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Bytebase is HIPAA-compliant — independent verification that our administrative, physical, and technical safeguards meet the HIPAA Security Rule for handling Protected Health Information.

HIPAA Compliance

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Database Access Control (DAC) Best Practices

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Convex vs Supabase: Which Backend Platform Should You Choose?

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Database Blue-Green Deployment: A Practical Guide

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Top 10 Data Management Tools in 2026

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How to Govern AI Agent Database Access

Postgres-only SQL editors are designed specifically for Postgres databases, providing a more focused and efficient experience for Postgres users. In this post, we are taking a look at the top Postgres-only SQL editors in 2026.

Enterprise Text-to-SQL: Context, Evaluation, and Governance

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Schema change detection and notification are essential for data pipelines. This article explains why a simple column rename can break your data pipeline and how to prevent it.

Schema Change Detection and Notification: Why a Simple Column Rename Can Break Your Data Pipeline

A comprehensive guide to understanding and analyzing Postgres query plans.

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This article explores the anatomy of Postgres permissions ERROR 42501.

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A guide to MySQL schema migration best practice.

MySQL Schema Migration Best Practice

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A guide to multi-environment deployments in databases.

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A guide to open source sensitive data discovery tools — from NLP libraries like spaCy and Microsoft Presidio to multi-source scanners like Hawk-Eye and full metadata platforms like OpenMetadata.

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Database Audit Logging: Two Layers, One Trail

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Database Compliance for HITRUST: Controls, Risks, and Best Practices

Learn why requiring a primary key is important and how the "Require Primary Key" review rule protects your production database.

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Postgres Audit Logging Guide

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Database Compliance for LGPD: Implications and Best Practices

Explain what is Postgres LATERAL JOIN and how to use it.

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Introduction to MySQL Online DDL and its three algorithms: INSTANT, INPLACE, and COPY

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List of open source postgres connection poolers

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A comprehensive guide to understanding the dilemma of singular vs. plural table naming in SQL databases.

SQL Table Naming Dilemma: Singular vs. Plural

PostgreSQL 18 features that change how application developers write and ship code.

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Snowflake Dynamic Data Masking (DDM) and Alternatives

Top open-source Postgres authentication solutions for 2025.

Postgres Case Sensitivity Explained

A comprehensive guide to understanding pg_database_owner in Postgres

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When evaluating database management tools, DataGrip and Bytebase represent two different philosophies: individual developer productivity versus team collaboration and governance.

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Database Security Lifecycle Management

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HIPAA Data Security and Retention Requirements

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Compare the different indexing options between Postgres and MySQL

Postgres vs. MySQL: Indexing Options

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Postgres vs. MySQL: JSON Support

While PostgreSQL RLS is a powerful feature, it has inherent design limitations that hinder broader adoption. In this post, we’ll examine these limitations and explore alternatives.

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Evaluate several database schema diagram tools, and help you to choose the right one for 2025

Top 4 Free, Open Source Database Schema Diagram Tools to Visualize Database Easier in 2025

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Top 5 Text-to-SQL Query Tools in 2026

Compare the DDL transaction difference between Postgres and MySQL

Postgres vs. MySQL: DDL Transaction Difference

Evaluate several TypeScript ORMs, and help you to choose the right one for 2025

Top TypeScript ORM 2025

Evaluate Prisma and TypeORM, and help you to choose the right TypeScript ORM for 2025

Prisma vs TypeORM: The Better TypeScript ORM in 2025

PostgreSQL 18 features and what they change for running production databases.

What's New in PostgreSQL 18: A DBA's Perspective

Explore alternatives to Amazon RDS, including 

RDS Alternatives in 2025

An extensive comparison between DynamoDB and Cassandra on performance, usability, operability, ecosystem and more.

DynamoDB vs. Cassandra: a Complete Comparison in 2025

An extensive comparison between DynamoDB and MongoDB on performance, usability, operability, ecosystem and more.

