Why Is Dataware Necessary to Handle the New Requirements of AI Applications Today?

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6 min read

Editor's Note: This is the 2nd blog post in a 5-part series that describes how modern enterprises are struggling with the handling of data, making it available to applications without creating new silos, and how MapR solves these challenges by introducing a new layer of abstraction called dataware. The first blog post can be found here.

When it comes to AI, success is all about the data: finding it, cleaning it, and feeding it to insatiable algorithms. It is not an exaggeration to say that 90% of AI success is derived from these data logistics. AI is data hungry, and it works best when it has as much data from as many sources as possible.

Now that AI has gone from its early skunkworks stage into production, the challenge facing companies has become how to productize and streamline the needed data logistics and data engineering so that data scientists don't spend the vast majority of their time wrangling data and instead spend that time putting data to use.

Increasingly, dataware has become the key to the success of AI deployments. Dataware is an abstraction layer that allows data to be managed as a first-class enterprise resource decoupled from any other dependencies. Dataware effectively handles the diversity of data types, data access, and ecosystem tools needed to manage data as an enterprise resource, regardless of the underlying infrastructure and location. Perhaps more importantly, dataware allows organizations to take advantage of new tools, algorithms, and approaches without having to start over each time.

Dataware allows companies to take data from wherever it exists and put it in a platform that can deliver the data to workloads used for production. Dataware does this by bringing all operational, historical, and real-time data in files, tables, and streams together in one place that is always available.

In essence, dataware replaces the older model of data pipelines, where data scientists have to wrangle data from multiple sources and put it through an ETL process. This is especially helpful when putting data to use in production.

Both in training and in operations, AI is perhaps the largest consumer of data the world has ever known. In addition, as time passes, it's the rare AI model that stays static. To ensure accuracy, any system that's using AI needs a consistent approach to bringing in and integrating data. This also includes feeding data to AI for training, taking the model from training, putting it in an operational model, and then getting the scoring and predictive model.

Dataware allows companies to do this by keeping data consistent across the enterprise. Every aspect, from streaming to ingestion to prediction, is consistent, so data integration problems are alleviated. With dataware, companies no longer have to worry about transforming data within an application because it came from a particular source. For example, data from a legacy application can be used in conjunction with data from S3. Dataware manages this seamlessly for the developer.

AI applications pull data from many sources. Without dataware, that data then has to be extracted and put in a single format so that algorithms can be run against it. The ETL process that ensures this data extraction and integration can occur is time-consuming and difficult.

With dataware that supports multi-tenancy, all data sources can be integrated without the need for the ETL process. Multi-tenancy is key to simplifying access abstraction and ensuring security.

An API can be pointed at the data, and developers do not have to spend time figuring out how to format or access that data to get it to work. Dataware automates these aspects. Through dataware, data can be accessed in an abstracted way, making the entire process of AI and development of applications simpler and faster.

By standardizing and automating so much of this data work, dataware empowers companies to leverage AI in a way that wasn't previously possible. Dataware allows AI models to constantly be updated and empowers ongoing and continuous learning of those models by making it easier to access the data needed for AI to work properly. It is important to do this training and exploring as fast as possible. Dataware ensures companies can integrate real-time data from as many repositories as necessary.

Dataware adds a new layer to the data stack and provides greater overall simplicity, as it resides underneath the API level, where all integration and assembly occurs. As a result, companies can go further and faster with AI in their operations.


This blog post was published April 11, 2019.
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