RLS Best Practices for Data Sources and Workbooks (2023)

Row-level security (RLS) in Tableau restricts the rows of data a certain user can see in a workbook. This differs from Tableau permissions, which control access to content and feature functionality. For example, permissions control whether a user can comment on or edit a workbook, while row-level security enables two users viewing the same dashboard to see only the data each user is allowed to see.

There are several ways to implement RLS in Tableau. For example, you can set RLS at the data source or workbook level, or you can set RLS at the connection level using a virtual connection with a data policy (requires Data Management). See the Overview of Row-Level Security Options in Tableau for details about alternatives.

Note: This topic focuses on RLSbest practices for data sources and workbooks. For more in-depth examples of the concepts outlined in this topic, refer to the whitepaper Best Practices for Row Level Security with Entitlement Tables(Link opens in a new window) or How to Set Up Your Database for Row Level Security in Tableau(Link opens in a new window) on the blog Tableau and Behold.


For live connections and multi-table extracts, the basic RLS workflow is:

  1. The user is identified by logging into Tableau Server or Tableau Cloud
    • This requires a distinct username per user and secure single sign-on (SSO)
    • Active Directory, LDAP, or the Tableau REST API can be used to synchronize user names and establish permissions
  2. The set of data entitlements for the user is retrieved from all possible data entitlements
    • This requires a data structure that can link entitlements to the Tableau username
  3. The data is filtered by the entitlements for that user
    • This often requires using user functions in a calculated field
  4. The published, filtered data is used to build content
    • Using a published (rather than embedded)data source with a data source filter ensures the RLS cannot be modified by downloading or web editing the workbook

How the joins, calculated fields, and filters are set up depends on the structure of the data and how users are managed.

Entitlement tables

Any unique combination of attributes that the data can be filtered on is an entitlement. Most commonly, there are separate tables for specifying the entitlements themselves and mapping those entitlements to users or user roles. Denormalizing is recommended from a performance standpoint because joins are expensive operations.

The entitlements view, consisting of the entitlements mapped to users or roles, is joined with the data. A user-based data source filter is then applied, acting as a WHERE clause that brings in only the entitlements—and therefore the appropriate data rows—for the relevant user. (Query optimization should ensure the filtering occurs before joining when the query is processed to minimize data duplication. For more information, see Performance and processing order of operations.)

Entitlement table models

Generally, there are two models for representing entitlements:

Full mapping to the deepest level of granularity

(Video) How to setup RLS with organizational hierarchy and multiple positions in Power BI?|BI Consulting Pro

  • Entitlements are defined fully for every column.
  • There is one row in the mapping table for every possible entitlement the user has.
  • This model requires fewer join clauses.

Sparse entitlements

  • Entitlements are defined for every level of hierarchy, with NULL used to represent an “all” state.
  • There is a single row in the mapping table for a particular level in the entitlement hierarchy, which vastly reduces the number of entitlement rows for users at high levels in a hierarchy.
  • This model requires more complex joins and filters.

Users and roles

Combinations of entitlements are commonly represented as roles, which are then linked to users in a many-to-many mapping table. This allows for easily changing or removing a user from the role, while still maintaining a record of the role and its entitlements.

Alternatively, a many-to-many mapping table can be created that instead assigns users directly to entitlements as opposed to going through joining a role table. It will require managing the values more directly in the table but does eliminate a join.

Note: The user values associated with a role or entitlement need to match the username or full name on the Tableau site in order to take advantage of the user functions in Tableau Desktop.


Regardless of the model used to represent the entitlements, it is advisable to join all entitlements and mapping tables together into a single denormalized entitlements view. While at first this will cause a “blowup” (highly duplicative) version of the entitlements, the data source filter on the user will reduce it back down. You will also want this view if you plan on using an extract.

The deepest granularity method can have a performance benefit when everything is hierarchical—you only need to do a single join on the deepest level of the hierarchy. This only works if all of the attributes at the lowest level are distinct. If there is a chance for duplication (for example, a Central sub-region in more than one region), then you’ll need to join on all the columns to achieve the effect of a distinct key value.

The actual details and their performance characteristics depend on the data system and require testing. For example, using a single key could potentially improve the performance because the join is then only executing on one column, but correctly indexing all of the columns may give equal performance when other factors are taken into consideration.

