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Getting started with Data Observability Guide

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3 reasons why Snowflake and Metaplane are better together

Today, Metaplane supports dozens of integrations and data sources (and counting!), but we’re especially proud to be a Snowflake Premier Partner, which means we have over 75 joint customers ranging from scaleups like Ramp and Klaviyo to established enterprises like Bose and Sotheby's. Want to know why Snowflake and Metaplane work so well together?

May 12, 2024

Co-founder / Engineering

May 12, 2024
3 reasons why Snowflake and Metaplane are better together

We’ll get into that. But first, some definitions:  

  • Snowflake is a single, global platform that powers the Data Cloud. It’s uniquely designed to connect businesses globally, across any type or scale of data and many different workloads, and unlock seamless data collaboration.
  • Metaplane is the leading data observability platform for data teams. Using machine learning-based data quality monitoring, it ensures that all companies can trust the data that powers their business.

1. Wider coverage to catch "unknown unknowns"

Snowflake provides tons of metadata (i.e. the number of rows, the size of tables, and timestamps indicating the last time tables were updated), all of which is crucial for maintaining and monitoring data health. Especially in large-scale data environments where changes occur faster than any data team can keep up with, monitoring this metadata allows you to detect issues earlier and quickly resolve them before they cause larger issues downstream.

Tracking row count and freshness, in particular, are crucial for early issue detection and resolution:

  • Monitoring row counts alerts you of any sudden row changes that might indicate missing data (decreases) or duplication (increases).
  • Monitoring freshness ensures that your data is current and all your processes/operations are running as scheduled. 

But at scale, monitoring these attributes manually is nearly impossible. You’d have to hard-code upper and lower limits, then merge in a PR every time the pattern of the underlying data changes.

For full coverage, you need an automated data observability solution. And with unmatched machine learning anomaly detection, Metaplane is the best (and highest-rated) data observability platform for the job. Within a few days, to account for seasonal patterns, you’ll get alerts for any unexpected events, so you can analyze and manage bottlenecks, unusual changes in data volumes, resource allocation, etc. 

One of the beauties of Snowflake is their consumption-based model, but as more people in your organization use Snowflake for more of their workloads, you need added visibility and predictability into that credit consumption. With Metaplane’s Snowflake Spend Analysis, you gain that added sense of control. You can immediately see your daily total credit spend, a 30-day spend aggregation, and your daily spend broken down by warehouse and user.

Example of Snowflake spend split out by Warehouse

It alerts you of any unexpected behavior, so your team can capture upstream issues, confirm proper Snowflake configurations, set up regular optimization efforts, and improve the predictability of your whole data stack. With Metaplane and Snowflake together, you get the full coverage you need (with a much lower price tag).

2. More Snowflake integrations than any other data observability platform

Snowflake is constantly releasing incredible new features. Case in point: they’ve released 7 features/enhancements, not including their enterprise-grade LLM called Snowflake Arctic, in just the first week of May!

Metaplane is the only data observability platform that integrates with the majority of Snowflake’s features. We support core data engineering features, from Tasks to Streams to Secure Data Shares to Snowpipes. We also integrate with data app development features like Snowpark and Native Apps through Event Tables. Here’s how:

  • Lineage: See Snowflake features with column-level end-to-end lineage visualization. For example, when secured data shares are populating a source table, or Snowpipe is reading from S3.
  • Root cause analysis: Use Metaplane’s column-level lineage to see what’s happening upstream when an incident occurs. For example, did someone make a change that impacts a table further downstream?
  • Impact analysis: Think of this as the reverse of root cause. When an incident happens, what Native app is impacted? What stored procedures depend on which table?

Though not all 3 of these features exist for each Snowflake feature today, we’re continuously working toward fully integrating with all Snowflake features. This broad compatibility means you can take full advantage of Snowflake's progressive capabilities (which gives you a competitive advantage in the market). What good is a data observability platform if it can’t integrate with the key parts of your data stack, anyway?

Metaplane integrates with key parts of your data stack
to alert on data quality issues without manual input.

Snowflake has made it easier than ever for data teams to serve use cases like business intelligence, machine learning, and operationalization. But with data lying at the core of these use cases, the stakes for poor data quality are raised higher than ever.

Using Metaplane together with Snowflake, data teams can power critical use cases with confidence. Not only will they be alerted to data anomalies, but they will also have the context to diagnose downstream impact and analyze upstream root causes.

On top of that, we also help users resolve issues more quickly by finding the origin of a problem with column-level lineage maps, and avoid future issues by integrating directly into your CI/CD workflows. One small Snowflake integration, one giant leap for your data.

3. Scalability across workloads

The beauty of Snowflake is it’s used across so many workloads. Snowflake's virtual warehouse architecture allows it to scale horizontally. Each virtual warehouse performs tasks—i.e. loading data, running queries, or executing complex analytics—independently. These can be leveraged at any time for SQL execution and DML and then turned off when they aren’t needed (as opposed to traditional on-premise data warehouses, which have tightly coupled data storage and compute).

Essentially, it can increase its computing power by adding more servers or resources in parallel. This horizontal scalability allows Snowflake to handle sudden data volume or user demand increases seamlessly, so different people using Snowflake for different use cases (marketing, sales, etc) don’t cause any drop in performance.

Scalability is a huge competitive advantage for Snowflake, but it’s tricky to get any meaningful usage and cost metrics across these different workloads. Fortunately, Metaplane optimized for this with the Data Insights feature. Our Data Insights makes it easy to understand where your team can make the highest impact when it comes time to optimize your query performance and cost by:

  • Giving an overview of cost over time to help you understand at a glance whether optimizations are needed‍
Data Insights example cost over time
  • Highlighting where your costs come from which can help make your TCO more obvious
Data Insights example cost breakdown by user
  • Highlighting your least performant queries based on query runtime
Data Insights example most expensive queries

We’re just getting started with our Data Insights dashboard and have many near-term updates planned, including an easy-to-use roll-up of what tables and views aren’t being used in your warehouse and may be good candidates for cleanup.

But in the meantime, if you want to see for yourself why Metaplane and Snowflake are a perfect match, try Metaplane for free! Just connect your Snowflake instance and we'll immediately generate column-level lineage from source to BI in a unified visualization map.

We’re hard at work helping you improve trust in your data in less time than ever. We promise to send a maximum of 1 update email per week.

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