You may have read or heard about IDC’s recent Global DataSphere Forecast, 2021-2025, which predicts that global data creation and replication will experience a compound annual growth rate (CAGR) of 23% over the forecast period, leaping to 181 zettabytes in 2025. That’s up from 64.2 zettabytes of data in 2020 which, in turn, is a tenfold increase from the 6.5 zettabytes in 2012.

Managing any database ecosystem is difficult enough: taking backups, maintaining statistics, and doing performance tuning all tax the time of the DBA or database developer. The job is complex even without considering the work you do to manage the various schema and data drifts that can occur. Unless you operate in a vacuum or within a single person organization (and even then, schema drift can occur), drift is going to manifest naturally and as the size of the environment expands.

When drift happens within a database, it can occur at a couple of different levels. Drift refers to entities—tables, views, or even data—out of synchronization with each other. This could be a difference in schema structure, data, or even operational metadata like permissions. Often, drifts happen between two different environments like development and staging databases.

Investigating your family tree can be an interesting experience. For example, what if you discovered you were related to a famous person who won a Nobel Prize or performed a heroic act? Conversely, what if you realized you had an ancestor who was an infamous criminal? Much like examining your genealogy can be an exciting adventure, looking at the family tree of your database can prove to be just as rewarding. Databases occasionally undergo a phenomenon known as drift.

The knapsack problem: how to use limited luggage space to pack only and all the items you're most likely to need? Enter PostgreSQL! Come and find out how to use the world's best open source database to help you pack.

One of the easiest ways to increase query performance in SQL Server is to make sure it can quickly access the requested data as efficiently as possible. In SQL Server, using one or more indexes can be exactly the fix you need. In fact, indexes are so important, SQL Server can warn you when it figures out there’s an index missing that would benefit a query.

Ideally, database queries use the fewest possible resources: time, memory, bandwidth, etc. Lower resource consumption maps to better query performance. To find relevant data in a table, a database query relies on lookup operations, and a table index can help a query efficiently find the table values it needs. With an efficient, well-designed table index, a database query can find the table data it needs, avoiding the need to "scan"—or search through—all the table data.

At the moment I’m working at a tool for migrating Icinga 2 IDO history to Icinga DB . Sure, one could also run IDO and Icinga DB in parallel for one year and then switch to Icinga DB if they only care for the history of the past year. But the disadvantage is: one would have to wait one year. Nowadays (in our quickly changing world) that’s quite a long time.

The performance of your applications is a complex, multi-layered puzzle. Performance can be negatively impacted at the application layer or even by remote calls to networked services. However, the most common bottleneck for applications is the data storage layer. The most common data storage tier for applications is a relational database, whose performance can vary widely depending on query optimization.