RDS vs. DynamoDB: a Complete Comparison in 2025

How gh-ost and pt-online-schema-change compare on architecture, cut-over, foreign keys, and runtime control — for teams choosing a MySQL online schema migration tool.

gh-ost vs pt-online-schema-change

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GitHub vs Bitbucket:a Complete Comparison in 2025

Drizzle ORM vs Prisma comparison: type safety, performance, bundle size, serverless support, migrations, and ecosystem. Find out which TypeScript ORM is right for your project in 2026.

Drizzle ORM vs Prisma: Which TypeScript ORM Should You Use in 2026?

An engineering perspective to evaluate Google AlloyDB and Cloud SQL, and help you to choose the right Google Cloud database for 2025

AlloyDB vs. Cloud SQL: engineering guide to choose the right Google Cloud database for 2025

Understanding DynamoDB Pricing (2025)

A simple comparison of AWS Aurora versus RDS pricing in 2025, helping you understand key differences and choose the right database service for your needs and budget.

AWS Aurora vs. RDS Pricing: A Detailed Comparison 2025

Infrastructure as Code security tools help organizations identify vulnerabilities and misconfigurations in their infrastructure definitions. This post explores the top open-source IaC security tools available in 2025.

Top Open Source Infrastructure as Code (IaC) Security Tools in 2025

Compare the price of the mainstream managed PostgreSQL hosting providers

PostgreSQL Hosting Options in 2025: Pricing Comparison

Complete guide to AWS Aurora pricing: instance types and costs, Aurora Serverless v2 pricing, I/O-Optimized vs Standard, reserved instance savings, and real-world cost examples. Learn how to optimize your Aurora costs.

AWS Aurora Pricing Explained: Instance Types, Serverless, and Cost Optimization (2026)

Database governance tools help organizations manage database changes, enforce policies, and maintain data quality. This post explores the top open-source database governance tools available in 2025.

Top Open Source Database Governance Tools in 2025

Workflow orchestration tools help coordinate tasks, handle errors, and monitor execution processes. In this post, we are taking a look at open source workflow orchestration options

Top Open Source Workflow Orchestration Tools in 2025

Understanding AWS RDS Pricing (2025)

Compare the price of the mainstream managed MySQL hosting providers

MySQL Hosting Options in 2025: Pricing Comparison


"Bytebase vs Metabase" is a common search, but the two tools sit in different product categories and solve different problems. Bytebase is a database DevSecOps platform for developers, DBAs, and platform teams. It handles schema changes, SQL review, access control, and data masking. Metabase is a business intelligence (BI) tool for data teams and business users. It turns SQL queries into dashboards and charts. This 2026 comparison covers where they actually overlap (web SQL editor, team permissions, self-hosting), where they differ (change workflow, BI visualization), and which one fits which role.

## Bytebase vs Metabase at a glance

|  | Metabase | Bytebase |
| --- | --- | --- |
| [Primary purpose](#primary-purpose) | Business intelligence (charts, dashboards) | Database change management + access control |
| [Primary users](#primary-purpose) | Data analysts, business users | Developers, DBAs, platform engineers |
| [Supported databases](#supported-databases) | 20+ (OLAP-leaning) | 20+ (OLTP-leaning, plus NoSQL) |
| [SQL editor](#query-experience) | ✅ (with visualization) | ✅ (SQL client feel, with schema browser) |
| [Dashboards / BI](#visualization-vs-schema-diagram) | ✅ Core product | ❌ Out of scope |
| [Schema change workflow](#change-workflow) | ❌ | ✅ Issue-based with approvals |
| [SQL Review (static + policy)](#sql-review) | ❌ | ✅ 200+ rules |
| [Data masking](#data-masking) | ❌ (row/column permissions only, Pro) | ✅ Dynamic masking with semantic types |
| [Audit log](#audit-log) | ✅ Pro/Enterprise | ✅ Pro/Enterprise |
| [Just-in-time access requests](#access-control) | ❌ | ✅ Enterprise |
| [Starting paid price](#pricing) | $100/mo flat (5 users) | $20/user/mo (Pro, cloud) |
| Open source | ✅ AGPL (fastest-growing BI) | ✅ MIT + Enterprise License |
| Self-host | ✅ | ✅ |

