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<h2>How Row Access Policies Work in Snowflake (And Why We Use Them)</h2><p>When we need to control who sees what rows in a Snowflake table, we use Row Access Policies.</p><p>Unlike column-level masking, which hides sensitive fields, row access policies filter entire rows based on user attributes like roles or session variables.</p><h3>Row access policy</h3><p>A row access policy is a Snowflake object that acts as a dynamic filter applied whenever someone queries a table or view.</p><ul><li>It behaves like an automatic WHERE clause</li><li>It filters rows based on the current user or session</li><li>It applies during query time (not at data load time)</li><li>It works with SELECT, COPY INTO, and other operations<br></li></ul><figure id="figure-qz3r7s4tghma1a8pk6"><img id="qz3r7s4tghma1a8pk6" src="https://cdn.durable.co/blocks/22ymOk5BmyNxUPH7mBv8MJGpHTpfCaBBC0kI33sj6ps8RLERkp8acC9Lyq265C4I.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/22ymOk5BmyNxUPH7mBv8MJGpHTpfCaBBC0kI33sj6ps8RLERkp8acC9Lyq265C4I.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Creating a Row Access Policy</h3><p>We typically follow three steps:</p><ul><li>Create the policy using CREATE ROW ACCESS POLICY</li><li>Attach it to a table or view</li><li>Let Snowflake evaluate it every time the object is queried</li></ul><p><strong>Example: Show Region-Specific Rows Only</strong></p><p>Let’s say we have a sales table with a region column. We want users to only see data for their assigned region.</p><p><strong>Step 1: Define the policy</strong></p><figure id="figure-26jdoux1iama1adx66"><img id="26jdoux1iama1adx66" src="https://cdn.durable.co/blocks/29hynIJm1PexI0vOpVlfI3LYIQR8ASX1zl4VWpGqSNEa362X4qX0MPRigybRTpoD.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/29hynIJm1PexI0vOpVlfI3LYIQR8ASX1zl4VWpGqSNEa362X4qX0MPRigybRTpoD.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p><strong>Step 2: Attach it to the table</strong></p><figure id="figure-v710ylcmorma1ahfx7"><img id="v710ylcmorma1ahfx7" src="https://cdn.durable.co/blocks/15ZbVJUapoGQP9ShRGqbvR4ADePo86U2MiBiFYeiyM8xixI4faYFVzB8CfuXRJgG.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/15ZbVJUapoGQP9ShRGqbvR4ADePo86U2MiBiFYeiyM8xixI4faYFVzB8CfuXRJgG.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p>Now, if someone in the US region runs a query, they’ll only see rows where region = 'US'.</p><h3>How this is handled behind the scenes</h3><p>Snowflake rewrites the query under the hood using the policy logic. It doesn’t alter the table data itself.</p><figure id="figure-4gs2w0e69hma1ania3"><img id="4gs2w0e69hma1ania3" src="https://cdn.durable.co/blocks/8lDULzac9VW91aiSuCDpBQTNdFnO8EBBCyGUKe7bIJozbmcBJwyKyU8SizFdlfX5.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/8lDULzac9VW91aiSuCDpBQTNdFnO8EBBCyGUKe7bIJozbmcBJwyKyU8SizFdlfX5.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Testing the Policy</h3><p>Before deploying a policy to production, test it like this:</p><ul><li>Set the session context (ALTER SESSION)</li><li>Run queries with different roles or users</li><li>Validate visibility of expected rows</li></ul><h3>Can You Apply Multiple Policies?</h3><p>Snowflake supports only <strong>one row access policy per table or view</strong>.</p><p>To implement more complex rules, you’ll need to:</p><ul><li>Use logical operators inside a single policy</li><li>Check for multiple roles using <strong>IS_ROLE_IN_SESSION()</strong></li><li>Apply CASE logic within the policy expression</li></ul><p><strong>Combining Row Access and Column Masking</strong></p><p>We can use both together for layered security:</p><figure id="figure-suuzevzbkorma1aveuj"><img id="suuzevzbkorma1aveuj" src="https://cdn.durable.co/blocks/bgIy7fY9H7V35gESQ411DoroU6Qt2kMQykkXAOgcMahGaAmzsKw7gM5IeVbawil6.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/bgIy7fY9H7V35gESQ411DoroU6Qt2kMQykkXAOgcMahGaAmzsKw7gM5IeVbawil6.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p>Row access controls which rows are shown</p><p>Column masking hides or transforms certain <em>fields</em> in those rows</p><h3>Managing and Auditing Policies</h3><p>Some helpful commands and tools:</p><ul><li>View all policies: <strong>SHOW ROW ACCESS POLICIES</strong></li><li>See policy definitions: Query <strong>INFORMATION_SCHEMA.ROW_ACCESS_POLICIES</strong></li><li>Remove a policy from a table: <strong>ALTER TABLE sales DROP ROW ACCESS POLICY;</strong></li><li>Name your policies clearly, especially if you plan to version them (e.g., policy_sales_v1)</li></ul><h3>Useful Snowflake Functions</h3><table style="border-collapse: collapse; width: 100%;" border="1" data-mce-style="border-collapse: collapse; width: 100%;"><tbody><tr><td style="width: 50%;" data-mce-style="width: 50%;"><strong>Function</strong></td><td style="width: 50%;" data-mce-style="width: 50%;"><strong>What it does</strong></td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">CURRENT_USER()</td><td style="width: 50%;" data-mce-style="width: 50%;">Returns the current user</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">CURRENT_ROLE()</td><td style="width: 50%;" data-mce-style="width: 50%;">Returns the current role</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">IS_ROLE_IN_SESSION()<br></td><td style="width: 50%;" data-mce-style="width: 50%;">Checks if a role is active</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">CURRENT_REGION()</td><td style="width: 50%;" data-mce-style="width: 50%;">Custom or session-based region</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">SESSION_USER()</td><td style="width: 50%;" data-mce-style="width: 50%;">Returns the session’s authenticated user</td></tr></tbody></table><p>These functions help make policies more flexible and context-aware.</p><h3>How Row Access Differs from Column Masking</h3><table style="border-collapse: collapse; width: 100%;" border="1"><tbody><tr><td style="width: 33.3325%;"><strong>Feature</strong></td><td style="width: 33.3325%;"><strong>Row Access Policy</strong></td><td style="width: 33.335%;"><strong>Column Masking</strong></td></tr><tr><td style="width: 33.3325%;">Filters</td><td style="width: 33.3325%;">Whole rows</td><td style="width: 33.335%;">Specific columns</td></tr><tr><td style="width: 33.3325%;">Use case</td><td style="width: 33.3325%;">Limit access by geography, department, etc.</td><td style="width: 33.335%;">Hide sensitive fields like SSNs</td></tr><tr><td style="width: 33.3325%;">Based on</td><td style="width: 33.3325%;">User, role, session context</td><td style="width: 33.335%;">User, role, session context</td></tr><tr><td style="width: 33.3325%;">Can combine</td><td style="width: 33.3325%;">Yes</td><td style="width: 33.335%;">Yes</td></tr></tbody></table><p>Use row access when what a user can see depends on where they belong (e.g., region or department).</p><p>Use column masking when what fields a user can see depends on their level of clearance.</p><p><strong>Row Access Policy vs Column masking</strong></p><ul><li>Use Row Access policy to limit /filter data based on certain condition. use column masking to hide sensitive fields.</li><li>For Example - use Row Access policy to limit access by geography, department, etc. meanwhile use column masking to hide sensitive fields like SSNs, IDs</li></ul><h3>Conclusion</h3><p>Row Access Policies help us control visibility at the row level. They’re easy to set up and flexible enough for complex scenarios.</p><p>We’ve used them to build region-specific dashboards, protect internal data by team, and support secure self-service reporting — all without duplicating data.</p><p>When used together with column masking and access control, they give us a clean way to manage security at scale.</p><p><br></p>

