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In order to analyze user behavior, an eCommerce company treats each user login as a separate event and collects time series data at regular time intervals. With this data, they can track user login history, usage over time, the frequency at which the user visits the website, etc. A trading organization uses algorithms that collect and analyze data about how the market is changing in real-time to provide insights to its customers. Self-driving cars continuously gather data about how their local environment is changing in order to analyze and respond.
By preserving the integral time series nature of the data and leveraging time series database monitoring tools, organizations can collect valuable information on system performance and user behavior over time.
Time series data is a sequence of data points continuously collected at regular time intervals. Here, time is not just a metric, but a primary axis. However, there are multiple challenges to storing data at this resolution – you rapidly end up with a massive amount of data. Yes, time series data piles up very quickly! Having a lot of data becomes a problem, especially when you need to record and analyze it, which is why people are now turning to time series databases.
As per DB Engines, time series databases (TSDBs) are the fastest growing category of database (even ahead of Hadoop and NoSQL data stores). There are two reasons behind this rapid growth in popularity:
Time series data accumulates very quickly. Did you know a single connected car can collect more than 25GB of data per hour! Standard databases cannot handle such a scale. In contrast, time series databases can easily manage the massive amount of data by introducing efficacies like higher ingest rates and better data compression – concepts that are only possible when you consider time as a first-class citizen.
TSDBs include functions common to time series data analysis like data retention policies, flexible time aggregations and continuous queries that help you provide a better user experience.
When you build an IoT application in a DevOps environment or drive a modern application architecture, you want them to be scalable and up all the time. To ensure scalability and continuous uptime, you require sub-second visibility into the architecture, which means you need to monitor your data continuously over time.
Continuous integration, testing, and delivery have significantly improved software quality. However, clean code doesn’t necessarily mean software will behave as expected. Failure to account for unforeseen conditions like server issues or faulty algorithms can make an application unpredictable. By recording and analyzing operational metrics, alerts, logs and business data in one place – a time series database – you can easily identify any system flaws, saving time and money.
Many new applications are being driven by microservices and instrumented by DevOps toolchains. Modern application architectures generate such a massive amount of metrics and events that the ingestion of legacy monitoring data becomes impossible. You need a platform that can ingest the metrics at breakneck speed, enable queries across the dataset and help you monitor and analyze issues in real-time. This is where time series data platforms come into the picture.
To efficiently handle incident management, you need to focus on time series analysis in your monitoring and detection systems. There are several time series database monitoring tools that are optimized for collecting time-stamped data continuously or at fine-grained intervals. These tools can store data for a much longer time in comparison to other traditional monitoring tools. They can compare and collate data in DevOps, coming from multiple devices rather than just the IT infrastructure. Let’s look at some of the tools available in the market:
Prometheus (popular among Kubernetes shops) is an open-source tool that helps you record time series data, monitor your system and generate alerts. You can monitor both – machine-centric data as well as service-oriented architectures. The tool’s biggest advantage is that, in a world of microservices, it supports multi-dimensional data collection and queries.
InfluxDB is an open-source time series database monitoring tool, optimized for quick, high-availability storage and retrieval of time series data. InfluxDB is known for its operations monitoring, application metrics and real-time analytics. The database is in a TICK stack, a technology that is growing and so is its need in the market.
Grafana provides you a plethora of visualization options, giving you a consolidated view of the business and IT infrastructure – helping you better understand your time series data. It’s popular for enabling high-quality, interactive graphics and visualizations to give you deep insights into infrastructure and application health.
Graphite is a popular open-source tool offering web-based dashboards for monitoring and analysis. Graphite is quite popular in enterprises because of the level of automation it provides – new data points are created automatically in the database. The tool doesn’t collect metrics or come bundled with any metrics collector, but it’s not difficult to find one to feed your metrics into Graphite.
Splunk is a leader in unstructured data, and with its Splunk7 release, Splunk added metrics collection and a native time-series database. Additionally, Splunk offers new ways of exploring time-series data and offers apps like Splunk App for Infrastructure that uses collectd to collect infrastructure data at scale. You can build dashboards and alerts that span time-series data and unstructured data with Splunk.
Today, applications are undergoing a sea of change. Organizations realize they should turn to artificial intelligence and machine learning driven smart apps that can drive automation through IoT and advanced instrumentation. Architectures for such applications are driven by microservices and developed using DevOps toolchains, with the ultimate goal of quick releases to offer an excellent customer experience.
As the processes to deliver these apps have evolved, so has the capability to monitor and instrument them. In order to do this effectively, you need to continuously collect data at regular time intervals. However, time series data stacks up so fast that your traditional monitoring platforms may fail to ingest everything quickly enough. You need time series database monitoring tools that can handle a large number of metrics and events generated by your system infrastructure.
These DevOps monitoring tools are pioneering the shift to time series databases, making it possible for organizations to become data-driven and take on digital transformation initiatives. Going forward, they’ll continue to grow with the requirements of AIOps, predictive analytics, and newer application architectures in the future.
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