## Primary purpose

**Metabase** is a BI tool. Its core loop is: connect a database, ask a question (visual query builder or SQL), turn the result into a chart, pin the chart to a dashboard, share it with the team. Metabase 57 (Nov 2025) added Remote Sync for Git-tracked dashboard versioning, and Metabase 59 and 60 (Mar–Apr 2026) added AI SQL generation, Data Studio, and the Metabot AI assistant. Its audience is data analysts and business users who need to read the database, not change it.

**Bytebase** is a database DevSecOps platform. Its core loop is: register a database, developers submit change issues, SQL Review and custom approval gates run automatically, approved changes are applied and logged, and access to production data is gated by policy, masked, or granted just-in-time. Its audience is developers, DBAs, and platform teams who own the database lifecycle. For background on this category, see [what is database change management](/blog/what-is-database-change-management/).

![mb-position](/content/blog/bytebase-vs-metabase/mb-position.webp)

![bb-3-op](/content/blog/bytebase-vs-metabase/bb-3-op.webp)

In one line: Metabase reads and visualizes data; Bytebase governs and changes the database itself.

## Pricing

Pricing is one of the biggest practical differences. Metabase's paid tiers have a higher floor but bundle BI hosting. Bytebase's Pro tier is per-seat with a low entry point.

| Plan | Metabase | Bytebase |
| --- | --- | --- |
| Free / self-host Community | Open Source (self-host, unlimited users) | Free (self-host or cloud, up to 20 users, 10 instances) |
| Starter / entry paid | **Starter: $100/mo flat for 5 users**, then ~$6/user/mo monthly or $65/user/yr annual (cloud) | **Pro: $20/user/mo** (cloud only, unlimited users, SSO) |
| Mid tier | **Pro: $575/mo base**, then ~$12/user/mo monthly or $130/user/yr annual (cloud or self-host) | n/a |
| Enterprise | **Custom** (median ~$39k/yr, entry ~$20k/yr per Vendr data) | **Custom** (self-host or cloud, dynamic masking, custom approval, audit log, SCIM, SSO) |

Numbers above are from Metabase's public pricing page and Vendr's 2026 buyer data. Two things to watch:

- Metabase's $100/mo Starter cap of 5 users makes the per-user math misleading at small scale. A team of 10 on Starter is effectively $16/user/mo; on Pro, it's $57.50/user/mo before annual discounts.
- Bytebase Pro is cloud-only. Self-hosting drops you into Community (free but capped at 20 users / 10 instances) or Enterprise (custom). Most teams that self-host move straight to Enterprise.

## Supported databases

- **Metabase**: 20+ databases with a BI lean (BigQuery, Snowflake, Redshift, Athena, Druid, Presto, plus the usual OLTP engines: PostgreSQL, MySQL, SQL Server).
- **Bytebase**: 20+ databases with an OLTP lean (PostgreSQL, MySQL, Oracle, SQL Server, TiDB, Spanner, OceanBase), plus NoSQL (MongoDB, Redis) and analytics engines (ClickHouse, Snowflake, Redshift, BigQuery).

Both cover the common set. The tail is different because the products serve different workloads.

## Query experience

Both ship a web SQL editor, but the editor is doing different work in each product.