Row Access Policies in Snowflake

<h2>How Column-Level Masking in Snowflake Works&nbsp;</h2><h3><br>What It Is</h3><p>Column-level masking in Snowflake helps us hide sensitive data from users who don’t need to see it. Instead of blocking entire tables, we can apply masking policies to specific columns—like email addresses or salaries—based on roles.</p><h3>Why We Use It</h3><ul><li>Controls access to specific columns, not entire datasets.</li><li>Helps us meet privacy requirements without duplicating tables.</li><li>Keeps sensitive data protected across environments like dev, QA, and prod.</li></ul><h3>How It Works</h3><p>Here’s how Snowflake handles column masking:</p><ul><li>We define a masking policy using SQL.</li><li>This policy acts like a filter—checking the user’s role and deciding what they can see.</li><li>We attach this policy to one or more columns.</li><li>When someone queries the column, Snowflake shows either the real value or a masked version.<br></li></ul><figure id="figure-z0jm7yp6rjm9lq24m8"><img id="z0jm7yp6rjm9lq24m8" src="https://cdn.durable.co/blocks/14VKOA1ciohWGDLnTTLoWJEhNOr4puPDs2SdZUWXVOekUzt3hIeTUaVsIsAthLqF.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/14VKOA1ciohWGDLnTTLoWJEhNOr4puPDs2SdZUWXVOekUzt3hIeTUaVsIsAthLqF.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Step-by-Step: Creating a Masking Policy</h3><p>Let’s walk through a basic example where we want only HR users to see employee email addresses.</p><p><strong>1. Create the Masking Policy</strong></p><p>We define a policy using SQL. It checks the user’s role. If the role is HR_ROLE, show the real email. Otherwise, show a generic masked value.</p><figure id="figure-2lw2lwmdcvm9lq66j8"><img id="2lw2lwmdcvm9lq66j8" src="https://cdn.durable.co/blocks/7jEMpdYrflOdwvpIPB48ff6Oc5MUgT3LzSEzSGoY8u9cQpCVzWzCNIp96ZpRvwcT.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/7jEMpdYrflOdwvpIPB48ff6Oc5MUgT3LzSEzSGoY8u9cQpCVzWzCNIp96ZpRvwcT.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p><strong>2. Apply the Policy to a Column</strong></p><p>We attach this policy to the email column in the employees table.</p><figure id="figure-duyejk6kwm9lq727q"><img id="duyejk6kwm9lq727q" src="https://cdn.durable.co/blocks/34sTcgqXK9jupXQTBBgmliMr1T9SODf8SY9nzuokicT4AmMwyXRRNnAccw7Jhv1Z.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/34sTcgqXK9jupXQTBBgmliMr1T9SODf8SY9nzuokicT4AmMwyXRRNnAccw7Jhv1Z.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p>Now, any time someone queries that column, Snowflake checks their role and applies the policy.</p><figure id="figure-nnpqr7kwgm9lqcpqe"><img id="nnpqr7kwgm9lqcpqe" src="https://cdn.durable.co/blocks/10FYTKdykZT1EpiNmKFRQItxcxIbeI7affLSnaWCJMXSrMnR8HwMfE8E4kcLPlhz.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: 45% 45%;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="45%" data-positiony="45%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/10FYTKdykZT1EpiNmKFRQItxcxIbeI7affLSnaWCJMXSrMnR8HwMfE8E4kcLPlhz.png" data-mce-style="aspect-ratio: 16/9; object-position: 45% 45%;"></figure><h3>Reusing the Policy Across Tables</h3><p>Snowflake lets us reuse the same policy on similar columns in other tables. For example, we can apply the same email_mask_policy to an email column in a contractors table without writing new logic.</p><p>This helps us maintain consistency and reduce duplication.</p><h3>Masking Numeric Data</h3><p>We can also mask numeric values like salaries or bonuses.</p><p>For example:</p><ul><li>Finance roles see the actual number.</li><li>Other roles see NULL or 0.</li></ul><p>To do this, we follow the same steps:</p><ul><li>Create a policy that checks the user’s role.</li><li>If not authorized, return a placeholder (like NULL).</li><li>Apply the policy to the numeric column.</li></ul><p>This is useful for sharing performance data while keeping compensation private.</p><h3>Tracking Where Masking is Applied</h3><p>To keep track of which columns have masking policies, we can use Snowflake's information_schema.columns view.