Implement row-level security

Deepest granularity

After the denormalized view of mapped entitlements is created, an inner join is set up between the view and the data in the Tableau data connection dialog. The data can remain in a traditional star schema. Alternatively, the dimension and fact tables can be materialized together into two views. Multi-table extracts will build extract tables to match the joins, so creating the two views will simplify the resulting extract. The SQL will follow this basic pattern:

SELECT * FROM data d INNER JOIN entitlements e ONd.attribute_a = e.attribute_a AND d.attribute_b = e.attribute_b AND ... WHERE e.username = USERNAME()

Sparse entitlements

If your entitlements more closely resemble the sparse entitlements model, then the custom SQL to join the data to the entitlements would be a little more complex because of the NULL values. Conceptually, it would look like the following:

(Video) What is Row-Level Security (RLS) in Power BI???

SELECT *FROM data d INNER JOIN entitlements e ON(e.region_id = d.region_id OR ISNULL(e.region_id) AND(e.sub_region_id = d.sub_region_id OR ISNULL(e.sub_region_id) AND(e.country_id = d.country_id OR ISNULL(e.country_id)

Without using custom SQL, this can be done with a cross join and additional filters in Tableau Desktop. Create a join calculation on both sides of the join dialog that simply consists of the integer 1 and set them equal. This joins every row from the data table with every row in the entitlements table.

Then you need a calculation (or individual calculations) to account for the levels in the hierarchy. For example, you could have several calculations that follow this format: [region_id] = [region_id (Entitlements View)] OR ISNULL([region_id (Entitlements View)]

Or you could have a combined calculation for all levels in one:

([region_id] = [region_id (Entitlements View)] OR ISNULL([region_id (Entitlements View)])AND([sub_region_id] = [sub_region_id (Entitlements View)] OR ISNULL([sub_region_id (Entitlements View)])AND([country_id] = [country_id (Entitlements View)] OR ISNULL([country_id (Entitlements View)])

The ISNULL function matches any entitlement column to all items in the other column. As always with RLS, these calculations should be added as data source filters.

Data source filter

For both approaches, once the entitlements are correctly joined with the data, a filter needs to be set up to limit the data for a specific user. A calculated field should be created with a user function. For example, a simple Boolean comparison of whether the user listed in the Username field is the same as the username of the person logged into the Tableau site:[Username] = USERNAME()

This calculation should be used as a data source filter (with TRUE selected).

If the data source is embedded and a user has permissions to web edit or download the workbook, then the RLS is nonexistent since the filters enforcing it can be easily removed. The Tableau data source should be published separately as opposed to being left embedded in the workbook.

All access with deepest granularity

There is also a common scenario in which there are two access levels within the organization: people who can see everything (“all access”) or people with some reasonably definable subset of entitlements. This is most commonly seen for embedded applications—the organization hosting the data can see everything, but each client can only see their own data. In this case, you need a way to return the full data for the “all access” users, while maintaining the deepest granularity joins for all other users.

(Video) What is Row Level Security (RLS) & How to setup RLS in Power BI ?

For this technique, you will use Tableau groups to create an override using a calculation in the join condition.

  1. Create a group for users who should see all the data (here called All Access)
  2. From the fact view, create a left join with two join conditions
    • The first join condition should be on the column that represents the deepest level of granularity
    • The second join condition should be two calculations:
      • On the left side (the fact view), for the calculation, enter True
      • On the right side (the entitlements view), the calculation should be: IF ISMEMBEROF('All Access') THEN False ELSE True END
  3. On a sheet, create a calculation structured as:[Username] = USERNAME() OR ISMEMBEROF(['All Access'] ([Entitlements View)])
  4. Create a data source filter on the username calculation

If a user is a member of the All Access group, then the join becomes a left join on True = False. This means there are no matches at all in the entitlements view, so the entire fact view is returned with NULLs for the columns from the entitlements view (zero duplication). In the case where the user is not part of the All Access group, the True = True join condition doesn’t change anything and the join will function as expected.

The user calculation used as a data source filter is true for all rows when the group override is working, or it will filter down to only the user’s deepest granularity in the hierarchy.

Performance and processing order of operations

When a visualization is viewed in Tableau Desktop, Tableau Server, or Tableau Cloud, Tableau sends an optimized query to the RDBMS which then processes the query and sends results back to Tableau to render the visualization with the resulting data. The order of operations for when joins, calculations, and filters are carried out depends on the query optimizer and how the query is executed.

Live connections

When using a live connection to a data source in Tableau, the performance of the query execution is dependent on the query optimizer which translates the incoming SQL into an efficient plan for retrieving the data.

