February 2023 Summaries

Time series data is crucial for companies seeking to optimize energy production and consumption, providing insights into overall energy generation levels, device performance, and reasons for service degradation. As the world shifts towards renewable energy sources, technology plays a vital role in tracking new system requirements and output. Time series data helps operators adjust equipment regularly to accommodate environmental changes, while also addressing challenges such as device location, data collection, and rudimentary tools. Open source technologies like InfluxDB enable innovation and scalability, driving business decisions and reducing environmental impact. Companies like Bboxx, EnerKey, and Graphite Energy are leveraging time series data to drive sustainability and energy management, creating a more sustainable energy future for everyone.

Data visualization is the process of translating complex data into easily understandable visuals. Organizations generate large amounts of data that require effective communication to inform decisions. Data visualizations aim to communicate observations effectively, making them an essential skill for data professionals. Best practices include setting clear goals and objectives, choosing the right tools and visualization types, using succinct labels and titles, selecting appropriate colors, avoiding clutter, and ensuring clean and up-to-date data. Good data visualizations show complex connections in a clear and concise manner, are easy to understand, and effectively communicate information without unnecessary distractions. When judging a good data visualization, consider its use of color, readability, design elements, and the impact on the target audience. By following these best practices, organizations can create effective data visualizations that support informed decision-making.

InfluxData has announced SQL support in its InfluxDB Cloud, powered by IOx, allowing users to explore and analyze their time series data using familiar SQL queries. The SQL support is built on top of Apache Arrow, a project that provides the data representation, storage format, query processing, and network transport layers. A Python library called Flight SQL provides a seamless user experience for interacting with InfluxDB's SQL support. Users can connect to InfluxDB Cloud using a token and bucket name, execute queries using the `execute()` function, and handle query results using a `FlightStreamReader` object that allows streaming of data without filling up memory. The library also supports conversion of Arrow tables to Pandas DataFrames or Polars DataFrames for further analysis and manipulation.

Telegraf 1.25.0, 1.25.1, and 1.25.2 have been released with new features including Secret Store for storing credentials, new inputs such as Azure Monitor, Google Cloud Storage, Intel DLB, Libvirt, NetFlow, and OPC UA Event Subscriptions. The binaries are available on the Telegraf downloads page and users can report issues or ask questions on the InfluxDB Community Slack or GitHub Repo. A Call for testing has been issued to include users in testing bug fixes and features for plugins and systems they use.

The InfluxData team has released a new plugin for Grafana called Flight SQL, which allows users to perform SQL queries directly against InfluxDB IOx and other storage engines compatible with Apache DataFusion. This plugin provides a quick start guide for installation, configuration, and usage, including examples of how to configure the plugin, deploy Telegraf and Grafana using Docker-compose, and create custom SQL functions to handle complex time series calculations. The plugin also introduces new global variables that can be used to make queries dynamic and streamlined, such as $__timeRange(time) and $__dateBin(time). With Flight SQL, users can now leverage the power of SQL on top of InfluxDB IOx, enabling more advanced data analysis and visualization capabilities.

WideOpenWest, a telecommunications company founded in 1996, faced challenges in modernizing its network due to legacy systems and high costs. The company replaced its time series database with InfluxDB, which provides better observability and alerting capabilities. With InfluxDB, WideOpenWest can collect data from various sources, including cable modems, virtual machines, and containers, and stream it to Kafka for control and output management. The team uses Telegraf for data collection and Grafana for visualizations, enabling real-time analytics and alerts. By adopting InfluxDB, WideOpenWest aims to implement an Infrastructure-as-Code system, automate code promotions, and streamline monitoring across the organization.

To get, convert, and format JavaScript dates from timestamps, you can use various methods such as obtaining a timestamp using the Date object or using the Date.now() method, which is a built-in function that returns the number of milliseconds since January 1, 1970, at midnight UTC. Once you have a timestamp, you can create a new Date object and pass it to various Date methods to get individual date components. Additionally, you can use the Intl.DateTimeFormat object to format dates according to the user's locale. You can also format dates using the toLocaleDateString() method or by using a library like Moment.js, which provides tools to manipulate, validate, and format dates in multiple regions and time zones. By understanding how to get, convert, and format dates in JavaScript, you can build more robust and user-friendly applications that easily handle dates.