</p><p>This allows us to:</p><ul><li>Audit which columns are protected.</li><li>Check if policies are applied consistently.</li><li>Identify columns missing masking rules.</li></ul><h3>Best Practices for Column Masking</h3><ul><li><strong>Use clear naming</strong> for policies (<em>email_mask_policy, salary_mask_policy</em>) to make them easier to manage.</li><li><strong>Keep logic simple :</strong> avoid overcomplicating the policy with too many conditions.</li><li><strong>Document the purpose</strong> of each policy in a central table or within your team’s governance process.</li><li><strong>Test across environments </strong>especially in dev or staging—to make sure masking works consistently.</li></ul><h3>Real-Life Example: Customer Support Access</h3><p>Let’s say our support team needs to see basic customer info but not payment details or phone numbers.</p><ul><li>We attach a masking policy to <em>credit_card_number</em> and <em>phone_number</em>.</li><li>The policy allows access only for roles like <em>PAYMENTS_TEAM</em> or <em>SECURITY_TEAM.</em></li><li>All other users just see "<em>masked</em>" or a generic value.</li></ul><figure id="figure-g7cdwmaggm9lqkx9q"><img id="g7cdwmaggm9lqkx9q" src="https://cdn.durable.co/blocks/25UMiRIaNu17pkAyMRCC8pYdlu2aQfga0eZ9N2zHaRyvtofRTT0wneTme8UhO0mw.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: 50% 50%;" data-corners="default" data-aspect="16:9" data-caption="" data-positionX="50%" data-positionY="50%" data-fullWidth="false"/></figure><p>This setup protects sensitive data while still supporting team collaboration.</p><h3>Common Limitations</h3><p>While column masking is powerful, there are a few things to watch for:</p><ul><li><strong>One input column only:</strong> Policies can only use the value of the column they’re attached to.</li><li><strong>Only SELECT queries are affected:</strong> Other operations like INSERT or COPY aren’t masked.</li><li><strong>Not meant for full security:</strong> Masking helps with access control but doesn’t encrypt or fully hide data from backend processes.</li></ul><h3>Summary</h3><p>Column-level masking in Snowflake is a practical way to protect specific fields without blocking access to the entire dataset. It’s flexible, role-based, and easy to maintain at scale.</p><table border="1" data-mce-style="border-collapse: collapse; width: 100%; margin-left: auto; margin-right: auto;" style="border-collapse: collapse; width: 100%; margin-left: auto; margin-right: auto;" data-mce-selected="1"><tbody><tr><td style="width: 50%;" data-mce-style="width: 50%;"><strong>Feature</strong></td><td style="width: 50%;" data-mce-style="width: 50%;"><strong>Benefit&nbsp;</strong></td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">Column Level masking</td><td style="width: 50%;" data-mce-style="width: 50%;"><p>Controls access to specific data fields</p></td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">Role based policies&nbsp;</td><td style="width: 50%;" data-mce-style="width: 50%;">Show real or masked values depending on who's querying&nbsp;</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">Reusable Logic&nbsp;</td><td style="width: 50%;" data-mce-style="width: 50%;"><p>Apply the same masking rules across multiple tables&nbsp;</p></td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">No data duplication</td><td style="width: 50%;" data-mce-style="width: 50%;">Keep one source of truth while customising access&nbsp;</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">Works across environments&nbsp;</td><td style="width: 50%;" data-mce-style="width: 50%;">Consistent protection from development to production</td></tr></tbody></table><p>If we’re sharing data across teams or environments, column-level masking is a helpful tool for minimising risk. It keeps access focused, simple, and transparent—without slowing down collaboration.</p>