There are two ways the query can be processed:

  1. Filter the entitlement rows to the user then join to the fact table
  2. Join the entitlements to the fact table then filter to the user’s rows

In an ideal situation, the query optimizer will ensure the database processes the query by filtering then joining. If a user is entitled to everything, this means the maximum number of rows processed will be the number of rows in the data table.

If the database processes the query by joining then filtering, there may be duplication of data. The maximum number of rows processed will be the number of users entitled to see that particular row times each row in the data table.

It will be clear if this second scenario happens: your queries take a long time to finish, you get errors, or there is an indication of performance issues in the database. Your total data volume will expand exponentially, which could cause inordinate system strain on the backend.


When the data source in Tableau is a live connection, Tableau sends every query that is necessary to render a particular viz or dashboard to the RDBMS. When the data source is an extract, the process of querying data from the underlying data source only happens at extract creation and refresh. All of the individual queries for visualizations are answered by the extract engine from the extract file.

(Video) Tableau Tutorial : ROW LEVEL SECURITY ( PART -1 )

The same order of operations issue is present when building single table extracts. However, the “blowup” will happen both on the underlying data source and within the resulting extract itself.

Considerations with extracts

Starting in Tableau 2018.3, the data engine can create a multi-table extract and RLS can be implemented as described above. Using multiple table extracts reduces the time it takes to generate an extract with many-to-many relationships by not materializing the join.

The extract should be built with a data object and an entitlements object. This is the simplest storage in the extract and results in the best performance.

  • The data object is the table, view or custom SQL query that represents the denormalized combination of the fact and necessary dimension tables
  • The entitlements object is a denormalized table, view or custom SQL query of whatever entitlements are necessary to filter the data at the most granular level, which requires:
    • A column for username matching the exact usernames in Tableau Server or Tableau Cloud
    • A row for each of the most granular entitlements to the data object

This format is laid out in the deepest granularity method above. Multi-table extracts use the same method, with the caveat that only two data objects are being joined and any field-specific filtering is already applied within the object.

Because multiple table extracts have extract filters disabled, you can filter either in the views or tables you connect to in the data source, or define the filters in custom SQL objects in the Tableau data connection dialog.

Note:As with live connections, if the data source is embedded and a user has permissions to web edit or download the workbook, then the RLS is nonexistent since the filters enforcing it can be easily removed. The extract should be published separately as opposed to being left embedded in the workbook.

Single table extracts

The following method is only recommended when using a version of Tableau prior to 2018.3—multiple table extracts are preferable if available.

Single table extracts materialize any joins you build when constructing the Tableau data source and stores everything as a single table through one query, the results of which are transformed in a single table in the extract file. This denormalization carries the risk of causing massive data duplication, as every row that was allocated to more than one entitlement or user would be duplicated as a result of the many-to-many relationship.

To prevent this duplication:

  1. Create a Security Users Field that contains the usernames for that entitlement
    • for example, a value may be “bhowell|mosterheld|rdugger”
  2. Use the CONTAINS() function within Tableau to correctly identify individual users
    • For example, CONTAINS([Security Users Field], USERNAME())

This method obviously has some caveats. It requires that you go from your entitlements in rows to a single column separated correctly using SQL, and that column can only contain so many characters. Partial matches can be trouble, and you need to use separators that will never be valid in the IDs themselves. Although it is performant within the Tableau Data Engine, as a string calculation it will be very slow for most databases. This limits your ability to switch back to a live connection.

(Video) Dynamic Row-Level Security for Manager Level in Power BI? | RLS In Power BI Ep4 | BI Consulting Pro

Alternatively, you can take different extracts per “role” or entitlement level, so that only the data appropriate to that person or level is contained within the extract, but this will require processes to appropriately permission and leverage template publication within Tableau Server, generally via the APIs.

Use built-in row-level security in a database

Many databases have mechanisms for RLS built in. If your organization has already put effort into building row-level security in a database, you might be able to take advantage of your existing RLS. It's not necessarily easier or better to implement a built-in RLS model vs. building it with Tableau in mind; these techniques are generally leveraged when an organization has already invested in these technologies and they want to take advantage of the investment. The main benefit of using built-in RLS is that administrators can implement and control their data security policy in one place: their databases. For more information, see Row-Level Security in the Database.


What are the best practices to use RLS row level security objects? ›

Best Practices

It's highly recommended to create a separate schema for the RLS objects: predicate functions, and security policies. This helps to separate the permissions that are required on these special objects from the target tables.

