Tableau helps organizations to collaborate and uncover insights by providing interactive visualizations of huge amounts of data. Extracting data is very essential for the optimal performance of Tableau because it reduces the load on its server by compressing the data. In this blog, you will learn 2 easy methods to set up your Tableau Extracts with ease!
Table of Contents
What is Tableau?
Tableau is a BI and data analysis platform founded in 2003 which went on to become very popular because every corporate wanted to study its data in a short time frame and collaborate with the employees in the organization. Visualization is a great way to analyze a huge amount of data and that is exactly what Tableau does. Tableau has helped leading industries to cut down their analysis time and make their business more data-driven while ensuring flexibility, security, and reliability. Tableau helps its customers to ask questions and seek answers from them.
For further information on Tableau, you can check the official website here.
Key features of Tableau
- Advanced Dashboard: Tableau Dashboards provide an in-depth view of the data using advanced visualizations. Dashboards are considered to be very informative as they support the addition of multiple views and objects. Tableau also allows visualization of data in the form of Stories by giving users a variety of layouts and formats to choose from.
- In-Memory and Live Data: Tableau ensures seamless connectivity with data extracted from numerous external data sources in the form of In-memory or lives data sources. This gives users the ability to analyze data from various data sources without any restrictions.
- Attractive Visualizations: Tableau gives users the ability to create different types of data visualizations. For example, users can seamlessly create the simplest visualizations such as a Pie Chart or Bar Chart or some of the most complex visualizations such as Bullet charts, Gantt charts, Boxplot, etc. Tableau also comes with information on geographical data such as Countries, Cities, Postal Codes, etc. that allows users to build visualizations using informative maps.
- Robust Security: Tableau implements special measures to ensure user and data security. It houses a security system based on permission and authentication mechanisms for user access and data connections.
- Predictive Analytics: Tableau houses several data modeling capabilities, including forecasting and trending. Users can easily add a trend line or forecast data for any chart, and view details describing the fit easily.
What is a Tableau Data Extracts (TDE)?
Tableau Extracts is a compressed snapshot of data stored on the disc that is loaded into memory when a Tableau visualization is needed. For a working definition, that’s reasonable. The full story, on the other hand, is far more interesting and powerful.
Tableau Extracts are ideal for supporting analytics and data discovery due to two aspects of their design. Tableau Extracts is a columnar store, for starters. I won’t go into detail about columnar stores because many excellent documents, such as this one, already do so.
Let us at least agree that columnar databases store column values together rather than row values. As a result, the input/output required to access and aggregate the values in a column is drastically reduced. That’s what makes them so useful for data discovery and analytics.
The structure of Tableau Extracts is the second important aspect of their design, as it affects how they are loaded into memory and used by Tableau. This is a crucial aspect of Tableau Extracts “architecture awareness.” Tableau Extracts that are architecture-aware use all parts of your computer’s memory, from RAM to hard disc, and put each part to work in the most efficient way possible.
We’ll walk through how a TDE is created and then used as the data source for one or more visualizations to better understand this aspect of Tableau Extracts.
Tableau first defines the structure of the Tableau Extracts and then creates separate files for each column in the underlying source when creating a data extract. (This is why limiting the number of data source columns to extract is advantageous.)
Tableau sorts compresses and adds the values for each column to their respective files as it retrieves data. Sorting and compression happen earlier in the process in version 8.2, which speeds up the process and reduces the amount of temporary disc space used for extract creation.
People frequently inquire whether a Tableau Extracts is decompressed as it is loaded into memory. No, that is not the case. The compression used to make Tableau Extracts more efficient by reducing their storage requirements is not file compression.
Rather, dictionary compression (where common column values are replaced with smaller token values), run-length encoding, frame of reference encoding, and delta encoding are used (you can read more about these compression techniques here). However, if you’re planning to email or copy a Tableau Extracts to a remote location, you can use good old file compression to further reduce its size.
Individual column files and metadata are combined to form a memory-mapped file — or, to be more precise, a single file containing as many individual memory-mapped files as there are columns in the underlying data source. This is a critical component of the architecture-awareness it has carefully engineered. (Even if you’ve never heard of memory-mapped files, you’ve probably seen them.) Any modern operating system contains them (OS).
Because a Tableau Extracts is a memory-mapped file, when Tableau requests data from one, the operating system loads the data directly into memory. To use the Tableau Extracts, Tableau does not need to open, process, or decompress it. If necessary, the operating system will continue to move data in and out of RAM to ensure that Tableau has access to all of the requested data. This is a crucial point: it means Tableau can query data that is larger than the machine’s available RAM!
Only the data for the requested columns are loaded into memory. There are, however, some more subtle optimizations. A common OS-level optimization is to recognize when access to data in a memory-mapped file is contiguous and, as a result, read ahead to improve access speed. An OS will only load memory-mapped files once, regardless of how many users or visualizations access them.
