Meritshot Tutorials

  1. Home
  2. »
  3. Introduction to Data Extracts
SQL
R-Overview
Python
Cyber Security
Tableau

Tableau Tutorial

Introduction to Data Extracts

Understanding Data Extracts

What are Data Extracts?

Data extracts in Tableau are local copies or subsets of your original data that are stored in a highly optimized format for faster querying and analysis. These extracts are created by taking a snapshot of the original data source, allowing you to work with it independently of the live connection. This feature is particularly useful for enhancing performance, enabling offline access, and managing large datasets more efficiently.

Why Use Data Extracts?

  • Improved Performance:
    Working with large datasets or complex visualizations can be slow when using live connections. Data extracts, being locally stored and optimized, significantly improve the speed of your Tableau workbooks by reducing query times and enhancing dashboard responsiveness.
  • Offline Access:
    Data extracts enable you to work with your data without needing a live connection to the original data source. This is especially beneficial for users who need to analyze data while traveling, working remotely, or in environments with limited internet access.
  • Data Subsetting and Filtering:
    During the extraction process, you can apply filters to include only the data that is relevant to your analysis. This not only reduces the size of the extract but also makes your analysis more focused and manageable.
  • Historical Snapshots:
    Data extracts can serve as historical snapshots of your data. By periodically refreshing your extracts, you can maintain a record of how your data has changed over time, which is invaluable for trend analysis and comparison.
  • Aggregation:
    You can aggregate data at a higher level of granularity during extraction, which simplifies your dataset and further improves performance. For example, you might aggregate daily sales data into monthly totals before creating the extract.

Key Features of Data Extracts:

  • Incremental Refresh:
    Instead of refreshing the entire extract, which can be time-consuming, you can perform an incremental refresh that only updates the extract with new or changed records. This is particularly useful when dealing with large datasets that receive frequent updates.
  • Data Compression:
    Tableau compresses data extracts using lossless compression techniques. This reduces the storage space required for the extract without sacrificing data accuracy, making it easier to manage large datasets.
  • Multi-Table Extracts:
    Tableau supports the creation of multi-table extracts, allowing you to maintain relationships between tables within the extract. This means you can work with related tables without needing to flatten your data, preserving its relational integrity.

How to Create Data Extracts in Tableau

Creating data extracts in Tableau is a straightforward process that can be customized based on your specific needs. Here’s how you can do it:

  1. Connect to Your Data Source:
  • Open Tableau and connect to the data source you want to extract data from. This could be a database, spreadsheet, or any other supported data source.
  1. Choose the Extract Option:
  • After connecting to your data source, go to the “Data” menu and select “Extract Data…” from the dropdown menu. This opens the Extract Data dialog box.
  1. Set Filters:
  • In the Extract Data dialog box, you can apply filters to include only the data you need. For example, you might filter data by date range, region, or product category to focus your analysis on specific subsets of the data.
  1. Define Aggregations (Optional):
  • If you want to aggregate your data during extraction, select the “Aggregate data for visible dimensions” checkbox. You can specify the level of aggregation, such as summing sales by month or calculating average order value by product category.
  1. Set Up Incremental Refresh (Optional):
  • To set up an incremental refresh, select the “Incremental refresh” option and specify the field that Tableau should use to determine new or updated records. This is typically a timestamp or an ID field that increments over time.
  1. Extract the Data:
  • Once you’ve configured the filters and aggregation options, click the “Extract” button. Tableau will create the extract and store it locally on your machine. The extract will be in a .hyper file format, optimized for performance.
  1. Use the Extract in Your Workbook:
  • After the extract is created, Tableau automatically switches to using the extract instead of the live connection. You can now build your visualizations and dashboards using this optimized, local data.
  1. Refreshing Extracts:
  • To keep your data up-to-date, you can refresh the extract. This can be done manually by selecting “Refresh” from the “Data” menu, or you can schedule automatic refreshes if you are using Tableau Server or Tableau Online.

Best Practices for Data Extracts:

  • Filter Data Early:
    Apply filters during the extraction process to keep your extracts small and relevant. This not only saves storage space but also improves performance.
  • Use Aggregation Wisely:
    Aggregate data during extraction only when necessary. Over-aggregation can lead to loss of detail that might be important for your analysis.
  • Monitor Extract Size:
    Keep an eye on the size of your extracts, especially if you are working with very large datasets. Large extracts can still impact performance, even though they are optimized.
  • Schedule Regular Refreshes:
    If your data changes frequently, set up regular refresh schedules to ensure that your extracts are always up-to-date.
  • Leverage Incremental Refresh:
    Use incremental refresh to reduce the load on your system and speed up the refresh process, particularly with large datasets that receive frequent updates.

Frequently Asked Questions

Q1: What is the difference between a live connection and a data extract in Tableau?

A1: A live connection queries the data source in real-time, which can be slow with large or complex data. A data extract is a snapshot stored locally, optimized for faster performance and offline access.

Q2: Can I update a data extract with new data?

A2: Yes, you can refresh a data extract to include new data. This can be done through a full refresh, which reloads all data, or an incremental refresh, which only adds new or updated records.

Q3: How do I decide between using a live connection or a data extract?

A3: Use a live connection if you need real-time data and your data source can handle the load. Opt for a data extract if you need better performance, offline access, or to work with a specific subset of your data.

Q4: Can I filter data during the extraction process?

A4: Yes, you can apply filters when creating the extract to include only the relevant data. This makes the extract smaller and more focused on the analysis at hand.

Q5: How does Tableau compress data extracts, and does it affect data accuracy?

A5: Tableau compresses extracts using lossless compression, meaning no data is lost, and the accuracy of your data is maintained while saving storage space.

Q6: Can I create multi-table extracts in Tableau?

A6: Yes, Tableau allows you to create multi-table extracts, which maintain the relationships between tables, making it easier to work with complex, relational data.

Q7: What are the limitations of data extracts in Tableau?

A7: While extracts offer many advantages, they can become outdated if not regularly refreshed, and very large extracts may still impact performance. Additionally, certain data source features may not be fully supported in extracts.

❮ Previous
Next ❯
ads website