Technologies like AI and machine learning are becoming essential cogs in the modern company’s arsenal. Add in IoT devices, and you’ll have reams of rich data feeding into these emerging technologies.
IoT devices can create billions of data points of potentially useful data every hour. Smartphones, farming equipment, and autonomous cars are just a few of the disparate IoT machines churning out data at an unfathomably large scale. This data needs to be stored somewhere accessible for further processing. Hyprcubd‘s solution can corral your IoT data and provide actionable data for your company.
The Challenges of Managing IoT Data
IoT data’s two main challenges are how to store it and how to process these ever-growing data sets. The data must be stored so that it can be easily retrieved for data mining. The devices that produce this data must also be registered and categorized. Database tables are then used to track the devices along with their properties.
The high complexity inherent with IoT devices demands a unique database structure with active management. A data management system for IoT devices should be designed to:
- Handle large streams of data concurrently across devices.
- Be highly available.
- Offer scalability that can keep up with however large the data becomes.
The data management system you use should also allow you to consume live data streams and analyze them with a backend application. The various IoT devices under management create alerts and notifications that are then stored as data in a database. These systems must interact with both data collection and retrieval efficiently and at scale. Handling the firehose of IoT data streams is not what traditional databases were designed to handle. It’s not just the sheer amount of data but also the velocity at which the data comes down the pipe that is a challenge for traditional database structures.
Managing IoT Time Series Data
A time-series database (TSDB) optimized for time-stamped data is the ideal format for IoT devices. Devices disparate as power grids, autonomous cars, satellites, and human-wearable devices all have sensors emitting data. TSDBs track and measure various metrics and events over time. This data needs to be clustered by time and summarized so that decision-makers can draw actionable insights from the immense quantity of IoT data produced by these IoT devices.
Hyprcubd can help manage the lifecycle of TSDB data and produce a summarization of its records. Consider the fact that one sensor alone could create 1440 data points each day if sampled once per minute. A summary of this data can be queried over any given time period. Some example queries useful to businesses might include month-to-month percentage changes or year-on-year statistics broken down by month.
Data life cycle management demands that high precision data is only kept around for a relatively short period. As time passes, data is downsampled and compressed into longer-term summary data. A robust database management system actively purges data, fully confident that a rich summary record is being kept in its place.
Collecting IoT Data At Scale
Collecting data is a very different task than retrieving it. A typical IoT setting involves collecting a nearly uncountable number of data points from a myriad number of devices. The Hyprcubd platform makes it simple to query the desired data from one device over a specific period. A Hyprcubd time-series database ingests a data stream and reformats it as timestamped data. Data streams contextualized with time stamps allow for more straightforward data synthesis and executive decision making based on the data.
A typical pipeline for IoT data passes through multiple levels. This process might look like the following:
- The first layer processes and collects data in real-time. This initial stage is known as the hot layer.
- The next layer stores a compact version with summary data. This is the compressed warm layer that enables quick retrieval times.
- Less actively queried data is then moved to long-term cold storage.
The recent data of IoT devices can be held the way it’s collected in the hot store. For example, IoT sensors might ping one data point value per minute. When analyzing these values, the required granularity for analysis might be one day. All the IoT data points for a single day will be moved into a single compressed field for ease of storage and retrieval. This allows data from a specific day to be retrieved through reading a single field. This reduces the data footprint and optimizes database queries to be extremely fast.
Managing Old IoT Data: Store or Delete?
Optimizations on long term storage can be made as access and cost demands dictate. Many companies store older data in a different state than more recent data. For example, old data should be moved from hot to cold storage over time. Data can be scored such that once it falls below a certain threshold, this less valuable data is then offloaded to cold storage. A data scoring mechanism makes sure that data most useful to a company is easily retrievable. As the IoT data ages, it could be further compressed and stored in a filesystem. These compressed files can then use cheap storage to form a company’s data warehouse.
Purging old data is an essential bookkeeping step of any database. Hyprcubd has some tools designed explicitly for data cleaning. For example, the time to live feature instructs the database to delete the data after a certain period of time. Hyprcubd can also concurrently process database maintenance jobs on a daily schedule. Batched jobs can quickly offload the data to long-term storage and remove it from either the hot or warm layer.
The insights produced from a system are only as good as the available data. Ensuring your IoT data is relevant and readily available is essential. A company can waste lots of time trying to find a cloud vendor that lacks the required data management expertise. The Hyprcubd platform features efficient data algorithms that allow for smooth database queries. Contact us today to see how you can implement the Hyprcubd solution with just a few lines of code.