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NoSQL and the Enterprise Data Hub | @BigDataExpo #BigData #Hadoop #IoT

Why NoSQL?

If you’re in business, you have data. And if you’re like a lot of businesses, you have a lot of data. And it’s not only coming from your customers, it’s coming from other business units, partners, in-house applications, the cloud, hardware logs, etc. And that data could help you be better at your business, if only you had the right solution to access it in ways that deliver quantifiable value.

One solution is to build an enterprise data hub (EDH) through which all your data flows for processing. Many IT professionals turn to Apache Hadoop as the core component of an EDH, but other technologies can be complementary. For example, a NoSQL database can play an important role in an EDH to help manage the complexities of processing and storing structured data for your organization.

Why NoSQL?
While RDBMSs still have plenty of useful functions, consider a NoSQL database instead to stay on top of large amounts of data generated across your enterprise. And while data volume is a significant factor in using NoSQL for an EDH, the more important factor is the variety of data. If you have different RDBMS schemas used by your various business units, partners, and other data sources, consolidating those schemas into one big “super schema” is far from straightforward. So for the purposes of a consolidated data set in an EDH, rethinking data modeling and going with a less structured format is recommended. Certainly, RDBMSs can handle less structured formats as well via BLOBs and CLOBs, but then you still have the challenge of growing data volumes and the associated expense in a RDBMS architecture.

What Can You Store in NoSQL?
Any type of structured information that you store in an RDBMS can also be efficiently stored in a NoSQL database. This includes customer profile information, product specifications data, sales/order data, etc. You won’t get all the powerful advantages of an RDBMS, like multi-row transactional integrity (via ACID transactions) or full SQL querying support, but you get the flexibility and scale to handle the requirements of a typical EDH.

To show how NoSQL can be helpful, we can describe a simple example. Suppose you have one data set that describes your inventory of books and another data set that describes your inventory of DVDs. Attributes that describe books (e.g., author, publisher, page count) are different than attributes for DVD movies (e.g., director, cast, length), which make data modeling harder in an RDBMS. You either have to have separate product tables, one for each product type, or you have to create a product table with lots of columns, many of which will be unused. On the other hand, NoSQL databases can vary the attributes on a per-row basis, meaning that each database record (representing a given product) can have its own set of columns.

Other EDH Requirements
Data security is important for any business, including in an EDH implementation. Your EDH might store all kinds of confidential information on your employees and customers. As a start, you want to make sure that sensitive data is accessible only by authorized users. Access controls tied in with authentication mechanisms like Kerberos are required for your NoSQL implementation. Not only will this protect sensitive data like personally identifiable information (PII) from broad access, but it also allows various users groups to include their proprietary data sets into your EDH while preventing other business units see their data.

Other security mechanisms like audit logs on data access let you investigate whether there are any suspicious activities on your data, like heavy data reads late at night. Audit logs are also important for demonstrating regulatory compliance, which is critical for highly-regulated industries such as healthcare, financial services, and telecommunications.

Other complementary security controls that are not necessarily part of your NoSQL solution, but can be added via third-party tools, should be considered as well. Encryption-at-rest, for example, can ensure your data is protected should your physical storage devices be stolen. Data masking and format-preserving encryption technologies lower the cost of securing your EDH by essentially obscuring the sensitive parts of your data.

High performance is another requirement of a successful EDH. Having so much data in one place is no good if it takes too long to access it. NoSQL allows you to take the workload off some of your overburdened RDBMSs and store even more data, while avoiding the overhead. Your datasets are only going to continue to grow, and that’s why you should invest now in technology that’s going to scale with the size of the data.

Usage Patterns
What can you do with your EDH? You can store and analyze customer histories or behavior to understand opportunities for additional revenue. With a “360 degree customer view” in which all aspects of customer information (such as purchases, returns, downloads from your website, etc.) are in a central system, you can more quickly identify what your customers currently have and don’t have, and what you can promote to them.

In addition, analysis of aggregated behavior lets you understand customer preferences. The premise is that customers with similar purchasing histories will buy similar items in the future, and using that information can help you steer customers to those expected purchases. Sometimes the behavior analysis suggests red flags because a pattern is revealed that leads to customer churn. If customers are exhibiting behavior that is consistent with other customers who have left, you definitely want to know that as soon as possible, not after the customer has gone to a different vendor.

Finally, in some industries, preventing fraud is top of mind, and understanding anomalous user behavior will indicate potential for fraudulent activity. Identifying fraud typically takes huge volumes of data for analysis, and collecting data from numerous sources into an EDH is one way to address this problem.

Summary
The EDH can become an important part of your enterprise data architecture, and a successful implementation will depend on the right tools. For your structured data, you should consider using a NoSQL database to handle the wide variety of data as well as the growing scale of your data.

One example of a technology that could fit your needs is MapR-DB , available as part of the MapR Distribution including Hadoop. This product provides an enterprise-grade NoSQL database integrated into Apache Hadoop, so you can process structured and unstructured data in the same platform. You will likely have a wide variety of data types to store and analyze in your EDH, which is why such an integrated solution is an architecture worth pursuing.

More Stories By Jim Scott

Jim has held positions running Operations, Engineering, Architecture and QA teams in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. Jim has built systems that handle more than 50 billion transactions per day and his work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.

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