The hardware requirements — and thus the costs — of a Tableau Server deployment are kept reasonable because it isn’t necessary to load the entire contents of Tableau Extracts into memory for them to be used.
For understanding this article you need to be equipped with the following skills:
- Good working knowledge of Tableau.
- A Tableau working account.
- Admin privileges associated with Tableau.
What are the Methods to Extract Data from Tableau?
Method 1: Setting up Tableau Extracts Manually
Let’s talk about what an Extract is. A Tableau Extracts is a technical term used when a huge set of data is compressed and stored for improved server performance. Depending on the version you are using, you can extract data from Tableau in different storage formats.
For versions 2020.2 and above, Tableau supports Extracts to be stored as physical tables (normalized schema) and logical tables (normalized schema). For versions 10.5 and above the data is stored in .hyper format. Tableau claims that the Extracts from .hyper format have the advantage of faster analysis and query performance on huge data sets.
Steps to Retrieve Data using Tableau Extracts
Step 1: Connect to your Data Source
Open your Tableau workflow and connect to your data source. Connect feature in Tableau allows you to connect data from multiple sources like spreadsheets, text files, big data, data in the cloud data warehouse, and in-house data warehouse and databases.
For users of Tableau Desktop, refer to the official page to connect to your preferred database. For users of Tableau Server and Tableau Online, refer to the official page to connect to your preferred database.
In case you cannot find a built-in Tableau connector for your preferred data source you can contact the Tableau Community here.
Step 2: Plan the Data Source
This step is crucial if you want to re-use this source for analysis. Planning the data source enables Tableau to interpret and interact with data the way you want for data analysis. The data source resembles a link connecting Tableau and your database. It may contain Extracts, connection properties, names of tables and sheets, and the transformations you make to your data.
Planning the data source allows you to blend, union, join, clean, filter data, and much more. Know more about this in detail here.
Step 3: Configuring the Tableau Extracts
Once a connection to your data source has been established then you can move on to creating a Tableau Extracts. On the upper right corner of your workflow, there is an option called Extract. Select it and then click Edit and open the Extract dialogue box.
Now in the dialogue box that pops up, you can configure the data storage format or define filters or define the limit of data to be extracted. In the image given below, you can see how the dialogue box looks:
Now after configuring, click the “OK” button.
Step 4: Creating Data Extracts in Tableau
Click on the sheet tab. Now, choose the folder where you want to save the Tableau Extract and then click Save. You will now be able to see the Tableau Extracts getting created on your screen.
In case you want to learn more about working with Tableau Extracts, you can click here to check out the official documentation.
Limitations of using Tableau Extracts
- The Extract from Tableau can only be stored on your local computer.
- If you need to create other Extracts you have to do them again manually.
“Loading data from Tableau to your desired destination such as a data warehouse can be a challenging task, even for experienced professionals! This is where Hevo saves the day!”
Method 2: Setting up Tableau Extracts using Hevo’s No-code Data Pipelines
Hevo is a No-code Data Pipeline that lets you load data from Tableau (among 100+ data sources) to a destination of your choice in real-time. It is fully managed and secure. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.
Steps to Extract data from Tableau using Hevo
Step 1: Connect Hevo with your Desired Data Source
Once you sign up for Hevo you can log into your account and create a pipeline. The first step is to choose a source. Hevo provides a huge variety of sources which comprise databases, data warehouses, analytical software, and BI tools. Choose Tableau from the given set of sources. Take a look at the integrations Hevo offers.
In this step, you can name your pipeline and provide the technical connection information. In case you feel stuck, you can click here to refer to our comprehensive documentation that will help you get started with ease! You can also leverage our 24/7 customer support to clear out any queries.
Step 2: Integrate Hevo with your Desired Destination
Hevo also provides enormous options to choose from as the destinations. Here, choose the destination of your choice. Once you are done with this, a pipeline is created and data starts moving from Tableau to the destination that you have chosen.
What makes Hevo Incredible
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
What are the latest changes in Tableau Extracts?
- Extracts in the web: Extracts are now available in web authoring and content server, starting with version 2020.4. You can now extract data sources without using Tableau Desktop.
- Logical and physical table extracts: Extract storage options have changed from Single Table to Multiple Tables to Logical Tables and Physical Tables since the introduction of logical tables and physical tables in the Tableau data model in version 2020.2. These options provide a more detailed description of how extracts will be kept.
- When you create a new extract in version 10.5, it is saved in the.hyper format. The improved data engine, which supports faster analytical and query performance for larger data sets, is used to benefit extracts in the.hyper format.
- A.tde extract is upgraded to a.hyper extract when an extract-related task is performed on it using version 10.5 or later. It is not possible to revert a.tde extract back to a.hyper extract once it has been upgraded.