What is the RLS method in Tableau? ›

Row-level security (RLS) in Tableau restricts the rows of data a certain user can see in a workbook. This differs from Tableau permissions, which control access to content and feature functionality.

What is the native Tableau suggested approach for row level security? ›

Tableau offers the following approaches to row-level security: Create a user filter and map users to values manually. This method is convenient but high maintenance, and security can be tentative. It must be done per-workbook, and you must update the filter and republish the data source as your user base changes.

When a workbook or data source is published with embedded database credentials How can you restrict which data each user sees? ›

If you select to prompt users, a user who opens the workbook must have View and Connect permissions on the data source to see the data. If you select embed password, users can see the information in the workbook even if they don't have View or Connect permissions.

What are five key steps that help to ensure database security? ›

Let's look at 10 database security best practices that can help you to bolster your sensitive data's safety.
  • Deploy physical database security. ...
  • Separate database servers. ...
  • Set up an HTTPS proxy server. ...
  • Avoid using default network ports. ...
  • Use real-time database monitoring. ...
  • Use database and web application firewalls.
Mar 2, 2023

How to implement row level security in database? ›

SQL Security Tips - Implement Row Level Security Quickly
  1. Fine-grained access role ( control both read & write access to specific rows)
  2. Application transparency ( No application changes required)
  3. Centralized access within the database.
  4. Easy to implement & maintain.
Mar 6, 2023

What is the difference between LMS and RLS algorithm? ›

At each step, the filter weights are updated based on the gradient of the mean square error.
Compare RLS and LMS Adaptive Filter Algorithms.
LMS AlgorithmRLS Algorithm
Objective is to minimize the current mean square error between the desired signal and the output.Objective is to minimize the total weighted squared error between the desired signal and the output.
7 more rows

How does RLS algorithm work? ›

Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This approach is in contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error.

How to implement dynamic RLS? ›

Create a table with three columns with data in them, name it Sales Rep, and make sure the data in the username column is from real Power BI accounts that you want to secure. Load the table. Create a second table with data in it and name it: Transactions.

What is the best way to organize data for Tableau? ›

Tableau Desktop works best with data that is in tables formatted like a spreadsheet. That is, data stored in rows and columns, with column headers in the first row.

Which of these are best practices when working with data in Tableau? ›

Enable highlighting
  • What's your goal?
  • Know your purpose and audience.
  • Leverage the most-viewed spot.
  • Design for the real world.
  • Author at your final display size.
  • Limit the number of views.
  • Add interactivity to encourage exploration.
  • Show filters.

How many types of row level security are there in Tableau? ›

The popular approaches to implementing row-level security in Tableau include using a User Filter, Dynamic Filter, and a Security Group. The User Filter approach involves mapping each user to the row they should access, for example, a particular country.

What is the difference between embedded data source and published data source? ›

The difference between published data source and embedded data source is that, Published data source: It contains connection information that is independent of any workbook and can be used by multiple workbooks. Embedded data source: It contains connection information and is associated with a workbook.

What else would you do to protect data in a workbook? ›

You have two choices here:
  1. File encryption: When you choose this option, you specify a password and lock the Excel file. ...
  2. Setting a password to open or modify a file: You specify a password to open or modify a file.

What are the 4 pillars of database security? ›

Protecting the Four Pillars: Physical, Data, Process, and Architecture.

What are the four 4 elements of data security? ›

  • Confidentiality.
  • Integrity.
  • Availability.
  • Non-repudiation.
  • Authorization.
  • Trust.
  • Accounting.

How do you implement row level security in Excel? ›

  1. Add Excel data to the analysis file in DataTable1.
  2. Insert one Calculated Column with expression as "1", called for example "Val" (this will give all rows the value 1)
  3. Create a personalized Information Link with only %CURRENT_USER% and 1 as the two columns, with users of Group A in the WHERE clause.
Oct 8, 2021

How do you manage row level security? ›

To define security roles, follow these steps.
  1. Import data into your Power BI Desktop report, or configure a DirectQuery connection. ...
  2. Select the Modeling tab.
  3. Select Manage Roles.
  4. Select Create.
  5. Provide a name for the role.
  6. Select the table that you want to apply a DAX rule.
  7. Enter the DAX expressions.
Mar 14, 2023

What is the difference between RBAC and row level security? ›

While RBAC secures access to Tables, row level access is used to control access to sub-sets of the data. In the above diagram users are authorised to view data for one or more REGIONS, and the System Administrator has no access to the data at all.

