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.
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.
Grafana has been a staple visualization tool used alongside InfluxDB since its inception. With the release of InfluxDB Cloud powered by IOx, there is now a new way to integrate InfluxDB and Grafana: Flight SQL. Two of our engineers, Brett and Helen, have been working hard to create a new Grafana plugin called Flight SQL. This open-source plugin allows users to perform SQL queries directly against InfluxDB IOx and other storage engines compatible with Apache DataFusion.
Telecommunication organizations need to ensure they have the necessary resources and technology to maintain service uptime SLAs. Increased regulations and emerging technologies forced telecommunications companies to evolve quickly in recent years. These organizations’ engineers and site reliability engineering (SRE) teams must use technology to improve performance, reliability and service uptime.
Today is an important milestone for InfluxData. I am thrilled to share that we closed our $51 million Series E round, and a $30 million debt facility, raising $81 million in capital. The Series E round was led by our new investors Princeville Capital and Citi Ventures, with participation from our existing investors Battery Ventures, Mayfield Fund, Sapphire Ventures, Trinity Ventures, Norwest Venture Partners, Sorenson Capital, and Harmony Partners.