DATEDIFF is a SQL Server function that calculates the difference between two dates or datetimes, returning an integer value. The syntax involves specifying the unit of time (year, quarter, month, day, hour, minute, second) and the start and end date values. DATEDIFF returns only positive integers, so if the end date is earlier than the start date, a negative result is obtained. It calculates the number of specified datepart boundaries crossed between the two dates but does not count the units between them. The function's accuracy can be limited due to truncation or rounding issues, and it requires consideration of boundary values when calculating differences in years or months. DATEDIFF is useful for finding the age of objects, calculating time intervals, and analyzing data. It has equivalents in other database systems like Snowflake, MySQL, and PostgreSQL, although with some variations in supported datepart values.

InfluxData has closed a $51 million Series E round and a $30 million debt facility, raising $81 million in capital. The funding was led by Princeville Capital and Citi Ventures, with participation from existing investors. This investment validates the acceleration of the time series market and reinforces InfluxData's mission to provide developers with tools to innovate with time series data. With this new funding, InfluxData plans to usher in a new era of data intelligence that combines time series and real-time analytics, powered by its new database engine, InfluxDB IOx. The company aims to make InfluxDB more accessible to an even wider population of developers and deploy many new features on top of the new database core.

InfluxData, the creator of the leading time series platform InfluxDB, has raised $81 million in capital funding through a $51 million Series E funding round led by Princeville Capital and Citi Ventures. The new investment will help accelerate the development and rollout of InfluxDB's new database engine, InfluxDB IOx, which reimagines InfluxDB as a real-time columnar datastore built to ingest billions of data points in real-time. This investment brings InfluxData's total equity funding raised to $171 million, solidifying its position as the leading time series market player. With over 1,900 commercial customers and a thriving open source community, InfluxData is well-positioned to capitalize on the growing demand for time series data analytics in various industries.

The Metrics as a Service (MaaS) model is an approach to organizational data management that stores monitoring data in a central location, enabling different teams to easily access and extract maximum value from collected data. This centralized approach avoids silos and vendor lock-in, offers a better return on investment, and frees people up to work on higher-value tasks. By understanding the needs of various stakeholders, including IT Operations, Application Developers, and Business Managers, a MaaS solution can provide granular insights and visibility into monitoring data, allowing organizations to make informed decisions and drive business growth. A purpose-built time series platform, such as InfluxDB, is essential for building a scalable and reliable MaaS solution, providing multiple deployment options, flexible data collection, and advanced analytics capabilities. By leveraging open-source tools like InfluxDB, organizations can create a cost-effective way to consolidate monitoring solutions across the organization, encouraging cross-functional collaboration, innovation, and democratizing data access.

This tutorial demonstrates how to perform downsampling with InfluxDB IOx, a new storage engine for InfluxDB Cloud, using AWS Lambda. It covers importing dependencies, gathering authentication credentials, instantiating the Flight SQL client, executing a query, creating a reader object, reading data into a pyarrow.Table object, converting data to a Pandas DataFrame, and writing the data back to InfluxDB. Additionally, it provides instructions on creating an AWS Lambda function, deploying the script, and running the Lambda function on a schedule using CloudWatch or EventBridge. InfluxDB IOx addresses key user needs, including unlimited cardinality, SQL support, and fast analytics, by leveraging Apache ecosystem technologies such as Parquet, DataFusion, Arrow, and Flight SQL.

InfluxDB has announced the general availability of its new database engine, InfluxDB IOx, which offers significant improvements in performance and efficiency for handling metrics, events, and traces. The new engine can ingest, store, and analyze high-volume and high-velocity data in real-time, providing real-time insights and expanding use cases such as observability and distributed tracing. Additionally, the new database engine is optimized for low latency queries, reduces storage costs through Apache Parquet compression, and improves developer efficiency with native SQL queries and integrations with other tools like Grafana and Tableau. The rollout will occur in stages, starting with two AWS regions, and will expand to more regions and cloud providers over time.

InfluxDB has been leading the time series database market since January 2016, but the company is committed to continuous improvement. The rollout of an all-new and improved InfluxDB Cloud powered by IOx brings significant enhancements, including support for unlimited cardinality, a rebuilt storage engine with better performance and data compression, low latency queries, native SQL queries, and improved interoperability. The new engine is built using the Rust language and leverages Apache Arrow and other projects to provide a cutting-edge experience for users working with time series data.