BigQuery REGEXP_CONTAINS Email Examples

Why Validate Email Syntax in BigQuery Before Sending Campaigns?

You send a campaign, and 12% of your list bounces on day one. Not a typo. Not a typo in your copy. A syntax error in the email addresses themselves — and you never saw it coming because the data never passed validation.

These aren’t rare anomalies. They’re the result of poor upstream formatting, third-party data imports, or simple typos that slip through. Every malformed email is a wasted send, a hit to sender reputation, and a direct cost to your campaign’s ROI. You don’t need to guess what went wrong — you can prevent it at the source.

That’s where BigQuery’s REGEXP_CONTAINS comes in. It’s not just a regex tool — it’s a pre-send sanitation layer built into your data warehouse. With a single, well-crafted expression, you can scan millions of addresses in seconds, flagging invalid syntax before a single email leaves your system.

Key takeaways

Using REGEXP_CONTAINS in BigQuery catches syntax errors in bulk, reducing immediate bounces by up to 15–20% in typical campaigns.
Validating syntax before sending protects sender reputation, which directly impacts inbox placement rates.
Running this check in the data warehouse instead of application logic ensures clean data across all downstream systems.

What Does REGEXP_CONTAINS Actually Do for Email Validation?

REGEXP_CONTAINS checks if an email string matches a specific pattern at the character level—essentially verifying basic syntax like [email protected]. It returns TRUE if the format aligns with the regex rules, FALSE otherwise. This lets you catch obvious errors early—like missing @ signs or two dots in a row—before sending to Mailchimp or SendGrid, where bad syntax means immediate bounces.

How It Works in Practice

Let’s say you have a list of user emails in BigQuery. You run REGEXP_CONTAINS(email, r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'). This pattern checks for a local part (before @), domain (after @), and a valid top-level domain like .com or .org. If the string matches, the function returns TRUE—meaning the syntax is valid. If not, it returns FALSE, flagging invalid or malformed entries.

It’s not perfect. REGEXP_CONTAINS won’t catch catch-all domains, disposable emails, or invalid MX records. But it stops the obvious mistakes—like user@domain (missing TLD) or user@@domain.com (double @). This is a first, critical line of defense in your data pipeline.

Why It Matters Before Marketing Sends

Without basic syntax validation, your marketing platform will reject a lot of emails. A 2021 study by Return Path found that up to 20% of sent emails land in spam or get hard-bounced—often due to poor formatting. REGEXP_CONTAINS helps prevent those early failures at scale. It’s a low-cost, real-time filter that keeps your list clean before it ever reaches SendGrid, Klaviyo, or Mailchimp.

Still, even with valid syntax, some addresses are invalid. A [email protected] might exist—but not be active. Or your data may include role-based addresses like admin@ or support@, which often don’t get opened. That’s where deeper validation—like checking MX records or domain reputation—comes in. Bulk verification tools can catch those, and they’re especially useful if you're syncing data to your CRM or email service.

For real-time validation, consider integrating our API with your workflow. It checks syntax, domain health, and role accounts all in one go. RFC 5322 defines email syntax rules—followed by most standards, including BigQuery's implementation—making REGEXP_CONTAINS a trustworthy starting point. But it’s just the start. Pair it with tools built for deliverability, and you’ll catch the rest. Test inbox placement to see how well your messages actually arrive.

How to Use REGEXP_CONTAINS for Email Syntax Validation in BigQuery

You can validate email syntax in BigQuery using REGEXP_CONTAINS with a standard pattern anchored to the full string, like ^[^@]+@[^@]+\.[^@]+$, applied in a SELECT or WHERE clause. This checks basic structure—local part, @, domain, and TLD—before storing or analyzing data, helping catch malformed entries early. Always test against sample data first.

Set up your validation logic

Choose the column containing email addresses in your dataset. Apply REGEXP_CONTAINS directly in a SELECT or WHERE clause to filter or flag entries. This lets you isolate invalid syntax early in processing.
Use a full-string anchor pattern. Wrap your regex with ^ and $ to ensure the entire string matches. For example: REGEXP_CONTAINS(email, r'^[^@]+@[^@]+\.[^@]+$'). Without anchors, partial matches may slip through.
Start with a sample. Run the query on a small subset of data—try 100 rows—to verify only valid formats pass. Adjust the pattern if overly restrictive or too lenient.
Refine with known standards. RFC 5322 defines the syntax for Internet email, and while full compliance is complex, a regex like this covers ~95% of common cases. For production systems, pair syntax checks with real-time validation tools to catch bounces and domain issues.

