it fixes your issue.

When Tableau Prep was initially launched, the only outputs supported were flat files and Tableau native formats TDE and Hyper. However, since the 2020.2.3 release in August 2020, you can output to database platforms such as SQL Server as well, making Tableau Prep a much more appealing tool. It allows you to do the following:

To follow along with the recipes in this chapter, you will require Tableau Prep Builder version 2020.2.3 or later. If you'd like to follow along and connect or write to a database, you must have the appropriate database set up and have an account with the appropriate access privileges.

In this recipe, we'll connect to a Comma-Separated Values (CSV) file containing sales transactions and create a second connection to multiple Excel files. These connection types are very similar and so we'll cover them in one recipe. However, there are key features to both, which we'll highlight.

Since the data connection is automatically selected, all options onscreen now relate to that particular connection. On the left-hand side, you can see the Tables pane. Note that there is only one table, equal to the filename. Since text files do not contain tables, this is by design. Tableau Prep Builder will always provide a generic user interface for data connections whenever possible. Once you're comfortable with one connection type, others should be easier to master:

In the bottom pane, you can find a summary of all data fields identified in your text file, along with the automatically determined data type. In Chapter 3, Cleaning Transformations, we'll dive into the cleaning options you can perform here:

Once we've selected the file, Tableau Prep Builder does not automatically show another data connection in the flow, as it did for our CSV file. This is the default behavior for any data connection that has multiple tables. In the case of Microsoft Excel, each Excel sheet is considered a table:

Tableau Prep text files and Microsoft Excel connections automatically detect most settings very well, so, in most cases, a couple of clicks will get you up and running. The most powerful feature is undoubtedly the ability to ingest multiple files at the same time. You can ingest hundreds of files at the same time using this method, using a single data connection.

In this recipe, we'll connect to a PDF file containing text and a table with data. Tableau Prep has an exciting feature that can automatically detect the presence of tables in PDF files and extract the data for you.

Tableau Prep converts each table in a PDF document into a data table when ingesting the file into a new flow. As such, Tableau Prep removes the complexity of parsing PDF documents and allows you to treat this like any other data connection.

I advocate the use of open file formats such as CSV or commonly used standards such as Excel. However, if you are unable to obtain your data in such a format from your data science partner, this connector may offer a solution.

Most options in the bottom pane are identical to those when processing Excel files. However, there is a small but important feature absent. You cannot alter the data type of the fields in the statistical file connection step. In order to do this, you have to use a cleaning step, which we'll discuss in Chapter 3, Cleaning Transformations:

There are some limitations when it comes to connecting to statistical files. If you run into any connection issues, I recommend you refer to the following section of the Tableau documentation online: -us/examples_statfile.htm.

In this recipe, we'll connect to a Microsoft SQL Server database. The many connectors provided out of the box by Tableau Prep allow you to connect to databases almost as easily as to file connections, allowing you to quickly start an advanced flow.

In order to follow along with this recipe, download the Sample Files 2.4 folder from the book's GitHub repository. This folder contains the Wide World Importers sample database backup (.BAK) file, which you can restore to your SQL Server instance.

Note that the provided BAK file is suitable for SQL Server 2016 SP1 or later. If you're running an earlier version or need instructions on installation, please consult Microsoft's support page at -us/sql/samples/wide-world-importers-oltp-install-configure.

Tableau Prep has a number of built-in database connections that remove the complexity of connecting by configuring connections such as ODBC manually. The number of supported data connection types is continually expanding, too. If you do not see your database listed, you can always opt to use an ODBC connection instead. See the recipe titled Connecting to JDBC or ODBC data sources in this chapter for more information.

Database tables can relate to each other and be joined to each other to create insightful datasets. We'll cover joins in detail in Chapter 5, Combining Data. For now, I want to highlight a great feature in Tableau Prep that indicates the table primary key and foreign keys in the bottom pane. You can find this information in the Linked Keys section as shown in the following screenshot:

In this recipe, we'll connect to a local Amazon AWS Athena database. Just like on-premises data connections, Tableau has made it as easy as possible to connect securely to cloud data sources. You'll find many connections for popular cloud providers including Microsoft, Google, and Amazon. Each data connection dialog has been customized to the technology you're attempting to connect to. This means you won't see irrelevant fields for the selected connection type, reducing the complexity of cloud connections.

a) The Server field for Athena needs to be populated with the region information. The format for this is athena.[region].amazonaws.com. For example, athena.us-east-1.amazonaws.com or athena.eu-west-1.amazonaws.com.

Similar to on-premises data connections, Tableau Prep provides a simplified user interface on top of the database driver, so you can easily configure the connection. In this recipe, we've used the Athena JDBC driver in the background and configuring it is as easy as any other connections.

Tableau has two popular proprietary data types, Tableau Data Extract (.tde) and Tableau Hyper Extract (.hyper). Neither format can easily be read, if at all, by most data pipeline and ETL tools. With Tableau Prep Builder, however, you can easily use a Tableau data extract as an input into your flow. In this recipe, we'll connect to a hyper extract. The steps are identical when connecting to a TDE extract.

As you've seen in this recipe, connecting to Tableau extracts is very straightforward, as you might expect from the company's own data source type. If you're fully into the Tableau ecosystem and using products such as Prep, Desktop, and Server, extracts are a great way to manage data and performance.

Although Tableau Prep provides out-of-the-box connections to many popular data sources, there's always a chance you might be using another data source type. If this is the case, you can use JDBC and ODBC connections instead.

In order to use such connections, you must first install the requisite driver on the machine running Tableau Prep Builder and configure the connection outside of Tableau Prep. If you intend to publish your flow to Tableau Server, you must also create the same connection on the server itself, using the same DSN.

Using JDBC and ODBC tells Tableau Prep to go to your system's respective data source connections, as referred to by the DSN name, and leverage the connection as configured. However, Tableau Prep cannot control these connections, its settings, or the drivers used. Therefore, they are not supported and should be used with caution.

In this recipe, we'll create an output to a file. There are two file outputs supported by Tableau Prep, Comma-Separated Values (.csv) files and Tableau extracts (.hyper). When you're planning to perform downstream analysis with Tableau Desktop, I recommend using Tableau extracts as they have great performance benefits. If, however, you're utilizing it for any other purposes, CSV is a great open format to utilize.

Using the Output tool, you can easily write data to CSV and hyper files for use in other analytics applications. If you save your output to Tableau Server, this is a great way to maintain those outputs.

When Tableau Prep was launched, it was only able to output data to files, including hyper extracts. Thankfully, Tableau introduced functionality to write to external databases in release 2020.3. With it, you can write the output of your flow directly to a database. 152ee80cbc

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