Column-level masking in Snowflake

<h2>Understanding How Sigma Uses Snowflake’s Compute Layer</h2><p>Sigma Computing connects directly to Snowflake, allowing teams to explore and analyze data at scale without moving it. But to really get value out of this integration, we need to understand how Sigma uses Snowflake’s compute layer.</p><p>This blog explains what’s happening under the hood, how compute usage is triggered, and how we can optimise our workflows for speed and cost.</p><h3>What is Snowflake’s Compute Layer?</h3><ul><li>Snowflake separates storage and compute.</li><li>Compute is handled by virtual warehouses, which run the SQL queries.</li><li>Each virtual warehouse consumes credits when running.</li></ul><p><strong>Example: </strong>When we run a SELECT query from Sigma, it triggers a virtual warehouse in Snowflake to perform the operation. We pay based on:</p><ul><li>Size of the warehouse (e.g., Small, Medium, Large)</li><li>Duration it runs<br>&nbsp;<h3>What Triggers Compute in Sigma?</h3><span style="font-family: inherit; font-size: 18px; font-weight: inherit;" data-mce-style="font-family: inherit; font-size: 18px; font-weight: inherit;">Sigma never stores our data. Instead, it translates actions into SQL queries that Snowflake runs.</span></li></ul><p><strong>Common actions that trigger compute:</strong></p><ul><li>Opening a workbook or dashboard</li><li>Applying filters or parameters</li><li>Creating new calculated columns</li><li>Previewing a dataset</li></ul><p><strong>Example:</strong></p><figure id="figure-j714rgl9dcm98q9gsn"><img id="j714rgl9dcm98q9gsn" src="https://cdn.durable.co/blocks/2altuVtyVvDwtL22PTREI273jISL0Rmc9ZjBkfzzOT1YAmGJgAEWFLXih1RII2ZW.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/2altuVtyVvDwtL22PTREI273jISL0Rmc9ZjBkfzzOT1YAmGJgAEWFLXih1RII2ZW.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p><em>When a user selects "2024" from a filter, Sigma dynamically builds and sends this SQL to Snowflake.</em></p><h3>Live Queries vs Cached Results</h3><p>Sigma provides live analytics by default, but it also uses caching to reduce unnecessary compute.</p><p><strong>How caching works:</strong></p><ul><li>Sigma caches the result of recent queries.</li><li>If the same query is run within a short period, Sigma returns cached results.</li><li>Changing filters, fields, or datasets invalidates the cache.<br></li></ul><figure id="figure-328iq77g6om98qcq3i"><img id="328iq77g6om98qcq3i" src="https://cdn.durable.co/blocks/fkrXPydg43juLsgARdpmDhYU662aQKXfwEnLiZWK8vzME2toYwLBcV64NqtYrDJ8.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/fkrXPydg43juLsgARdpmDhYU662aQKXfwEnLiZWK8vzME2toYwLBcV64NqtYrDJ8.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>How to Control Snowflake Spend in Sigma</h3><p>Here are some ways we can reduce costs and speed up performance:</p><ol><li><strong>Set Query Timeout and Limits</strong><ul data-mce-style="list-style-type: square;" style="list-style-type: square;"><li>Sigma lets us limit query run time and row count.</li><li>Helps avoid large accidental queries.</li></ul></li><li><strong>Choose Appropriate Warehouse Sizes</strong><ul data-mce-style="list-style-type: square;" style="list-style-type: square;"><li>Use Small or Medium warehouses for most Sigma work.</li><li>Avoid auto-scaling unless needed.</li></ul></li><li><strong>Use Input Controls to Scope Data</strong><ul data-mce-style="list-style-type: square;" style="list-style-type: square;"><li>Drop-down filters or date pickers reduce the size of result sets.</li></ul></li><li><strong>Use Sigma Datasets for Reuse</strong><ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Build reusable datasets that predefine logic.</li><li>Reduces repeated computation.<br></li></ul></li></ol><figure id="figure-uym6e6zu8nm98qpwt0"><img id="uym6e6zu8nm98qpwt0" src="https://cdn.durable.co/blocks/26dlMyNFfv8AwTH9P68oOpXa8XxHNwNi281z3MLuzWhSaEUnwqXd2tMQFj6xVCQM.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/26dlMyNFfv8AwTH9P68oOpXa8XxHNwNi281z3MLuzWhSaEUnwqXd2tMQFj6xVCQM.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Real-World Example: Monthly Sales Dashboard</h3><p>We built a dashboard that shows monthly sales by region, category, and salesperson.</p><p><strong>What happens behind the scenes:</strong></p><ul><li>Sigma sends a SQL query to Snowflake with filters from the user.</li><li>Snowflake computes the results and returns them to Sigma.</li><li>Sigma displays the results in tables and charts.<br data-mce-bogus="1"></li></ul><figure id="figure-erhujrcfdsm98qss4d"><img id="erhujrcfdsm98qss4d" src="https://cdn.durable.co/blocks/15RRbF1pb8UC1Z7NFJTLDmGVdOGKkQlqS2vQEKDwipWiJBRH6gegl0gIS6XRCGZE.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionX="undefined" data-positionY="undefined" data-fullWidth="false"/></figure><p><strong>Note:</strong> If the same dashboard is opened again within a few minutes without changes, Sigma uses cached results.</p><h3>What Doesn’t Trigger Compute?</h3><p>Sigma actions that don’t hit Snowflake include:</p><ul><li>Changing visualisation types (e.g., bar to line chart)</li><li>Rearranging columns in a table</li><li>Sorting by a field already present in the result set</li></ul><p>This helps us avoid wasting compute on non-SQL changes.</p><h3>Monitoring Usage in Snowflake</h3><p>We can monitor Sigma’s compute usage directly in Snowflake:</p><ul><li>Use the <strong>QUERY_HISTORY</strong> view</li><li>Filter by user, warehouse, time period</li></ul><p><strong>Example:</strong></p><figure id="figure-wvs84kfczcm98qj7or"><img id="wvs84kfczcm98qj7or" src="https://cdn.durable.co/blocks/16Fhx07B1vyuLgxvxb9b3J3i2r3ozGr0AzCjuyrQzhQyfDHPoUZO4WXSubkjs60Y.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/16Fhx07B1vyuLgxvxb9b3J3i2r3ozGr0AzCjuyrQzhQyfDHPoUZO4WXSubkjs60Y.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p>This helps us identify expensive queries and tune them.</p><h3>Summary:</h3><ul><li>Sigma relies on Snowflake for all compute operations.</li><li>Every action in Sigma can potentially trigger a SQL query.</li><li>Caching helps reduce repeated queries, but live analytics means compute is often active.</li><li>We can control costs by limiting query size, using filters, and monitoring queries in Snowflake.</li></ul><p>By understanding the connection between Sigma and Snowflake’s compute layer, we can build faster dashboards and keep our cloud bill under control.</p><p><br></p><p><br></p>