**Metabase** offers two entry points:

1. **Question builder**: a no-code interactive query for non-technical users. Drops to SQL when needed.
   ![mb-question](/content/blog/bytebase-vs-metabase/mb-question.webp)
2. **SQL query**: a raw SQL editor with schema browser. Results become charts and feed dashboards.
   ![mb-sql-query](/content/blog/bytebase-vs-metabase/mb-sql-query.webp)

**Bytebase SQL Editor** is closer to a traditional SQL client (think DBeaver or pgAdmin in the browser) with auto-completion, a schema browser, saved sheets, and a history pane. For a direct comparison against dedicated SQL clients, see [Bytebase vs DBeaver](/blog/bytebase-vs-dbeaver/) and [Bytebase vs CloudBeaver](/blog/bytebase-vs-cloudbeaver/), or the roundup at [top open source SQL clients](/blog/top-open-source-sql-clients/).

![bb-data-query](/content/blog/bytebase-vs-metabase/bb-data-query.webp)

The key difference: a Metabase query is meant to end in a chart. A Bytebase query is meant to end in verified data for a developer or an ad-hoc fix, and the editor enforces read-only or masking policies when the user opens it.

## Visualization vs schema diagram

- **Metabase**: visualization is the product. Line, bar, pie, map, pivot, funnel, and custom visualizations with dashboard embedding and AI-assisted chart suggestions in Metabase 60.
- **Bytebase**: no chart builder. The only visualization is the **Schema Diagram** for the database structure.
  ![bb-schema-diagram](/content/blog/bytebase-vs-metabase/bb-schema-diagram.webp)

If your team needs dashboards, Bytebase is not the tool. Pair it with Metabase.

## Access control

- **Metabase** organizes content into Collections with three permission levels (`Curate`, `View`, `No Access`) assignable to user groups. The Pro plan adds row- and column-level data permissions.
  ![mb-access-control](/content/blog/bytebase-vs-metabase/mb-access-control.webp)

- **Bytebase** has a two-level model: Workspace (Admin, DBA) manages database instances and global members; Project (Owner, Developer, SQL Editor User) manages day-to-day database work. Enterprise adds fine-grained controls: per-database or per-table access, per-environment policies (open in dev, restricted in prod), and request-based access where a developer files an issue to query or export a specific dataset.
  ![bb-access-control-adv](/content/blog/bytebase-vs-metabase/bb-access-control-adv.webp)

This second model matches [database access control best practices](/blog/database-access-control-best-practices/) for production: least privilege by default, access granted through approvals, every grant tied to an audit trail.

## Data masking

- **Metabase**: no dynamic masking. Row- and column-level permissions on Pro can hide fields from groups, but there's no transformation (e.g., showing `****-1234` instead of a full card number).
- **Bytebase**: dynamic data masking with semantic types. Admins tag a column (email, phone, credit card), Bytebase applies the appropriate masking algorithm, and authorized users can request unmasked access through an approval flow.
  ![bb-masking-graph](/content/blog/bytebase-vs-metabase/bb-masking-graph.webp)

Masking is an Enterprise feature. If you handle regulated data (PCI, HIPAA, GDPR), this is usually the deciding factor.

## Audit log

- **Metabase**: audit log is Pro+. Shows dashboard views, question runs, user sessions. Useful for BI usage analytics.
- **Bytebase**: audit log is Pro and Enterprise. Records every SQL Editor query, every schema change, every access grant, every approval decision. Built for compliance (SOC 2, ISO 27001) rather than product analytics. See [audit log for SOC 2 compliance](/blog/soc2-audit-logging/) for the mapping.

Both products are auditing different things, which follows from their primary purpose.

## SQL Review

**Only Bytebase has this.** SQL Review is a static-analysis layer with 200+ built-in rules plus custom rules (naming conventions, missing primary keys, dangerous statements, performance anti-patterns) that run automatically when a developer submits a change issue. It pairs with custom approval flows so that policy violations can block or gate a merge. Metabase has no equivalent because it does not change the database.

## Change workflow

- **Metabase**: not available. Metabase is a read-path tool; schema changes happen outside it.

