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Enterprises have access to limitless amounts of data thanks to technology like the embedded sensors, the Internet of Things (IoT), activity trackers, open data groups, and much more. This data comes from different systems and several different formats. 

Because of this, it’s important to have a central hub that can streamline a wide variety of data. That’s where a unified data architecture (UDA) comes into play. A central architecture ensures that all data is stored, processed, analyzed, and output efficiently and reliably. 

So what does a modern unified data architecture entail? 

A modern unified data architecture 

Data collectors/producers  

All data starts somewhere, whether that’s an IoT device, web-based form, or a browser cookie. Data collectors come in a wide range of shapes and sizes, but their end goal is to provide a solid stream of data. 

At this stage, the data is in a raw and unstructured or loosely structured form. It lacks any organization or usefulness until it’s sorted and processed via the next few stages of the UDA process. 

Data uptake 

Robust data systems take all of the data collected from different sources and bring it together to begin its process through the UDA. The data ingestion system is highly dependent on performance and throughput as it’s the primary bottleneck of the architecture. That’s because it’s responsible for consolidating data from multiple sources while keeping the flow regardless of the volume. 

Data ingestion systems use various techniques and processes typically based on the volume and frequency of the data, including publish-subscribe, batch ingestion, micro-batches, stream ingestion, and more. It also incorporates the push and pulls mechanisms of data extraction.  

Data processing 

The data processing system is responsible for taking the raw data and turning it into something useful. It does this through various methods, including profiling, validating, enriching, and aggregating datasets. 

Once complete, the data can be sorted, modeled, and stored for future use. This ensures that it can be stored organized and structured, making it easily accessible for the next several steps within the UDA. 

Storing the data 

The processed data can now be efficiently stored while waiting to be analyzed or used down the line. Data lakes are a common solution to storage as they provide a centralized solution that can accept a wide variety of data types. Additionally, platforms like PetroVisor can add indexing functionality to help increase efficiency and speed when preparing data for the next stage in the UDA. 

Modeling the data 

Now it’s time to make sense of the data. The data is organized and structured. However, it still isn’t providing any value. Artificial intelligence, specifically machine learning (ML), is one way to organize and model data for use and analysis.  

At first, data on the ML models must be separated into training and validation sets. Afterward, it can be trained on how to effectively process the data. But once trained, machine learning can create insights, metrics, and classifications on new incoming data, compiling datasets into valuable and insightful information. This information can then be analyzed and used to empower the decision-making process. 

PetroVisor - The unified platform for automation and integration 

PetroVisor provides an all-in-one platform that provides companies with a reliable unified data architecture solution. The platform incorporates an open API automation and workflow system so that companies can customize it to their specifications. 

Kenton G.
Post by Kenton G.
June 28, 2022