Understanding how Sigma uses Snowflake’s compute layer

<h2>Managing Big Data with Sigma: How to Handle Complex Data Sets<br></h2><p>Big data presents both opportunities and challenges. As organizations collect massive amounts of information, they need tools that simplify analysis without requiring deep technical expertise. Sigma Computing provides a way to explore, visualize, and work with complex data sets directly in the cloud—without SQL or coding experience.</p><h3>Why Big Data Feels Overwhelming</h3><p>Organizations today deal with data from multiple sources:</p><ul><li>Customer interactions</li><li>Transaction records</li><li>IoT sensor readings</li><li>Web traffic logs</li><li>Social media insights</li></ul><p>Each data source generates information in different formats, at high volumes, and often with inconsistencies. Managing this complexity requires:</p><ul><li><strong>Fast access</strong> to large data sets</li><li><strong>Real-time collaboration</strong> across teams</li><li><strong>Scalability</strong> to process millions or billions of rows</li><li><strong>Integration</strong> with cloud data warehouses like Snowflake, BigQuery, and Redshift<br></li></ul><figure id="figure-xfzvrjlm9rm8xzq335"><img id="xfzvrjlm9rm8xzq335" src="https://cdn.durable.co/blocks/8bBqWW77l264xU3eA9NuJfOtKs4Faw6W5DU4kBvRhTLaoX2k9Fuv2pdhmHA7Wfm1.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/8bBqWW77l264xU3eA9NuJfOtKs4Faw6W5DU4kBvRhTLaoX2k9Fuv2pdhmHA7Wfm1.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>How Sigma Helps Manage Big Data</h3><p>Sigma is built for cloud data analytics, making it easier to work with large data sets directly from your cloud warehouse and features like <strong>Data Apps</strong>, <strong>AI-powered analytics</strong>, <strong>enhanced data modeling, and expanded connectivity</strong> further improve its capabilities.</p><p><strong>1. Work with Live Data (No Extracts)</strong></p><ul><li>Unlike traditional BI tools that require data extracts, Sigma connects directly to live data. This means:<ul><li>No delays caused by scheduled refreshes</li><li>No outdated reports based on stale data</li><li>Always working with the most current information</li></ul></li><li><strong>Example:</strong> If our business is tracking daily sales, we can build reports that update in real time as new transactions occur.</li></ul><p><strong>2. Explore Billions of Rows Without Writing SQL</strong></p><ul><li>Sigma’s spreadsheet-like interface makes it easy to analyze data without writing SQL queries. Users can:<ul><li>Drag and drop columns</li><li>Apply filters and aggregations</li><li>Build calculations using familiar spreadsheet functions</li></ul></li><li>Use <strong>Formula Assistant</strong>, Sigma’s AI-powered tool, to help create complex calculations efficiently</li><li><strong>Example</strong>: A marketing team can analyze customer behavior by segmenting data without needing help from a data engineer.</li></ul><p><strong>3. Handle Nested and Semi-Structured Data</strong></p><ul><li>Many cloud warehouses store data in semi-structured formats like JSON. Sigma allows users to:<ul><li>Extract JSON fields into separate columns</li><li>Explore nested fields through the user interface</li><li>Join structured and semi-structured data using table relationships</li></ul></li><li>However, JSON data is not automatically flattened; users must manually extract relevant fields when needed.</li></ul><p><strong>4. Optimize Performance by Pushing Queries to the Warehouse</strong></p><ul><li>Analyzing raw big data can be slow. Sigma optimizes performance by:<ul><li>Pushing calculations directly to the cloud data warehouse</li><li>Minimizing data movement by executing queries at the source</li><li>Allowing drill-downs from summaries to details without performance loss</li><li>Using predefined metrics and reusable tables to standardize calculations and improve governance</li></ul></li></ul><p><strong>Note:</strong> Sigma does not automatically create aggregated tables but allows users to manually build summaries for efficient querying.</p><figure id="figure-2zo7secda9m8xzqt4g"><img id="2zo7secda9m8xzqt4g" src="https://cdn.durable.co/blocks/27pOeKdGkJ0AT1a0IsrLwfAknIUBpLgbaWVXj3vPZEKHmyLYmwROEr0NHI6ZsTR5.