- **Bytebase**: the change workflow is the product. A change goes through an issue that includes SQL Review, custom approval, scheduled rollout, rollback, and full history. If a developer types `ALTER TABLE` in SQL Editor, Bytebase prompts them to open an issue (or switch to Admin mode if they have the role).
  ![bb-issue](/content/blog/bytebase-vs-metabase/bb-issue-waiting.webp)

  Every change is recorded in the **Change History**. Bytebase also detects out-of-band changes and marks them as **Schema Drift**.
  ![bb-history](/content/blog/bytebase-vs-metabase/bb-history.webp)

## Open source and community

Both are open source, and they often appear together in database tooling stacks. Metabase has the longer history (launched 2015) and a larger BI user base. Bytebase (launched 2022) has grown faster on a per-month basis and sits in a different category.

![star-history](/content/blog/bytebase-vs-metabase/star-history.webp)

## Which should you choose?

This is rarely an either-or decision. A typical data stack uses both:

- **Pick Metabase** if your goal is dashboards, self-serve analytics, or embedded reporting. It is one of the cheapest ways to give business users a view into the database.
- **Pick Bytebase** if your goal is to govern database changes and production access: schema migrations with review and approval, masked queries for developers, audit trails for compliance.
- **Run both** if you have developers changing the schema and analysts reading from it. Bytebase protects the write path; Metabase powers the read path. They don't overlap.

## FAQ

**Is Bytebase a replacement for Metabase?**
No. Bytebase does not build dashboards or charts. If you need BI, you still need Metabase (or Superset, Looker, etc.). Bytebase replaces schema-change spreadsheets, ad-hoc DBA queues, and per-database access scripts: problems Metabase doesn't touch.

**Can Bytebase and Metabase run on the same database?**
Yes, and many teams do. Bytebase connects with an admin-level account to manage changes and access; Metabase connects with a read-only account for analytics. They don't conflict.

**Does Bytebase do data visualization?**
Only a Schema Diagram for database structure. There is no chart builder, no dashboard, no pivot. For data visualization, pair Bytebase with a BI tool.

**Does Metabase have schema change management or SQL review?**
No. Metabase reads the database; it does not alter it. Schema changes, SQL review, and approval workflows require a database change management tool like Bytebase, Liquibase, or Flyway.

**Which is cheaper for a 20-person team?**
Rough math with public pricing: Metabase Starter is $100/mo flat for 5 users + $65/user/yr for the other 15 on annual = ~$181/mo. Bytebase Pro is $20/user/mo × 20 = $400/mo. Bytebase Community (self-hosted) is free for up to 20 users. The answer depends on whether you need BI, self-hosting, and which features you actually use.

**Are both open source?**
Yes. Metabase is AGPL-licensed; Bytebase uses a mix of licenses with a commercial Enterprise edition. Self-hosting is supported by both.


Bytebase vs Metabase in 2026: a side-by-side comparison of database tooling for developer teams and data teams, covering pricing, features, SQL Review, change workflow, and BI.

Bytebase vs. Metabase: a side-by-side comparison for developer and data teams


The `Modern Data Stack` is a collection of cloud-native technologies and tools designed to streamline data collection, storage, management, analysis, and sharing. It embodies a shift from traditional, monolithic data management systems to more flexible, scalable, and user-friendly solutions. The term `Modern Data Stack` does not have a single inventor or originator credited with its coining. Instead, it emerged organically over time as the data technology landscape evolved and new cloud-native tools and services were developed.

Snowflake plays the pivotal role in transitioning the industry into the `Modern Data Stack` era. Below we introduce tools working
with Snowflake and coin them together as the `Modern Snowflake Stack`.

## Data Ingestion - Fivetran

![_](/content/blog/modern-snowflake-stack/fivetran.webp)

[Fivetran](https://www.fivetran.com/) is a cloud-based data integration service that automates the extraction, loading, and transformation (ELT) of data from various sources into a data warehouse or data lake. Designed to simplify data integration and ensure data accuracy, Fivetran supports a wide range of data sources, including SaaS applications, databases, event logs, file storage, and more.