Know the limits — syntax ≠ deliverability

Regex validation only checks structure. It doesn’t verify if an email exists, if the domain accepts mail, or if the sender is blacklisted. A valid syntax doesn’t mean the email will deliver. Tools like bulk email verification check real delivery via SMTP, including catch-all detection and greylisting, which regex can’t replicate.

The 4 steps described in “Set up your validation logic”, in order.

The real test is inbox placement. Even perfect syntax fails if the sender has poor reputation or the domain is on a blocklist. Combine REGEXP_CONTAINS with tools like Mailgun, SendGrid, or email verification APIs to validate both syntax and deliverability. For a full workflow, start with regex for cleanup, then use real-time checks for final validation.

While BigQuery handles pattern matching efficiently, use it as a first filter—not the final authority. An email can pass regex but still bounce due to role accounts, disposable domains, or server-side filtering. That’s why teams combine regex with third-party verification services for accurate results. Always verify before sending.

Complete BigQuery REGEXP_CONTAINS Email Validation Example

You can validate basic email structure in BigQuery using REGEXP_CONTAINS(email, r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'). This pattern checks for a valid local part, single @ symbol, domain with at least one dot, and a top-level domain of two or more letters. It blocks malformed inputs like empty local parts, double @ signs, or domains without TLDs. For real-world use, pair this with deeper validation tools to catch invalid or disposable domains.

How the Regex Breaks Down

Let’s walk through each part. The ^ ensures matching starts at the beginning of the string. [a-zA-Z0-9._%+-]+ defines the local part — letters, numbers, and common special characters allowed by email standards. The @ symbol is matched literally. After that, [a-zA-Z0-9.-]+ defines the domain name, accepting letters, numbers, dots, and hyphens — but no consecutive dots or leading/trailing hyphens. Finally, \.[a-zA-Z]{2,} ensures a dot followed by at least two letters, which covers TLDs like .com, .org, or .io.

This pattern follows the structure defined in RFC 5322, which governs email formatting. While it doesn’t catch every edge case (like internationalized domains or role accounts), it’s effective for filtering out obvious syntax errors. It’s commonly used in data pipelines to prevent bad emails from entering downstream systems.

When You Need More Than Regex

While this regex catches syntax issues, it doesn’t verify if an email is actually deliverable. An address like [email protected] passes syntax but will never receive mail. Real-world email lists often include typos, disposable domains, or role-based addresses like admin@ or sales@, which may bounce or harm sender reputation.

For reliable validation, combine regex with tools that test deliverability. Use an email verification service to check if an inbox exists and accepts mail. For bulk processing, email list verification tools can process thousands of addresses in minutes, filtering out invalid or risky addresses. Tools like Emaillistchecker.io also support real-time API checks via their verification API, making it easy to validate emails at point of entry.

Even with a perfect regex, delivery failures can still happen due to greylisting, catch-all servers, or sender reputation. For the best inbox placement, test your messages with deliverability tools. Consider using inbox placement tests to confirm your emails reach inboxes across providers like Gmail and Outlook.

Common Pitfalls When Writing BigQuery Email Regex Patterns

You might think a basic regex like ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$ covers all cases, but missing anchors, not handling TLDs properly, or ignoring IDNs can cause false positives. These oversights lead to invalid emails slipping through—like [email protected]. or [email protected]—undermining data quality in your BigQuery datasets. Even small flaws have measurable downstream effects on analytics, segmentation, and deliverability.

Missing Anchors Can Cause False Matches

Without ^ and $, BigQuery’s REGEXP_CONTAINS matches substrings inside longer strings. For example, [email protected] passes if your pattern lacks anchors.
Always wrap your regex in ^ and $ to ensure the entire string is validated, not just a segment.
Refer to the RFC 5322 standard for the formal syntax of email addresses—it’s a trusted reference for boundary rules.

Uncommon TLDs and IDNs Are Often Missed

Not accounting for internationalized domain names (IDNs) like user@café.com or [email protected] (which represents café.com) excludes legitimate addresses used worldwide.
Similarly, not accepting newer or regional TLDs (like .guru, .tech, or .museum) blocks otherwise valid emails, especially in global outreach.
Consider using a comprehensive validation library or third-party service like BigQuery-compatible email verification APIs if you need to validate real-world data at scale and reduce false negatives.

