Running a full-featured Microsoft SQL Server on a Mac used to be a headache, but as of 2026, it is smoother than ever. Thanks to improvements in Docker Desktop and macOS’s Rosetta 2 translation, you can now run the enterprise-grade engine (including the new SQL Server 2025 Preview) directly on your M1, M2, M3, or M4 Mac.
This guide will walk you through setting up a modern SQL Server environment from scratch.
SQL Server’s Query Store captures all executed queries (or a subset based on your capture mode) along with their execution statistics. Viewing the complete list of captured queries helps you understand what’s been tracked and provides a starting point for performance analysis.
In data warehousing, a slowly changing dimension (SCD) is a dimension table where the attributes change over time, but not very frequently. The term “slowly changing” refers to the fact that these changes happen occasionally (perhaps days, weeks, or months apart) rather than constantly like transactional data.
The challenge is figuring out how to handle these changes while maintaining accurate historical analysis. When a customer moves to a new state or a product gets recategorized, you need a strategy that preserves the integrity of your historical data.
Polyglot persistence is an architectural approach where an application uses multiple different database technologies, each chosen for its specific strengths and matched to particular data storage needs. Instead of forcing all your data into one database system, you use the right database for each job. For example, a relational database for transactional data, a document store for flexible content, a cache for session data, and a graph database for relationships.
When you’re analyzing how events unfold over time in a SQL database, one of the biggest challenges can be efficiently comparing what’s happening now to what comes next. Each event typically appears as its own row with a timestamp, but meaningful insight often comes from understanding how those timestamps relate to one another. Fortunately SQL Server provides some useful tools for this kind of sequential analysis.
Rather than relying on bulky self-joins or multi-step logic, SQL window functions offer a streamlined way to track these transitions. For example, by pairing the LEAD() function with DATEDIFF(), you can instantly measure the gap between consecutive events and surface insights that would otherwise require far more complex queries.
When you’ve forced an execution plan in SQL Server’s query store and later want to allow the optimizer to choose plans freely again, you’ll need to unforce the plan. You might want to do this because you’ve fixed the underlying issue or the forced plan is no longer optimal. Whatever the reason, the solution is straightforward and easy.
A CRDT (Conflict-Free Replicated Data Type) is a special type of data structure designed for distributed systems that guarantees multiple replicas of data will eventually converge to the same state without requiring coordination between nodes. Even when different users simultaneously modify the same data in different locations, CRDTs automatically resolve conflicts in a mathematically consistent way that ensures all replicas eventually agree.
The main insight behind CRDTs is that certain operations can be designed to be commutative, meaning the order in which you apply them doesn’t matter. If operation A followed by operation B produces the same result as operation B followed by operation A, you can apply updates in any order and still reach the same final state. This property eliminates the need for complex conflict resolution logic.
SQL Server’s window functions open up some creative possibilities when you need to work with dates. One interesting pattern involves pairing DATEADD() with ROW_NUMBER() to automatically generate sequential dates based on your query results.
This technique gives you a flexible way to calculate dates dynamically without hardcoding values or maintaining separate date tables. This can be useful for doing things like building a scheduling system, creating test data with realistic timestamps, or just spacing events across a timeline.
Eventual consistency is a consistency model used in distributed databases where data updates don’t immediately propagate to all copies, but given enough time without new updates, all copies will eventually become identical. When you write data to one location in the system, other locations might temporarily see old data, but they’ll all catch up eventually. This usually happens within milliseconds or seconds (although it can be longer during network issues or node failures).
This approach contrasts with strong consistency, where every read is guaranteed to return the most recent write immediately. With eventual consistency, the system prioritizes availability and performance over immediate accuracy. You’re accepting that different parts of your database might temporarily disagree about the current state of the data in exchange for faster operations and better fault tolerance.
If you’ve ever wondered why your database queries sometimes seem to wait around doing nothing, or why two users can’t update the same record at the exact same moment, you’re dealing with locks. In SQL Server, locks are the fundamental mechanism that keeps your data consistent and prevents the chaos that would ensue if everyone could modify everything simultaneously.