Fivetran offers a large library of pre-built connectors for popular data sources and destinations, allowing quick and easy setup of data pipelines without the need for custom coding. e.g. The [Salesforce to Snowflake](https://www.fivetran.com/connector-warehouse/salesforce-snowflake-data-cloud) is Fivetran's flagship connector that contributes a significant portion of its revenue.

## Data Transformation - DBT

![_](/content/blog/modern-snowflake-stack/dbt.webp)

[dbt (data build tool)](https://www.getdbt.com/) is an open-source command-line tool that enables data analysts and engineers to transform data in their analytics warehouses by writing select SQL queries and managing them as code. It is designed to facilitate the "transform" step of the ELT (Extract, Load, Transform) process, where raw data is transformed into a format suitable for analysis after being loaded into a data warehouse. dbt does this by treating transformations as models, which can be version-controlled, tested, and deployed following software engineering best practices.

Snowflake's architecture allows data teams to perform transformations on large datasets efficiently and cost-effectively, without worrying about the underlying infrastructure. When dbt runs transformations in Snowflake, it leverages Snowflake's compute power to execute SQL commands, transforming raw data into structured, analysis-ready information.

## Data Security - Immuta

![_](/content/blog/modern-snowflake-stack/immuta.webp)

[Immuta](https://www.immuta.com/) is a data security platform designed to facilitate the secure access and control of sensitive data across cloud, hybrid, and on-premises environments. It focuses on automating data access controls, privacy compliance, and security policies, enabling organizations to manage and share their data more effectively and in compliance with various regulatory requirements. Immuta's platform is particularly useful for organizations that deal with large volumes of sensitive data and need to ensure that their data handling practices comply with laws such as GDPR, CCPA, HIPAA, and others.

Snowflake has already provided built-in access control and dynamic data masking, and Immuta's platform augments the Snowflake core and allows teams to manage the access control and masking policies at scale.

## Data Catalog - Atlan

![_](/content/blog/modern-snowflake-stack/atlan.webp)

[Atlan](https://atlan.com/) is a modern data catalog and collaborative workspace designed to empower data teams by centralizing data discovery, governance, and metadata management. Atlan's approach integrates the functionality of a traditional data catalog with collaboration and governance features, making it a comprehensive platform for data teams, including data analysts, data scientists, data engineers, and business users.

Atlan is the first data catalog to be validated as a Snowflake Ready Technology Partner.

![_](/content/blog/modern-snowflake-stack/atlan-snowflake.webp)

Similar to Immuta's approach, Atlan build's the data solution on the top of Snowflake's built-in features such as Column-level security, row-level access, object tag-based masking, data classification, which allows team to manage data lineage, data discovery at scale.

## Data Monitoring - Bigeye

![_](/content/blog/modern-snowflake-stack/bigeye.webp)

[Bigeye](https://www.bigeye.com/) is a data monitoring platform designed to help data teams ensure the health and reliability of their data. By providing tools to monitor data quality, set alerts for anomalies, and track data lineage, Bigeye aims to automate the process of identifying and addressing issues in data systems before they impact downstream analyses or operational processes. It's part of a broader category of data observability and monitoring solutions that have emerged to tackle the challenges associated with managing data at scale in modern data ecosystems.

Bigeye and Atlan do have some overlap in their capabilities, particularly around data cataloging and governance. However, their core offerings and primary use cases differ significantly, catering to different needs within the data management and operations landscape.

Bigeye is primarily focused on data observability and quality monitoring. Its main features include automated data monitoring, anomaly detection, and alerts to ensure the reliability of data in operational systems and analytics. Atlan, on the other hand, positions itself as a modern data workspace, emphasizing data discovery, collaboration, and governance. Its features are designed to make data easily accessible and usable across teams, with strong support for metadata management, data cataloging, and collaboration tools.

