Companies nowadays rely heavily on data to make business-decisions, think of sales forecasts, anticipate market shifts, and managing risks. To be able to make accurate decisions, you need accurate data. With the help of data ingestion, you can grab data from all your different sources and move it to one place, so you get a clear overview.
The literal definition of ingestion means “the process of absorbing information”. Data ingestion, therefore, means, the process of absorbing data from the sources to the destination. Often, that’s a database, warehouse, or lake or, in other cases, it’s used immediately for analyses.
As for sources, there are many different ones. Almost any business activity generates some kind of data, and thus turns into a data source, especially when using the Datumize Data Collector (DDC). Sources consist of your traditional ones, like machine data and file data. But with the DDC, you can now reach Dark Data. The valuable sources that are reached with the DDC are network transactions, distributed databases, industrial networks, and WiFi technologies.
Thanks to data ingestion, you can move the data from many different sources to one place. Data ingestion is important because this is the start of your analytics and downstream reporting. If not done correctly, you will make inaccurate decisions based on false or incomplete data. It is important that during the transformation, no data gets lost or damaged.
Types of ingestion
There are two different types of processes; the batch ingestion process and a real-time ingestion process. You do not need to choose one model that applies to all your data sources. As explained by Stitch, businesses often decide on the model appropriate for each data source by considering the timeliness with which they’ll need analytical access to the data.
Batch processing
If you use the batch ingestion process, data is grouped at the source and imported to the destination at periodic intervals. Groups might be formed based on logical ordering, the activation of certain conditions, or a simple schedule. Batch processing is most used when near real-time data is not critical for the business. The reason for this is because it takes less effort and is cheaper than real-time streaming ingestion.
Batch process ingestion can be particularly useful when you have processes that run on a schedule at regular intervals. For example, processes that report daily at an exact time. It is effective to process large amounts of data in smaller chunks over a longer period.
Real-time ingestion
Real-time processing, or streaming, is useful for when the information you need out of the data is highly time-sensitive. In contrast with batch processing, real-time ingestion involves no grouping at all. It is a much more expensive process because it requires the system to be programmed in such a way that it doesn’t miss any events or new information.
With stream processing, data sources are manipulated and loaded as soon as it’s created or recognized by the data ingestion layer. While it is more complex to implement real-time data ingestion, it could be very useful to analytics that need continuously updated and fresh data.
How can the Datumize Data Collector help with ingestion?
Datumize Data Collector (DDC) is a lightweight and high-performance software for data ingestion, capable of tapping into the data from WiFi technologies or IoT via active polling techniques. Datumize Data Collector offers also an affordable, non-intrusive and efficient way to gather data by tapping directly into an existing network (industrial or IT). In different industries, this data capturer and ingestion tool can be helpful in various ways.
In hospitality, you can make use of WiFi technologies to gain more insight into customer behavior. Thanks to WiFi access points, Datumize Data Collector (DDC) can perform the first layer of motion data enrichment, consisting of trilateration and device fingerprinting.
The resulting structured and prepared geospatial data can then be further expanded by joining it with third-party business-specific data, resulting in valuable operational metrics.
A user case that is a good example is the tracking of hotel guests, giving valuable insights into the use of various hotel facilities, restaurant peak times, etc. The result of such information is a deep understanding of guests' behavior, which can be used to plan resource allocations and productivity enhancements.
Another example is the application of Datumize Data Collector in warehouses, where we join the geospatial data with data from a Warehouse Management System. The resulting combination allows the tracking of events in a warehouse on a mission level.
This solution has a major advantage over the traditional polling approach, as DDC utilizes overhead-free network sniffing: it reads and interprets the live data of an existing network via well-established use of port mirroring.
DDC can ingest data also flowing through networks of an operational technology environment, tapping into existing control loops using various industrial protocols (Modbus, OPC-DA, Fieldbus, etc.). The extracted data flow can then be further enhanced by the Cook Processor functionality of DDC and fed into the corporate data warehouse or lake for advanced analytics.
Final thoughts
Data ingestion is where your decision-making process begins. If you make a mistake in the ingestion phase, this will eventually flow through your decisions. Therefore, it is important to use a solid ingestion tool that can handle the complexity, timeliness, and security of ingestion.
With the DDC you can not only reach more data sources that will give you so much more insight but it also smoothes out the entire ingestion phase.
