It’s been less than 4 months since we released Cribl Search, the first federated query engine focused on observability and security data. The reception has been tremendous. Customers, partners, prospects, and even our internal teams were overjoyed by the initial offering but have been anxiously awaiting the promises of the next release. The wait is over!
SQL databases have several functions that reduce the complexity of working with date and time. Using these functions and a date and time type column, you can depend on SQL for the logic to write and read data with date and time. In this post, you’ll learn how to use the SQL date and time functions to get the current date and time.
For a very long time, businesses in various industries have been using smart sensors. However, with the development of the Internet of Things (IoT), their significance has increased. Sensors represent the beginning of a data collection chain that, when processed by IoT platforms, generates essential insights for assertive decision-making and even for developing new business ideas. Generally, a smart sensor has three main components: This technology is already used in many countries.
In the world of databases, cardinality refers to the number of unique sets of data stored in a database. If we drill down a little further, we can think of cardinality as the total number of unique values possible within a table column or database equivalent. When thinking about time series data, we can ask some specific questions about cardinality. What does cardinality look like in practice? When does cardinality become a problem? How do we prevent cardinality issues?
Deduplication is an effective alternative to transactions for eventually consistent use cases of a distributed database. Here’s why. Building a distributed database is complicated and needs to consider many factors. Previously, I discussed two important techniques, sharding and partitioning, for gaining greater throughput and performance from databases.
Fun fact: Observability goes all the way back to the 1960s, coined by scientist Rudolf Kálmán as a way to measure a system through its output. Now, over six decades later, observability has fragmented into several specialized segments — from application observability, to security observability, and everything in between. The two segments driving the most confusion are data observability and observability data.
