Database sharding is a technique for splitting a large database into smaller, more manageable pieces called shards. Each shard contains a subset of the total data and operates as an independent database. Together, all the shards hold the complete dataset, but queries are distributed across them to improve performance and scalability.
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What is a Cloud Database?
A cloud database is a database that runs on cloud computing platforms rather than on local servers or personal computers. Instead of installing and managing database software on your own hardware, you access the database over the internet through a cloud provider like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform.
Basic Database Terms for Beginners
If you’re just getting into databases, the terminology can sometimes feel overwhelming. Between acronyms like SQL and CRUD, technical jargon like normalization and schema, and concepts that sound similar but mean different things, it’s easy to get lost before you even start.
Let’s break down the essential concepts you need to know to actually understand what people are talking about.
What is a Dimension Table?
In data warehousing and analytics systems, dimension tables provide context for the measurements stored in fact tables. While fact tables contain the numbers you want to analyze, dimension tables tell you what those numbers mean. This could be things like who was involved, what product it was, when it happened, where it occurred, etc.
Basically, dimension tables are the “descriptive” side of dimensional modeling, containing the attributes and characteristics that let you filter, group, and label your data in meaningful ways.
What is a Fact Table?
In data warehousing and analytics databases, a fact table is where you store the actual measurements and metrics of your business. It’s the table that contains the numbers you want to analyze. This could include things like sales amounts, quantities, counts, durations, or any other quantifiable data points.
Fact tables are the foundation of dimensional modeling, the approach most commonly used for organizing data in data warehouses and business intelligence systems.
What is a Star Schema?
If you’ve ever worked with data warehouses or business intelligence systems, you’ve probably encountered star schemas. Perhaps even without realizing it. Star schemas are one of the most common ways to organize data for analytics and reporting.
Star schemas look exactly like their name suggests. They consist of a central table surrounded by related tables, forming a star shape.
Star schemas are designed specifically for querying and analysis rather than transactional operations. They make it easy to slice and dice data in ways that business users actually care about.
What is Denormalization?
If you’ve spent any time working with relational databases, you’re probably well aware of the concept of normalization. This is the process of organizing data in a way that reduces redundancy and maintains consistency. It’s basically SQL Database Design 101. And for good reason.
But sometimes the “right” way to design a database isn’t necessarily the most practical way to run it. Sometimes we need to tweak the thing until we get it performing just right. And sometimes this means deviating from the norm and using a different approach. Denormalization is an example of this.
What is a Savepoint in SQL?
When working with databases, there’s a good chance you’ve had to deal with transactions. Transactions are those “all or nothing” blocks of work that make sure your data stays consistent. But what happens if you’re halfway through a transaction and realize that only part of it needs to be undone, not the whole thing? That’s where savepoints can help.
In SQL, a savepoint is basically a checkpoint you can set inside a transaction. It lets you roll back to that specific point if something goes wrong, without undoing everything that came before it. If something gets messed up, you can load your last save instead of starting again from scratch.
What is a MERGE Statement in SQL?
The MERGE statement is SQL’s convenient tool for synchronizing data between two tables. It lets you perform INSERT, UPDATE, and DELETE operations in a single statement based on whether matching records exist. Instead of writing separate logic to check if a record exists and then deciding what to do with it, MERGE handles all of that in one go.
Most major database systems support MERGE, including SQL Server, Oracle, and DB2. PostgreSQL added native MERGE support in version 15, but if you’re on an older version, you can use INSERT … ON CONFLICT as an alternative. MySQL doesn’t have MERGE but offers INSERT … ON DUPLICATE KEY UPDATE for similar functionality.
What is Parameter Sniffing in SQL Server?
Parameter sniffing is a feature in SQL Server where the query optimizer examines (or “sniffs”) the parameter values the first time a stored procedure or parameterized query executes. It uses these specific values to create an execution plan optimized for those particular parameters. The plan is then cached and reused for subsequent executions, even when different parameter values are passed in.
This behavior can be efficient if the initial parameters are representative of typical queries, but it can also cause performance problems if later calls use very different parameter values that make the cached plan inefficient.