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/27pOeKdGkJ0AT1a0IsrLwfAknIUBpLgbaWVXj3vPZEKHmyLYmwROEr0NHI6ZsTR5.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><p><strong>5. Build Interactive Data Apps</strong></p><ul><li>Sigma offers Data Apps, which allow users to:<ul><li>Create interactive, no-code analytics applications</li><li>Guide decision-making through customized workflows</li><li>Go beyond static dashboards for a more dynamic experience</li></ul></li><li><strong>Example:</strong> A finance team can build an interactive budgeting tool that updates forecasts based on real-time inputs.</li></ul><p><strong>6. Expanded Connectivity and Embedded Analytics</strong></p><ul><li>Sigma has expanded its integrations, including:<ul><li>OAuth with write access for Snowflake and Databricks</li><li>Azure SQL Database connector for smoother Microsoft integration</li><li>Improved embedded analytics, offering:<ul><li>More customization for reports</li><li>Interactive exploration for embedded users</li><li>A more intuitive user interface</li></ul></li></ul></li></ul><h3>Real-World Use Cases</h3><p><strong>Retail: Inventory and Sales Forecasting</strong></p><ul><li>Retailers must balance stock levels to avoid overstocking or shortages. With Sigma, they can:<ul><li>Analyze historical sales trends</li><li>Predict demand based on seasonality</li><li>Adjust orders dynamically based on real-time inventory</li></ul></li></ul><p><strong>Healthcare: Patient Data Analysis</strong></p><ul><li>Healthcare organizations deal with massive amounts of patient data across different systems. Sigma helps by:<ul><li>Unifying patient records from various sources</li><li>Identifying trends in treatment outcomes</li><li>Ensuring compliance with data security regulations</li></ul></li></ul><p><strong>Financial Services: Fraud Detection</strong></p><ul><li>Banks and fintech companies need to detect fraudulent transactions in real time. Sigma enables them to:<ul><li>Analyze transaction patterns for anomalies</li><li>Cross-check data across multiple sources</li><li>Flag suspicious activity instantly</li></ul></li></ul><h3>Best Practices for Managing Big Data in Sigma</h3><ol><li><strong>Use Filters to Reduce Query Load</strong><ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Limit the data displayed to focus on relevant records</li><li>Use date ranges and category filters to speed up analysis</li></ul></li><li><strong>Build Aggregated Views for High-Level Analysis</strong><ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Summarize data at the appropriate level (e.g., daily, weekly, monthly)</li><li>Drill down only when needed to preserve performance</li></ul></li><li><strong>Use Sigma’s Built-in Spreadsheet Functions</strong><ul data-mce-style="list-style-type: square;" style="list-style-type: square;"><li>Utilize familiar spreadsheet-like calculations for quick analysis</li><li>Apply functions such as moving averages or percentile rankings</li></ul></li><li><strong>Ensure Data Governance and Access Control</strong><ul data-mce-style="list-style-type: square;" style="list-style-type: square;"><li>Use role-based permissions to restrict sensitive data</li><li>Share reports with appropriate stakeholders without exporting raw data</li></ul></li><li><strong>Integrate Sigma with Other Business Tools</strong><ul data-mce-style="list-style-type: square;" style="list-style-type: square;"><li>Connect dashboards with Slack, Google Sheets, or CRM platforms</li><li>Automate workflows using Sigma’s API</li></ul></li></ol><h3>Conclusion</h3><p>Managing big data doesn’t have to be complicated. Sigma provides a cloud-native solution that simplifies exploration, visualization, and collaboration.</p><p>With advancements like Data Apps, AI-powered tools, improved data modeling, and expanded connectivity, Sigma continues to enhance how teams analyze complex data sets. Whether we’re analyzing millions of customer transactions, tracking business performance, or detecting fraud, Sigma makes big data analysis more interactive and accessible.</p><p>If your team is struggling with large-scale data analysis, Sigma might be the missing piece to make insights faster and easier.</p><p><br></p>

