# snowflake Published articles for snowflake. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building a Centralized Alerting Framework for Data Quality Monitoring and Incident Management DevFeed: [Building a Centralized Alerting Framework for Data Quality Monitoring and Incident Management]() Original publisher: [Read original article]() Author: Manav Mehta Published: 2026-06-18T07:11:45Z Content type: article Language: en Sources: [Helpshift]() Topics: [Data Quality](), [incident management](), [Monitoring](), [DataOps]() Tags: [data-engineering](), [data-quality](), [email](), [incident-management](), [monitoring](), [notifications](), [observability](), [pipelines](), [slack](), [snowflake]() ### AI overview This article describes the design of a centralized alerting and incident management framework for data quality and pipeline monitoring. The framework uses Snowflake's native Alerting capabilities with Email, Slack, and Splunk On-Call integrations to detect issues, notify the appropriate engineers, escalate critical incidents, and provide actionable context. ### Source excerpt Before We Knew Better As our data platform grew, so did the number of pipelines, scheduled tasks, and data quality checks running every day. While Snowflake provided a reliable platform for storing and processing data, operational monitoring was fragmented across multiple systems. Data quality failures were often discovered only after downstream reports showed inconsistencies. Pipeline issues sometimes required engineers to manually inspect logs, query tables, and trace execution paths before identifying the root cause. The challenge wasn't detecting failures -- we already had mechanisms to identify them. The real challenge was ensuring the right people were notified quickly, with enough context to take action. Questions during on-call incidents were often similar: Did the pipeline fail or was data simply delayed? Which validation check triggered the alert? Who should respond to the issue? How can we ensure critical failures don't get missed overnight? As the number of pipelines increased, manually monitoring these failures became increasingly difficult. We needed a centralized alerting framework. What We Actually Needed Our goal wasn't simply to send more notifications. We wanted a system that could: Detect data quality issues automatically Notify engineers through channels they already use Escalate critical incidents to on-call responders Provide actionable context instead of generic failure messages Scale across multiple pipelines and monitoring use cases Most importantly, we wanted to keep the solution as close to the data platform as possible. Since our monitoring logic already lived in Snowflake, it made sense for the alerting framework to live there as well. The Architecture We Chose To address these challenges, we designed a centralized notification and incident management framework using Snowflake's native Alerting capabilities, combined with Email, Slack, and Splunk On-Call integrations. Rather than introducing another monitoring platform, we chose to build ## When SQL Meets Lambda Expressions DevFeed: [When SQL Meets Lambda Expressions]() Original publisher: [Read original article]() Author: lukaseder Published: 2025-03-27T13:04:44Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [SQL](), [Structured-data](), [DuckDB](), [clickhouse](), [databricks](), [PostgreSQL]() Tags: [apply](), [array](), [array-types](), [arrays](), [clickhouse](), [concatenation](), [databricks](), [duckdb](), [filter](), [function](), [java](), [jooq](), [kotlin](), [lambda](), [postgresql](), [scala](), [snowflake](), [sql](), [trino]() ### AI overview This article explains how SQL ARRAY types and lambda expressions are supported across several modern SQL dialects. It shows how jOOQ maps Java, Kotlin, and Scala lambda expressions to SQL expressions, including filtering arrays, and describes emulation with subqueries for dialects without lambda syntax. ### Source excerpt ARRAY types are a part of the ISO/IEC 9075 SQL standard. The standard specifies how to: But it is very unopinionated when it comes to function support. The ISO/IEC 9075-2:2023(E) 6.47 specifies concatenation of arrays, whereas the 6.48 section lists a not extremely useful TRIM_ARRAY function, exclusively (using which ... Continue reading When SQL Meets Lambda Expressions -> ## The foundations of Canva's continuous data platform with Snowpipe Streaming DevFeed: [The foundations of Canva's continuous data platform with Snowpipe Streaming]() Original publisher: [Read original article]() Author: Jack Caperon Published: 2025-01-06T00:00:01Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [Streaming](), [product analytics](), [data-platforms](), [Amazon S3](), [Serverless]() Tags: [analytics](), [continuous](), [data](), [data-platform](), [partitioning](), [performance](), [s3](), [snowflake](), [streaming]() ### AI overview Canva describes how it is building a continuous data platform with Snowpipe Streaming to support product analytics as its user base, workforce, and data volume grow. The article discusses schema management, streaming changes, cost reduction, and the limitations of its previous AWS Data Firehose-based approach. ### Source excerpt Leveraging Snowpipe Streaming to build a continuous data platform. ## Securely Managing Your Audit Logs with Teleport and Snowflake DevFeed: [Securely Managing Your Audit Logs with Teleport and Snowflake]() Original publisher: [Read original article]() Author: kenneth.dumez@goteleport.com (Kenneth DuMez) Published: 2022-10-11T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [audit](), [Logging](), [Security](), [SIEM, Security, Observability](), [event driven](), [Amazon S3](), [data]() Tags: [audit](), [aws](), [cloud](), [cybersecurity](), [data](), [data-lake](), [data-storage](), [database](), [deployment](), [event](), [event-driven](), [export](), [filter](), [format](), [how-to](), [infrastructure](), [latency](), [logging](), [logs](), [snowflake]() ### AI overview A tutorial explains how to forward Teleport audit logs to Snowflake using Fluentd as a collector, Amazon S3 as an intermediary, and Snowpipe for ingestion. It covers the security, organization, searchability, and low-latency benefits of this workflow for SIEM-related log management. ### Source excerpt How to securely manage all of your event-driven audit logs with Snowflake and Teleport. ## Snowflake Data Access with Teleport Database Access DevFeed: [Snowflake Data Access with Teleport Database Access]() Original publisher: [Read original article]() Author: kenneth.dumez@goteleport.com (Kenneth DuMez) Published: 2022-08-11T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Database](), [Security](), [audit](), [networking](), [Command-line interface]() Tags: [audit](), [cli](), [configuration](), [credentials](), [database](), [firewalls](), [logging](), [security](), [snowflake]() ### AI overview This tutorial introduces Snowflake Database Access support in Teleport 10. It explains how Teleport can provide identity-based access without shared long-lived credentials or firewalls, with audit logging, role configuration, and networking rules supporting security. ### Source excerpt Teleport has just added support for Snowflake Database Access, making it easier than ever to secure your Snowflake database. ## Connecting to Snowflake with Ruby on Rails DevFeed: [Connecting to Snowflake with Ruby on Rails]() Original publisher: [Read original article]() Author: Kevin Deisz Published: 2017-06-09T14:53:09Z Content type: tutorial Language: en Sources: [Localytics]() Topics: [Ruby on Rails](), [API](), [data-processing](), [Cloud](), [Databases](), [SQL]() Tags: [activerecord](), [adapter](), [api](), [cloud](), [data](), [data-processing](), [open-source](), [ruby-on-rails](), [snowflake](), [sql]() ### AI overview A tutorial on connecting a Ruby on Rails API to the Snowflake data warehouse using unixODBC, Snowflake's ODBC driver, and an ActiveRecord adapter. It explains ODBC, DSN configuration, installation, and debugging. ### Source excerpt At Localytics, one of the tools we use for data processing is the Snowflake data warehouse. We connect to Snowflake in a couple different ways, but our main data retrieval application is a Ruby on Rails API. To accomplish this we use a combination of unixODBC (an open-source implementation ## Faster Snowflake Queries through Clustering DevFeed: [Faster Snowflake Queries through Clustering]() Original publisher: [Read original article]() Author: Michal Klos Published: 2017-03-13T14:17:01Z Content type: article Language: en Sources: [Localytics]() Topics: [data-processing](), [Databases](), [Query (disambiguation)](), [Amazon Redshift]() Tags: [clustering](), [data-processing](), [database](), [databases](), [join](), [mpp](), [mysql](), [partitioning](), [performance](), [redshift](), [s3](), [scale](), [snowflake](), [time-series]() ### AI overview This article explains how Snowflake clustering and re-clustering can improve query performance by helping the query engine prune unneeded data. It discusses choosing cluster keys, comparisons with MPP database techniques, and the caveat that naturally ordered time-series data may not need clustering. ### Source excerpt At Localytics we have petabytes of data that needs to be served at low latencies and we use Snowflake in our mix of data processing technologies. Snowflake, like many other MPP databases, has a way of partitioning data to optimize read-time performance by allowing the query engine to prune