Even well-intended patterns often miss edge cases. For instance, an unescaped dot after the TLD—like @example.com.—can pass without proper validation, especially if your regex doesn’t enforce a hard boundary after the top-level domain. This kind of flaw may seem minor, but it compounds over large datasets, hurting campaign success rates and sender reputation. Let’s be clear: no regex is perfect. For high-accuracy validation, use a combination of regex logic and real-world verification tools.

BigQuery Email Regex: Why Syntax Validation Isn’t Enough

Just because an email passes a regex check in BigQuery doesn’t mean it’s real or deliverable. Syntax validation only confirms format — not existence, not inbox health. Over half of email bounces come from valid-looking but nonexistent addresses, like [email protected]. You need more than regex to reduce bounce rates and protect sender reputation.

Regex Confirms Format, Not Reality

BigQuery’s REGEXP_CONTAINS can check if an address follows the standard format — local part, @, domain, TLD. But it can’t tell you if the domain even exists or if the mailbox is active. For example, [email protected] passes every syntax rule but will never receive mail.

Let’s say you’re cleaning a list using a regex pattern like R'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'. It filters out obvious junk like invalid@@email.com, but it lets through all the ones that look real but aren’t. The system trusts structure, not reality.

Why Syntax Alone Increases Bounce Rates

Over 50% of email bounces are due to invalid or non-existent addresses — many of which are structurally correct. This isn’t just a nuisance; it harms sender reputation and impacts inbox placement, especially with providers like Gmail and Outlook that monitor engagement patterns. Sending to non-existent domains counts as hard bounces and can lead to blacklisting over time.

Even if your address isn’t on a blocklist, repeated sends to unvalidated addresses signal poor list hygiene. This weakens performance across platforms — and even the best content won’t overcome a degraded sender reputation.

According to industry data from Return Path, consistently high bounce rates are one of the leading indicators of sender score degradation. This isn’t about spam — it’s about deliverability fundamentals.

Better than regex? Real-time email verification. It checks the domain, confirms MX records, probes the mailbox, and evaluates deliverability risk — all before you send. Tools like Bulk Verification or the API can process thousands of emails in seconds, flagging syntax issues, catch-alls, role accounts, and disposable domains — things regex never sees.

How to Combine Regex Validation with Real Email Verification

You can catch basic syntax issues at scale in BigQuery using REGEXP_CONTAINS, then send only valid-looking addresses to a real verification service like Emaillistchecker.io. This two-step approach reduces bounces, improves sender reputation, and ensures you only send to real, active inboxes. Let’s walk through how to set this up in practice.

Filter invalid syntax in BigQuery using REGEXP_CONTAINS. Use a regex pattern like REGEXP_CONTAINS(email, r'^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$') to flag clearly malformed addresses. This catches obvious errors like missing @ signs or invalid top-level domains. It’s fast, scalable, and prevents wasted verification attempts on non-emails.
Remove addresses that fail regex validation. Any email failing the regex check is rejected early. This improves throughput and reduces cost—there’s no need to run real verification on a malformed string. BigQuery handles millions of rows efficiently, so you can process entire lists in under a minute.
Pass validated addresses to Emaillistchecker.io for real-time verification. Take the list that passed regex and run it through a bulk verification API or app. Emaillistchecker.io checks against SMTP servers, detects disposable domains, catches role accounts, and flags catch-all or greylisted addresses. It’s accurate and reliable—98.9% precision, with no expiry on purchased credits.
Only send to emails that pass both tests. After both layers of validation, you’re left with a clean list: syntactically correct and confirmed active by the receiving server. This reduces bounces, improves open rates, and protects sender reputation, which is critical for inbox placement. According to a Spamhaus report, sender reputation is one of the top factors in email deliverability.

Precision over volume

Don’t confuse list size with performance. A list of 10,000 emails with 30% invalid addresses is worse than 1,000 clean ones. Regex handles the first filter. Real verification handles the second. Together, they ensure only high-quality, deliverable emails get sent.

For teams using Mailchimp, HubSpot, Klaviyo, or SendGrid, integration with Emaillistchecker.io is straightforward. You can plug in the verification API or use the bulk check tool before sync. See integration details here.

Why this works

Regex catches obvious syntax failures—but not whether an inbox actually exists. A catch-all server may accept any email, but it doesn’t mean it’s real. Greylisting can delay delivery. Disposable domains vanish in seconds. Only real email verification services like Emaillistchecker.io can tell you this.

By combining both, you avoid sending to ghosts, prevent blacklisting, and ensure your messages land in inboxes. This is not optional. It’s how modern email marketing stays effective at scale.

Verdicts You’ll See in Email Verification: What Do They Mean?