Managing Big Data with Sigma Comutng

<h2>What Are Data Apps in Sigma Computing?</h2><p>Sigma Computing enables teams to create interactive, no-code data applications (data apps) directly within the platform. These apps allow business users to explore, filter, and interact with data without requiring SQL expertise.</p><h3>Key Features:</h3><ul><li><strong>No-Code Interface:</strong> Build apps with drag-and-drop elements.</li><li><strong>Live Data Interaction:</strong> Query data in real-time without exporting.</li><li><strong>Custom UI Components:</strong> Use buttons, dropdowns, and filters for better experience.</li><li><strong>Security &amp; Access Control:</strong> Share insights securely with role-based permissions</li></ul><figure id="figure-npwui14jspm8nehfuz"><img id="npwui14jspm8nehfuz" src="https://cdn.durable.co/blocks/339852awCdJCddZW5gyMEZiwx8u23t0SLiJE1Y8aTu7fsJQhQEDgbnXDMpf3qrsW.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: undefined undefined;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/339852awCdJCddZW5gyMEZiwx8u23t0SLiJE1Y8aTu7fsJQhQEDgbnXDMpf3qrsW.png" data-mce-style="aspect-ratio: 16/9; object-position: undefined undefined;"></figure><h3>How to Build a Data App&nbsp;</h3><p>Creating a data app involves structuring data, defining user interactions, and enabling real-time updates. Here’s a step-by-step guide:</p><ul><li><strong>Step 1: Define the Purpose</strong><ul><li>Identify the business problem the app will solve.</li><li>Determine key metrics and data sources.</li></ul></li><li><strong>Step 2: Prepare Your Data</strong><ul><li>Connect to data sources (Snowflake, Google BigQuery, etc.).</li><li>Clean and structure data for easy access.</li></ul></li><li><strong>Step 3: Design the User Interface</strong><ul><li>Use Input Tables for user-driven data filtering.</li><li>Add Dropdowns and Buttons for interactivity.</li><li>Configure Conditional Formatting for highlighting insights.</li></ul></li><li><strong>Step 4: Add Real-Time Interactivity</strong><ul><li>Use Sigma’s parameter feature to dynamically update charts and tables.</li><li>Set up Drilldowns to allow users to explore granular details.</li></ul></li><li><strong>Step 5: Publish and Share</strong><ul><li>Set permissions for different user roles.</li><li>Embed the app within dashboards or external platforms.</li></ul></li></ul><figure id="figure-i0eh2urq7om8nfni5d"><img id="i0eh2urq7om8nfni5d" src="https://cdn.durable.co/blocks/21yMMIDmeaR77GxYIl7LyqMkucd8B4dvAeareWdXAtw2fR8K2QVB8SJ4nE0WS4Uh.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl selected" style="aspect-ratio: 16/9; object-position: undefined undefined;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/21yMMIDmeaR77GxYIl7LyqMkucd8B4dvAeareWdXAtw2fR8K2QVB8SJ4nE0WS4Uh.png" data-mce-style="aspect-ratio: 16/9; object-position: undefined undefined;" data-mce-selected="1"></figure><h3>Interactive Features That Make Data Apps Powerful</h3><ul><li><strong>Parameterized Filters</strong><ul><li>Let users adjust filters dynamically without modifying queries.</li><li><strong>Example:</strong> A dropdown to select different product categories and update sales reports instantly.</li></ul></li><li><strong>Dynamic Calculations</strong><ul><li>Users can modify input values, triggering real-time calculations.</li><li><strong>Example:</strong> A sales projection tool where users enter expected growth rates to update charts.</li></ul></li><li><strong>Drill-Through Capabilities</strong><ul><li>Click on data points to explore detailed records.</li><li><strong>Example:</strong> Clicking on a country in a global sales map to view regional breakdowns.</li></ul></li><li><strong>Embedded Workflows</strong><ul><li>Integrate apps into other platforms like Slack or web portals.</li><li><strong>Example:</strong> A finance app that pulls live revenue data into executive dashboards</li></ul></li></ul><h3>Real-World Use Cases&nbsp;</h3><ol><li><strong>Sales Performance Dashboard</strong><ul><li>Track regional and product-wise sales with interactive selectors.</li><li>Users filter by timeframe, product category, and sales team.</li></ul></li><li><strong>Financial Forecasting Tool</strong><ul><li>Allows finance teams to simulate revenue scenarios.</li><li>Users input expected costs and revenue trends to update forecasts.</li></ul></li><li><strong>Customer Support Insights</strong><ul><li>Displays real-time customer feedback and support ticket trends.</li><li>Users filter by sentiment, agent, and response time.</li></ul></li><li><strong>Inventory Management System</strong><ul><li>Helps supply chain teams track stock levels and predict shortages.</li><li>Users select warehouse locations to view inventory data.</li></ul></li></ol><h3>Importance</h3><p>Data apps bridge the gap between data teams and business users. They make insights actionable without requiring advanced analytics skills. By using interactive components, organizations can ensure that data is not just stored but actively used for better decision-making.</p><figure id="figure-fvowhow6esm8nfomaj"><img id="fvowhow6esm8nfomaj" src="https://cdn.durable.co/blocks/18nvEB8S2CI3z2ixgwSy74UVnDpQiW3heg7cvA9WVou6xp2LMgYznShEbTRa25dx.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/18nvEB8S2CI3z2ixgwSy74UVnDpQiW3heg7cvA9WVou6xp2LMgYznShEbTRa25dx.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Conclusion</h3><p>Building data apps in Sigma Computing transforms raw data into interactive experiences. With minimal effort, teams can create powerful applications that drive meaningful insights.</p><p>Want to see Sigma data apps in action? Start experimenting today with interactive dashboards that fit your business needs!</p><p><br></p><p><br><br><br></p>