What is LMS vs NLMS vs RLS? ›

Three types of adaptive filters are used to identify the unknown system Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Square (RLS) algorithms. LMS has less computational complexity than NLMS and RLS while NLMS is the normalized form of LMS adaptive filter.

What is the code for RLS algorithm in Matlab? ›

The code to run this adaptive filter is: [y,e] = rls(x,d); where y returns the filtered output and e contains the error signal as the filter adapts to find the inverse of the unknown system. Obtain the estimated coefficients of the RLS filter.

What are the limitations of LMS algorithm? ›

The Least Mean Square (LMS) algorithm is familiar and simple to use for cancellation of noises. However, the low convergence rate and low signal to noise ratio are the limitations for this LMS algorithm.

What is the rating scale score for RLS? ›

How often do you get RLS symptoms? Very severe: 6 to 7 days a week (4 points), severe: 4 to 5 days a week (3 points), moderate 2 to 3 days a week (2 points), mild (1 day a week or less), none (0 points).

What is the difference between Kalman filter and recursive least squares? ›

While recursive least squares update the estimate of a static parameter, Kalman filter is able to update and estimate of an evolving state[2]. It has two models or stages. One is the motion model which is corresponding to prediction. Another is the measurement model which is used to do the correction.

What is LMS algorithm in signal processing? ›

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal (difference between the desired and the actual signal).

Can we apply RLS in paginated reports? ›

If your paginated report is based on a Power BI dataset, the article Row-level security (RLS) with Power BI provides a solid background. To use RLS in a paginated report, you first create parameters in that report. Then you take advantage of the built-in field UserID: Use UserID in a filter.

What is the difference between RLS and dynamic RLS? ›

RLS enables the grouping of users by roles. The developer can configure specific data access for each role. Dynamic RLS identifies the actual user, and applies data access according to pre-determined rules (e.g. team / division / seniority).

How do you apply RLS in paginated report? ›

To apply row-level security to a Power BI paginated report, use the built-in field UserID to assign a parameter. This parameter will be used to filter or query your data. Then, pass the UserID to the Embed Token - Generate Token API to get the embed token.

What are 3 ways to organize data? ›

Overview of organising your data
  • use folders to sort out your files into a series of meaningful and useful groups.
  • use naming conventions to give your files and folders meaningful names according to a consistent pattern.
Mar 6, 2023

What is the difference between data source and workbook in Tableau? ›

A data source can contain a direct (or live) connection to your database or an extract you can refresh on a schedule. For information, see Best Practices for Published Data Sources. Workbooks: Workbooks contain your views, dashboards, and stories, and data connection.

What are best practices for data management? ›

7 Best Practices for Successful Data Management
  • Build strong file naming and cataloging conventions. ...
  • Carefully consider metadata for data sets. ...
  • Data Storage. ...
  • Documentation. ...
  • Commitment to data culture. ...
  • Data quality trust in security and privacy. ...
  • Invest in quality data-management software.

What are the 3 data source types used in Tableau? ›

Because there are so many ways to bring data into a workbook, Tableau groups the types of data connections into three main categories: Tableau Server, File, and Server. The File category encompasses the gamut of different files you can reference. These include text, CSV, JSON, Excel, etc.

How many rows can Tableau handle efficiently? ›

The short answer is, Tableau can handle 50 million rows. Here's a few things to watch for. Tableau normally performs better with long data as opposed to wide data. Try to limit the number of columns when connecting to data with millions of rows.

What is the difference between row-level and aggregate in Tableau? ›

There are two general types of calculated fields: Row-Level – Calculation is calculated for each row in the data. Row-level results are then aggregated just like normal fields. Aggregate – Measures are first aggregated, and the calculation is then run on the aggregated result.

What is the maximum number of rows in data sources in Tableau? ›

Tableau Desktop and Tableau Server do not have any enforced row or column limits for the amount of data that can be imported.

What is the difference between protect sheet and protect workbook? ›

Amongst other things, Workbook protection allows you to prevent deletion, moving and hiding/ unhiding of worksheets. Worksheet protection allows you to lock cells, prevents modification of data validation and formats etc etc.

How do I protect my workbook but allow read only? ›

Restrict Editing
  1. Click Review > Protect > Restrict Editing.
  2. Under Editing restrictions, select Allow only this type of editing in the document, and make sure the list says No changes (Read only).
  3. Select Yes, Start Enforcing Protection.

