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.

Time-varying entities may contain multiple time-varying and static attributes, making mapping them a particular challenge. Time is notorious in modeling tasks. Indeed, the temporal aspect exacerbates the complexity of the modeling task, making simple diagrams look pretty complex. The temporal dimension becomes particularly nasty when it takes part in identifying entities. The figure on the right visualizes the typical database example.

From time to time throughout my career, I have been involved in projects with dramatic releases when we built and delivered something very new and very special. The release of InfluxDB Cloud, powered by IOx (referred to as “InfluxDB IOx” for short below) absolutely meets those criteria. I want to explain my personal views of why this release is so impactful and why I am so excited to be part of it.

In different techniques, entities and relationships remain central. However, their nature and roles are reinterpreted according to the business goals. Data modeling is the process of defining and representing the data elements in a system in order to communicate connections between data points and structures. In his impactful book “Designing Data-Intensive Applications,” Martin Kleppmann describes data modeling as the most critical step in developing any information system.

Data is kind of like Newton’s first law of motion. Data is just that unless acted upon by something else. Time series data, therefore, is something you derive from data. We generally derive time series data to record historical observations about a physical or virtual system (for example, think of sensors and servers, respectively). However, not all time series data is the same. There are different use cases for time series data, and each has its own workload needs.

As part of the InfluxDB Cloud, powered by IOx launch, Paul Dix and Balaji Palani provided an InfluxDB Cloud overview and demo. In case you missed it, this blog is a quick 5 minute read summarizing the webinar. We shared the recording and the slides from the presentation for everyone to review and watch at your leisure.

Time series data can provide insight into ways to make energy production and consumption more cost-effective and efficient. The year 2022 saw the impact that world events can have on global energy markets. The most drastic fluctuations affected fossil fuels, which led to greater discussion about the practicalities of renewable energy. Fortunately, the move toward increasing reliance on renewable energy remains a consistent trend.

A look at best practices, no-code and low-code platforms you can use, common visualization types, criteria for good data visualization and more. Organizations regularly generate an overabundance of data that is essential for decision-making. Data visualizations play an important role in helping people understand complex data and observe patterns and trends over a period of time.

Recently InfluxData announced SQL support in InfluxDB Cloud, powered by IOx. Users can now use familiar SQL queries to explore and analyze their time series data. The SQL support was introduced along with the usage of Apache Arrow. Apache Arrow is an open source project used as the foundation of InfluxDB’s SQL support. Arrow provides the data representation, storage format, query processing, and network transport layers. Apache Flight SQL provides a method for interacting with Arrow via SQL.