Data Apps in Sigma Computing

<h2>Mastering Input Tables in Sigma Computing</h2><p>Data analysis often requires flexibility—whether it’s adding assumptions for forecasting, adjusting data for scenario modeling, or simply incorporating new information manually. Sigma Computing’s Input Tables provides an intuitive way to input and modify data directly in a cloud-based environment, making data analysis more interactive and dynamic.</p><h3>What Are Input Tables?</h3><p>Input Tables in Sigma allow users to manually enter data directly into their workbooks. Unlike standard tables that only display pre-existing data, Input Tables let users modify and enhance their datasets dynamically.</p><p><strong>They are particularly useful for:</strong></p><ul><li><strong>Scenario Modeling:</strong> Enter different variables and analyze outcomes.</li><li><strong>Manual Adjustments:</strong> Fix data inconsistencies without modifying source data.</li><li><strong>Collaborative Planning:</strong> Teams can input estimates, assumptions, or project plans.</li></ul><p>This feature enables the integration of new data points and the augmentation of existing datasets from sources like Snowflake or Databricks, facilitating rapid prototyping, advanced modeling, forecasting, and what-if analyses—all without altering the original source data.</p><h3>Types of Input Tables</h3><p>Sigma offers two primary types of Input Tables:</p><ul><li><strong>Empty Input Tables:</strong> These are blank tables where users can manually add data. They are ideal for scenarios where you need to input data that doesn't already exist in your data warehouse.</li><li><strong>Linked Input Tables:</strong> These tables are derived from existing data elements within your workbook. They allow you to link new data entries to existing datasets, facilitating more complex analyses and data relationships.</li></ul><figure id="figure-y182mjofzm8e4iw86"><img id="y182mjofzm8e4iw86" src="https://cdn.durable.co/blocks/bBBiXdmUJVBxwI3ECHgGR10f25Q7YTAkqnYZrEZnimYhcG4I3iBHIKnGVwRpigZr.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/bBBiXdmUJVBxwI3ECHgGR10f25Q7YTAkqnYZrEZnimYhcG4I3iBHIKnGVwRpigZr.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Creating an Input Table</h3><p>To create an Input Table in Sigma, follow these steps:</p><ul><li><strong>Enter Edit Mode:</strong> Open your workbook and switch to edit mode.</li><li><strong>Access the Add New Panel:</strong> Navigate to the 'Add New' panel within the workbook.</li><li><strong>Select Input Table Type:</strong>For an empty table, choose 'Empty' under the Input Tables section.</li></ul><p>For a linked table, select 'Linked' and choose the source element you wish to link to.</p><ul><li><strong>Define Table Structure:</strong> Add columns and specify data types as needed.</li><li><strong>Input Data:</strong> Manually enter your data into the table cells.<br></li></ul><figure id="figure-allssw1qpam8e4qxfc"><img id="allssw1qpam8e4qxfc" src="https://cdn.durable.co/blocks/14wI310BuiUQ4hyIosdvthpbEfpPRJVBzTuuvhQOS7Fk25dlCn5wcgGd4hfKF0O4.png" alt="" title="" class="object-cover bg-gray-100 rounded-lg md:rounded-xl lg:rounded-2xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionX="undefined" data-positionY="undefined" data-fullWidth="false"/></figure><h3>Benefits of Using Input Tables</h3><ul><li><strong>Dynamic &amp; Interactive</strong><ul><li>Unlike traditional static reports, Input Tables allow real-time modifications without altering the underlying data source.</li></ul></li><li><strong>Enhanced Collaboration</strong><ul><li>Multiple users can simultaneously enter, adjust, and analyze data without spreadsheet chaos.</li></ul></li><li><strong>Scenario Modeling</strong><ul><li>Quickly adjust variables and assumptions to see their impact on calculations.</li></ul></li></ul><h3>Use Cases for Input Tables:</h3><ul><li><strong>Data Entry:</strong> Input Tables enable users to manually input data that may not be available in existing datasets, allowing for the incorporation of external information into analyses.</li><li><strong>Data Augmentation:</strong> Users can add supplementary data to existing datasets, enriching analyses with additional context or variables.</li><li><strong>Scenario Modeling and What-If Analysis:</strong> Input Tables facilitate the creation of hypothetical scenarios by allowing users to adjust variables and observe potential outcomes without modifying the original data.<br></li></ul><table style="border-collapse: collapse; width: 100%;" border="1" data-mce-style="border-collapse: collapse; width: 100%;"><tbody><tr><td style="width: 38.3333%;" data-mce-style="width: 38.3333%;"><strong>USE CASE</strong></td><td style="width: 61.6667%;" data-mce-style="width: 61.6667%;"><strong>HOW INPUT TABLES HELP</strong></td></tr><tr><td style="width: 38.3333%;" data-mce-style="width: 38.3333%;"><strong>Budget Planning&nbsp;</strong></td><td style="width: 61.6667%;" data-mce-style="width: 61.6667%;">Enter budget estimates and compare them with actuals</td></tr><tr><td style="width: 38.3333%;" data-mce-style="width: 38.3333%;"><strong>Risk Assessment</strong></td><td style="width: 61.6667%;" data-mce-style="width: 61.6667%;">Manually input risk factors and model their impact</td></tr><tr><td style="width: 38.3333%;" data-mce-style="width: 38.3333%;"><strong>HR &amp; Payroll</strong></td><td style="width: 61.6667%;" data-mce-style="width: 61.6667%;">Add employee salary adjustments for financial forecasting&nbsp;</td></tr><tr><td style="width: 38.3333%;" data-mce-style="width: 38.3333%;"><strong>Operational Planning&nbsp;</strong></td><td style="width: 61.6667%;" data-mce-style="width: 61.6667%;">Track equipment maintenance costs and schedules</td></tr></tbody></table><h3>Best Practices for Using Input Tables</h3><ul><li><strong>Use Linked Input Tables Wisely:</strong> When modifying existing datasets, always ensure data integrity.</li><li><strong>Keep Input Table Data Clean:</strong> Avoid unnecessary data entry to prevent clutter.</li><li><strong>Utilize Sigma’s Functions:</strong> Combine Input Tables with Sigma's built-in calculations and aggregations for more robust analysis.</li></ul><h3>Practical Example: Sales Forecasting</h3><p>Imagine you're a sales manager aiming to project future sales based on various scenarios. Here's how you can utilize an Input Table for this purpose:</p><ul><li><strong>Create an Empty Input Table:</strong> Set up a table with columns for 'Month', 'Projected Sales', and 'Marketing Spend'.</li><li><strong>Input Data:</strong> Enter data for each month, including projected sales figures and planned marketing expenditures.</li><li><strong>Perform Analysis:</strong> Use Sigma's analytical tools to correlate marketing spend with projected sales, helping you identify optimal investment strategies.</li></ul><h3>Conclusion:</h3><p>Input Tables in Sigma Computing provide a versatile and user-friendly means to enhance data analyses. By allowing manual data entry and integration with existing datasets, they support a wide range of applications, from basic data augmentation to complex scenario modeling. This functionality empowers users to conduct more comprehensive and flexible analyses, leading to more informed business decisions. Input Tables empower teams to work smarter with data.</p><p><br></p>

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Row Access Policies in Snowflake

Column-level masking in Snowflake

Understanding how Sigma uses Snowflake’s compute layer

Managing Big Data with Sigma Comutng

Data Apps in Sigma Computing

Input tables in Sigma Computing