With its data health agent, Bigeye can surface data quality and pipeline health alerts directly in Atlan.

## Data Activation - Census / Hightouch

Data Activation aka reverse-ETL, which focuses on operationalizing data by syncing it from data warehouses to various SaaS applications, operational systems, and customer relationship management (CRM) platforms. [Census](https://www.getcensus.com/) and [Hightouch](https://hightouch.com/) are two leading solutions, they are neck-to-neck and provide similar feature sets.

Both products claim to be superior than the other. The competition is real and we think it's better to leave the judgement to the audience:

- [Census vs Hightouch (Census view)](https://www.getcensus.com/compare/census-vs-hightouch)
- [Hightouch vs Census (Hightouch view)](https://hightouch.com/blog/hightouch-vs-census)

## Customer Data Platform (CDP) - Segment

The main purpose of building the entire data stack is to unlock insights from the customer data. Segment is a customer data platform (CDP) that helps organizations collect, clean, and control their customer data. Segment does overlap with some of the aforementioned solutions.

![_](/content/blog/modern-snowflake-stack/segment.webp)

Segment overlaps with Fivetran on data ingestion. Fivetran consolidates data from diverse sources into a data warehouse, while Segment emphasizes the collection and integration of customer data across multiple touchpoints to enhance marketing strategies and customer understanding, focusing on real-time data routing and customer profile unification. Actually, Segment is often a data source in Fivetran.

Segment overlaps Census / Hightouch on data activation / reverse-ETL. This is a natural move for Segment to close the loop for
customer data. Since the purpose for reverse-ETL is to deliver more personalized and timely experiences to their customers across various channels.

## CI/CD - Bytebase

After all, Snowflake is a database system. And for database, one of pain points is to manage schema migrations. All aforementioned solutions belong to the DataOps domain, while the schema migration falls into the DevOps and CI/CD category. Snowflake itself open sourced [schemachange](https://github.com/Snowflake-Labs/schemachange) for database change management. It's a python-based CLI
carrying the spirit of classic java schema migration tool [flyway](https://github.com/flyway/flyway).

[Bytebase](/) is another CI/CD and change management solution providing review workflow and version control for Snowflake schema migration and ad-hoc changes. Different from schemachange, Bytebase provides a GUI collaborative workspace like what GitHub does for code management.

![_](/content/blog/modern-snowflake-stack/snowflake-issue.webp)

It also provides native GitOps integration with all mainstream VCSs to manage the change scripts in the repository.

![_](/content/blog/modern-snowflake-stack/vscode-create-table.webp)

FWIW, Snowflake also [added Git integration](https://www.snowflake.com/blog/snowflake-expands-developer-programmability-snowpark-container-services/) recently.

![_](/content/blog/modern-snowflake-stack/snowflake-git.webp)

## What are we missing?

The acute reader might have already noticed that we missed an elephant in the room, the BI tool. We don't include BI because
although there are quite a few options like Tableau, Mode, Looker, There is no outstanding one specifically built for Snowflake.
Maybe the industry is just waiting for Snowflake to announce its own BI solution, not a far sight from Snowsight anyway.

## Summary

![_](/content/blog/modern-snowflake-stack/google-trend.webp)

`Modern Data Stack` starts to pick up in 2022, this article introduces the similar `Modern Snowflake Stack`.
Snowflake is still the pinnacle of the modern data stack ([though Databricks likely disagrees](https://www.databricks.com/databricks-vs-snowflake)). By narrowing our focus on the Snowflake ecosystem, we can learn the state-of-the-art of the data landscape
and adopt the best practice.


The "Modern Snowflake Stack" is inspired from the phrase "Modern Data Stack", refers to a set of technologies and tools built around Snowflake, a cloud-based data warehousing solution, to enable efficient data storage, processing, and analytics.

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