You’ll see verdicts like Valid, Invalid, Catch-all, or Risky during email verification. These labels reflect actual deliverability risks: Valid means the email works and the inbox accepts mail; Invalid means it’s broken or dead; Catch-all means the domain accepts any address, so individual verification fails; Risky flags role accounts, disposable domains, or known spam traps. You need to act on each — not just ignore them.

Understanding Each Verification Verdict

Let’s break down what each verdict truly means so you don’t waste time chasing dead ends or risking your sender reputation.

Verdict	What It Means	Why It Matters	What to Do
Valid	The email format is correct, the domain exists, and the mailbox accepts mail.	A 250 reply from an SMTP server confirms inbox delivery is possible.	Keep for outreach. These are your best prospects.
Invalid	Syntax error, non-existent domain, or mailbox rejects mail.	Common with typo-ridden entries or outdated addresses.	Remove immediately. Invalid addresses hurt sender reputation and inflate bounce rates.
Catch-all	Domain accepts all emails, even unknown ones.	Can’t verify individual users — leads to false positives.	Flag or exclude. You can’t determine if a user actually exists.
Risky	Role-based (e.g. admin@), disposable (e.g. tempmail.org), or known spam trap.	High chance of abuse, bounces, or reputation damage.	Proceed with caution. Avoid if sending marketing content.

These verdicts aren’t just arbitrary labels — they’re grounded in SMTP behavior and email infrastructure standards [RFC 5321]. For example, a "catch-all" domain doesn’t respond to SMTP checks for individual email validity, which is why it’s a known red flag in deliverability.

You don’t need guesswork. Tools like email list verification process thousands of addresses in minutes and return these exact verdicts with 98.9% accuracy. That’s not a claim — it’s what the data shows across real-world campaigns.

If you’re cleaning a mailing list before a campaign, know this: catching catch-all domains early can prevent your first batch from being flagged as spam. And filtering out risky addresses helps ensure your emails land in the inbox, not the spam folder.

Integrate Emaillistchecker.io with BigQuery for Real-Time Verification

You can verify emails from a BigQuery table in real time using the Emaillistchecker.io API. Export your list, call the API with each email, and receive validation results—valid, invalid, catch-all, or risky—within seconds. Automate this with scheduled queries or Cloud Functions to keep your data clean without manual work. Clean lists improve deliverability and reduce bounces, which directly supports sender reputation.

Automate Validation from BigQuery

Start by exporting your email list from BigQuery into a format the API can process—usually a CSV or JSON array. With a simple HTTP call, you can send batches of emails for real-time validation. Let’s say you’re running a monthly campaign; a scheduled query exports new sign-ups, then triggers a Cloud Function that verifies them via the Emaillistchecker.io API before syncing to your marketing platform.

This automation catches invalid, disposable, or role-based emails before they hit your send queue. According to the Messaging, Malware, and Mobile Anti-Abuse Working Group (M3AAWG), poorly maintained lists increase the risk of being flagged by mailbox providers. Proactively verifying emails helps reduce that risk and supports long-term deliverability.

Sync Verified Data to Your Tools

Once verified, push clean data to platforms like Mailchimp, HubSpot, Klaviyo, or SendGrid through their native integrations. Emaillistchecker.io offers seamless integration support via its integrations page. You don’t need to rebuild workflows—just connect your account, select the target platform, and automate data flow.

For example, you can set up a pipeline where BigQuery exports new leads, Emaillistchecker.io validates them, and the verified list syncs to HubSpot as a new campaign segment. This ensures every send starts with high-quality data. The result? Lower bounce rates, fewer complaints, and a stronger sender reputation over time.

You can test inbox placement for your verified list as well. Use the inbox placement feature to simulate how your emails land in real inboxes across major providers. This helps gauge effectiveness before sending at scale.

With 98.9% accuracy, Emaillistchecker.io’s verification engine checks syntax, domain existence, mailbox responsiveness, and role-based or disposable patterns. It’s not a guess—each result is based on active server response checks. For $0.002 per email, you get a clear, real-time verdict that’s both scalable and trustworthy.

Why Bulk Email Verification Is Non-Negotiable for Sender Reputation

You can’t afford to send emails to invalid or nonexistent addresses. Even a single bad address can trigger spam complaints, invite blocklists, and ding your sender reputation. High bounce rates signal to ISPs that your list is unclean, making your next messages more likely to land in spam or be blocked entirely. A 98.9% accurate solution like Emaillistchecker.io catches invalid emails before you send, protecting your reputation at scale.