- Changes to values and marks in the view:
Values in extracts can be computed differently in versions 10.5 and later compared to versions 10.4 and earlier to improve extract efficiency and scalability. Changes in the way values are computed can affect how your view’s marks are populated. The changes may cause your view to change shape or become blank in some rare cases. Multi-connection data sources, data sources that use live connections to filed-based data, data sources that connect to Google Sheets data, cloud-based data sources, extract-only data sources, and WDC data sources are all affected by these changes.
See the sections below for an overview of some of the differences you might notice in your view when using version 2022.1.
Format of date and date time values
Extracts are subject to more consistent and stricter rules around how date strings are interpreted by the DATE, DATETIME, and DATEPARSE functions in versions 10.5 and later. This has an impact on how dates are parsed, as well as the date formats and patterns that these functions are allowed to use. The following are more specific rules:
Dates are parsed by column rather than by row.
Dates are evaluated and then parsed using the locale of the computer where the workbook was created, not the locale of the computer where it is opened.
These new rules make extracts faster and more consistent with commercial databases.
However, because of these rules, you may notice that 1.) date and datetime values change to different date and datetime values or 2.) date and datetime values change to Null in international scenarios where the workbook is created in a locale other than the locale in which the workbook is opened or the server in which the workbook is published. When your date and datetime values change to different date and datetime values or become Null, it’s usually a sign that the underlying data is corrupt.
Sort order and case sensitivity
Because extracts support collation, they can better sort string values that have accents or are cased differently.
Consider the following scenario: you have a table of string values. This means that a string value like Égypte will now appear after Estonie and before Fidji in the sort order.
Data from Excel:
This means that between version 10.4 (and earlier) and version 10.5 of Tableau, the way values are stored has changed (and later). The rules for sorting and comparing values, however, have not changed. String values like “House,” “HOUSE,” and “houSe” are treated the same in version 10.4 (and earlier) and are stored with one representative value. The same string values are considered unique and stored as individual values in version 10.5 (and later).
Breaking ties in Top N queries
When using version 10.5 or later, the position that breaks the tie when a Top N query in your extract returns duplicate values for a specific position in a rank can be different. Consider making a top three filter. The values are the same in positions 3, 4, and 5. The top filter can return 1, 2, and 3 positions when using version 10.4 or earlier. The top filter, however, can return 1, 2, and 5 positions when using version 10.5 or later.
Precision of floating-point values
Extracts are better at utilising a computer’s available hardware resources and can thus perform mathematical operations in a highly parallel manner. As a result,.hyper extracts can aggregate real numbers in different orders. When numbers are aggregated in different orders, the values after the decimal point in your view may change each time the aggregation is computed.
This is due to the fact that floating-point addition and multiplication are not always associative. That is, (a + b) + c does not always equal a + (b + c). Furthermore, because floating-point multiplication is not always distributive, real numbers can be aggregated in any order. That is, (a x b) x c does not always equal an x b x c. This type of floating-point rounding behaviour in.hyper extracts is similar to that of commercial databases.
Accuracy of aggregations
Extracts optimise for large data sets by taking better advantage of a computer’s available hardware resources, allowing for highly parallel computation of aggregations. As a result, the results of aggregations performed by.hyper extracts may resemble those of commercial databases more than those of statistical computation software. Consider performing aggregations using reference lines, summary card statistics, or table calculation functions like variance, standard deviation, correlation, or covariance if you’re working with a small data set or need a higher level of accuracy.
- About the Compute Calculations Now option for extracts: Certain calculated fields were materialised and therefore computed in advance and stored in the extract if the Compute Calculations Now option was used in a.tde extract using an earlier version of Tableau Desktop. The previously materialised calculations in your extract are not included if you upgrade it from a.tde extract to a.hyper extract. To ensure that materialised calculations are included in the extract after the upgrade, you must use the Compute Calculations Now option once more.
- New Extract API: To make.hyper extracts, use the Extract API 2.0. You can use the Tableau Server REST API or the Tableau Server Client (Python) library to perform tasks that you previously performed using the Tableau SDK, such as publishing extracts. The Tableau Server REST API can also be used for refresh tasks.
This article elaborates on how you can set up Tableau Extracts with ease. This is useful if you only want to work with Tableau. But if you want to extract your Tableau data to any other destination of your choice, Hevo comes to your rescue by providing pre-built automated pipelines that are secure and hassle-free. Even someone with less technical experience can set up a pipeline using Hevo. Also, it not only extracts and loads data, but it also transforms the data to make it analysis-ready.
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Hevo Data will automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ multiple sources to Cloud-based Data Warehouses like Snowflake, Google BigQuery, Amazon Redshift, etc. It will provide you a hassle-free experience and make your work life much easier.
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