When would you use source and destination workbooks? ›

The source worksheet is the worksheet with the data. The destination worksheet has the link formula or external cell reference. If a referenced cell value changes, the destination cell updates when activated. How Can You Create the Worksheet Link?

What are the 2 types of source data? ›

Data can be gathered from two places: internal and external sources. The information collected from internal sources is called “primary data,” while the information gathered from outside references is called “secondary data.”

What are two types of data sources? ›

There are two types of data sources: machine data sources and file data sources. Although both contain similar information about the source of the data, they differ in the way this information is stored. Because of these differences, they are used in somewhat different manners.

How do you connect a workbook to a published data source? ›

Connect to a published data source in the web editing environment
  1. While you're signed in to your Tableau Server or Tableau Cloud site, select a view to edit.
  2. In editing mode, click the New Data Source icon .
  3. In the Connect to Data Source dialog box, select a published data source from the list, and then click Add.

What are at least 4 actions that protect workbook prevents other users from doing? ›

To prevent other users from viewing hidden worksheets, adding, moving, deleting, or hiding worksheets, and renaming worksheets, you can protect the structure of your Excel workbook with a password.

What is the better way to organize your data in a workbook? ›

Data organization guidelines
  1. Put similar items in the same column Design the data so that all rows have similar items in the same column.
  2. Keep a range of data separate Leave at least one blank column and one blank row between a related data range and other data on the worksheet.

Which of the following are security best practices for Snowflake? ›

The network security best practices are as follows:
  • Use network policies.
  • Use private connectivity with Snowflake.
  • Allow firewall to connect client applications to Snowflake.
  • Allow Snowflake to access your cloud storage location for loading/unloading data.
Mar 29, 2023

What is RLS vs object level security? ›

In simple terms, Row Level Security prevents user access to specified rows. Meanwhile, Object Level Security prevents user access to specified columns and tables.

What does row level security allow you to enable this at? ›

Row Level Security enables you to apply security to roles and adds users to each role. An example is helpful when you want people from one branch, city, department, or store to be able to only see their part of the data and not the whole data set.

How do you implement row-level security in Excel? ›

  1. Add Excel data to the analysis file in DataTable1.
  2. Insert one Calculated Column with expression as "1", called for example "Val" (this will give all rows the value 1)
  3. Create a personalized Information Link with only %CURRENT_USER% and 1 as the two columns, with users of Group A in the WHERE clause.
Oct 8, 2021

What are the different types of row-level security? ›

What are the Different Types of Row-Level Security? Two main types of RLS can be implemented into your Power BI report: Status RLS and Dynamic RLS. Status is the simpler of the two and requires a Power BI developer to define security-based logic manually within the PIBX file (explained later).

How do you implement row-level security for paginated reports? ›

To apply row-level security to a Power BI paginated report, use the built-in field UserID to assign a parameter. This parameter will be used to filter or query your data. Then, pass the UserID to the Embed Token - Generate Token API to get the embed token.

What are some best practices for data loading in Snowflake? ›

To optimize the number of parallel operations for a load, we recommend aiming to produce data files roughly 100-250 MB (or larger) in size compressed. Loading very large files (e.g. 100 GB or larger) is not recommended. If you must load a large file, carefully consider the ON_ERROR copy option value.

Which ETL tool is good for Snowflake? ›

Snowflake and ETL Tools

Snowflake supports both transformation during (ETL) or after loading (ELT). Snowflake works with a wide range of data integration tools, including Informatica, Talend, Fivetran, Matillion and others.

What are the four types of Snowflake tables and which ones have fail safe storage? ›

Comparison of Table Types
TypePersistenceFail-safe Period (Days)
TemporaryRemainder of session0
TransientUntil explicitly dropped0
Permanent (Standard Edition)Until explicitly dropped7
Permanent (Enterprise Edition and higher)Until explicitly dropped7

What are the advantages of RLS? ›

The benefit of the RLS algorithm is that there is no need to invert matrices, thereby saving computational cost. Another advantage is that it provides intuition behind such results as the Kalman filter.

What is row level security RLS and column level security CLS? ›

A record-level security (RLS) and column-level security (CLS) policy at data source scope refers to a security entry in the data source of a catalog. It allows you to control user access to different subsets of data, and ensures that people only see what they are supposed to see: certain records and/or columns.

What is the difference between field level security and object level security? ›

Field level security is applied to the set of objects that the user is entitled to by either role-based security or record level security rules. If no field level security is defined for an object, security is applied at the object level (if security rules are defined) or at the folder level.


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