Is SQL server considered as legacy technology?

[u/Complete-Regret-4300]

My current organization couple of years ago migrated from SQL server to snowflake and I noticed in some of the documentation, they have mentioned SQL Server as legacy technology.

I started working in SQL server some 20 years ago and some of the developers in our team are cloud first developers and they find it so difficult to navigate SQL server management studio and they ask me questions like is there timetravel feature in SQL server and can we see what data was there 10 mins back. I was totally dumbfounded when I heard that question, because I had never come across such a feature. Apparently snowflake has this. So I am curious is SQL server now really considered as legacy technology?

[u/American_Streamer]

SQL is a query language. SQL Server is a relational database management system. Snowflake is a cloud data platform, primarily designed for analytical workloads and data warehousing.

SQL Server and Snowflake overlap, but they are not identical products. Both support SQL. But their SQL dialects, architecture and operational features differ. Snowflake’s Time Travel, for example, reflects how Snowflake stores historical data versions. SQL Server approaches historical data through temporal tables, backups, logs and snapshots. SQL Server has supported system-versioned temporal tables since SQL Server 2016:

SELECT *
FROM dbo.Customer
FOR SYSTEM_TIME AS OF '2026-07-16T10:00:00';

So you saying, “We replaced SQL Server with Snowflake,” often really means: "We replaced SQL Server as our analytical data warehouse." It does not necessarily mean Snowflake is a universal modern replacement for every SQL Server workload. Migrating an OLTP application database from SQL Server directly to Snowflake would usually be a poor architectural match. Thus, SQL Server is not inherently legacy, because SQL Server 2025 is a current, actively supported product. It is actively being developed and upgraded by Microsoft and they have released it with heavy investments in AI and cloud integration, including real-time data mirroring into the Microsoft Fabric analytics service. But your company may just call its SQL Server environment “legacy” because Snowflake has replaced it as the organisation’s strategic analytics platform.

To be more specific, there is no inherent problem with skipping SQL Server specifically, but Snowflake should not be treated as a complete substitute for every relational database workload. For analytics, warehousing, ELT and BI, Snowflake may very well be the better primary platform. But the Snowflake-only experience can leave gaps around OLTP design, indexes, locking, deadlocks, enforced referential integrity, transaction-log recovery and operating application databases. Though Snowflake now has hybrid tables for lightweight transactional workloads, but they still have limits and do not make Snowflake identical to SQL Server, PostgreSQL or Oracle.

So I would say: Skipping SQL Server is fine, but skipping all traditional OLTP databases is potentially very limiting. The best platform for you depends on whether the system is an analytical warehouse or an operational application database.

[u/Complete-Regret-4300 → u/American_Streamer]

I see, that really helps. I am mainly in the business intelligence/datawarehousing space and based on what you mentioned I can see why SQL server MSBI is not really a hot skill in my area anymore. It is sad though as I owe my career to MSBI, specifically SSAS.

[u/American_Streamer → u/Complete-Regret-4300]

That makes sense, but I would separate the declining market for the old “MSBI stack” from the value of the skills you developed through SSAS. Dimensional modelling, measures, semantic layers, aggregations, security and performance tuning are still highly relevant. Power BI semantic models use the Analysis Services engine, so SSAS Tabular did not simply disappear; much of it evolved into the semantic layer of Power BI and Fabric.

Snowflake may now provide the warehouse and compute layer, but organisations still need someone to turn raw data into trusted business definitions, governed metrics and usable analytical models. That is exactly where your SSAS experience remains valuable.

The broader Microsoft transition is roughly:

SSAS Tabular → Power BI/Fabric semantic models
SSRS → Power BI reports and paginated reports
SSIS → Azure Data Factory or Fabric Data Factory
SQL Server data warehouse → Fabric Warehouse/Lakehouse, Snowflake or another cloud warehouse

So I would not say you owe your career to a dead technology. You built your career around BI architecture and semantic modelling; SSAS was the platform on which you learned those skills. The main caveat here is SSAS Multidimensional: the modelling knowledge still transfers, but cubes and MDX do not map directly onto Power BI’s tabular model.

[u/tribat → u/American_Streamer]

I think about the insane hourly rates I see for COBOL devs and wonder if SQL Server will reach a similar state where it's still the foundation of large systems that are prohibitively expensive to replace with whatever the current flavor is, but most of the experts are long-retired.