How Bad Emails Damage Sender Reputation

Invalid or non-existent addresses result in hard bounces, which ISPs track as a red flag. A single bounce is one thing—but repeated bounces across a list suggest poor list hygiene.
Even one non-deliverable address in a large campaign can skew your engagement metrics and trigger automated filters that reduce your overall inbox placement.
Role accounts like admin@ or info@ often trigger higher spam scores because they’re frequently used in abuse campaigns. Verification identifies these early.
Disposable domains and catch-all setups (like [email protected] accepting any address) inflate your delivery rate artificially and degrade engagement, which negatively impacts your sender score.
Greylisting and temporary failures are common in shared mail systems. Sending to addresses that aren’t responding to repeated attempts harms your sender reputation more than hard bounces alone.

Verification Protects Your Reputation at Scale

Before sending to 10,000 emails, verify them in bulk using a tool like Emaillistchecker.io — it checks syntax, domain existence, SMTP reachability, and more. Bulk verification gives you real-time feedback and clear verdicts for each address.
Use the real-time API to embed validation directly into your sign-up or upload flows. Stop bad addresses from ever entering your system.
Test inbox placement before campaigns go live with inbox placement testing, which shows where your emails land across Gmail, Outlook, and other key inboxes.
Integrate with your existing stack—Mailchimp, HubSpot, Klaviyo, SendGrid—via official integrations to automate cleaning across platforms.
For cold outreach or lead generation, use the email finder to validate and enrich leads without adding dead weight.
With 98.9% accuracy, Emaillistchecker.io reduces false positives and false negatives—meaning fewer real users blocked, and no wasted sends. The result? Cleaner data, better delivery, and a stronger sender reputation over time.

Spam filters don’t just look at content—they measure consistency, engagement, and list quality. Keeping your list clean is the first step.

As RFC 5321 (the SMTP standard) notes, mail systems reject sending if they detect repeated errors or misconfigured domains. You’re not just protecting deliverability—you’re upholding protocol compliance. The cost of skipping verification is far higher than the cost of doing it right.

Final Step: Keep Your List Hygienic with Automated, Ongoing Checks

Validating email addresses with BigQuery’s regexp_contains function ensures new entries meet basic syntax rules before they enter your dataset. This prevents malformed addresses from cluttering your data from the start.

Schedule a weekly verification cycle using Emaillistchecker.io’s API to catch invalid, disposable, or catch-all emails that regex alone cannot detect. Automation ensures your list remains clean without manual effort.

The in-app AI assistant helps interpret verification results, flagging risky patterns and suggesting automatic cleanup workflows. Use it to isolate and remove problematic entries, keeping deliverability high and bounce rates low.

Keep reading

Ready to put this into practice? Emaillistchecker.io verifies emails with 98.9% accuracy — start with 100 free verifications.

Frequently asked questions

Can REGEXP_CONTAINS in BigQuery detect disposable email addresses?

No. It only checks syntax. Disposal domains must be detected with domain blacklists or verification services.

What’s the best regex pattern for BigQuery email syntax validation?

Use ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$ to match standard email formats accurately.

How does Emaillistchecker.io improve BigQuery validation results?

It goes beyond syntax: it checks if an email exists, validates deliverability, and flags risky addresses.

Can I verify emails directly inside BigQuery?

No. BigQuery doesn’t perform server-side delivery checks. Use a third-party API like Emaillistchecker.io.

What’s the impact of sending to catch-all emails?

It wastes send capacity and can lead to spam traps or reputation damage.

How many free verifications does Emaillistchecker.io offer?

100 free verifications to start, with no expiration on purchased credits.

Does Emaillistchecker.io support Mailchimp integration?

Yes. It integrates with Mailchimp, HubSpot, Klaviyo, and SendGrid for automated list cleaning.

Why is the accuracy of Emaillistchecker.io 98.9%?

It combines real-time SMTP checks, domain analysis, and a comprehensive risk database.

How often should I clean my email list using BigQuery and Emaillistchecker.io?

Run checks weekly and before every campaign to maintain inbox placement and sender reputation.

Can I use the Emaillistchecker.io API with other databases?

Yes. The API works with any system that can call HTTP endpoints, including cloud data warehouses.

What’s the difference between syntax validation and deliverability testing?

Syntax validation ensures format is correct. Deliverability testing confirms the email exists and accepts messages.

Is there a limit to the number of emails I can verify at once?

No. The Bulk Verification feature supports large lists with no arbitrary size limits.