Honeycomb is known for its incredibly fast performance: you can sift through billions of rows, comparing high-cardinality data across thousands of fields, and get fast answers to your queries. And none of that is possible without our purpose-built distributed column store. This post is an introduction to what a distributed column store is, how it functions, and why a distributed column store is a fundamental requirement for achieving observability.

In the era of data abundance, there exists a significant need for database systems that can effectively manage large quantities of data. For certain types of applications, an oft-considered option is Apache Cassandra. Like any other piece of software, however, Cassandra has issues that could potentially impact performance. When this happens, it’s critical to know where to look and what to look for in the effort to quickly restore service to an acceptable level.

You can learn a lot about an organization by looking at their data. For example, if I see “LoanToValue” (LTV) or “CollateralValue,” I could surmise they deal with financial data—specifically loans—in some fashion. Developers and database administrators with domain knowledge about the intrinsic meaning of the data are precious friends. These individuals understand the meaning behind the data and how it works within the applications of the organization.

Every database administrator (DBA) is—first and foremost—human. And everyone makes mistakes. It’s not the absence of mistakes but rather how you prepare for those mistakes that makes you a great DBA. Luckily, there are many ways to prepare for those mishaps, whether the errors are made by you or someone else on your team. One commonly made mistake is to drop an object in a database or accidentally delete data.

A database is a collection of organized information for easy access and management. Computer databases generally consist of aggregated data or files that contain information about customers, transactions, or inventories. Regular monitoring of the database’s performance is necessary to ensure that it is running properly and to detect issues as they arise. Here is a short database monitoring guide that can assist you in choosing the right tools.

If you’ve worked in IT in recent years, you’re no doubt familiar with the term “DevOps.” Accelerating the pace of development and faster delivery of new features is the goal. DevOps involves integrating the development life cycle with Agile methodology. DevOps practices applied to database operations is now commonly referred to as DataOps.

With a multitude of digital options available in almost every industry, it’s become increasingly critical that applications and services provide a positive user experience. Doing so requires a high level of availability, made possible (in part) by efficiently identifying and resolving issues with the system, when they occur. To achieve this, monitoring all critical components of an application and its infrastructure is a necessity.