For enterprises, data and analytics can be compared to cooking: as of 2019, organizations have an abundance of high-quality ingredients (data) with which to cook (derive actionable business insights).
The problem is that most companies aren’t very good in the kitchen.
Just as the best ingredients are useless without the knowledge necessary to cook a wonderful dish, so too is data without an adequate understanding of that data and the tools required to analyze it for the purposes of business insights.
Fortunately, the team at Hanu is here to help you get started with the basics of structured and unstructured data, and some tools to help you get the most out of both.
Structured data
Often thought of as “traditional data,” structured data can be stored in Relational Databases (RDBMS). Common examples are fixed-length data points such as social security numbers, phone numbers, and zip codes. Most structured data consists of customer information in the form of variable length text strings, provided they are well organized and stored in an RDBMS format.
Before the advent of Big Data, structured data was what organizations used to drive business decisions. Data of this kind is typically stored in data warehouses and easy to digest, which make analytics possible through the use of traditional, legacy data mining solutions.
Common RDBMS applications include bank ATMs, inventory management solutions, airline reservation systems, and most CRM data.
Unstructured data
Essentially, unstructured data is all the data that – while it has an internal structure – does not conform to pre-defined data models. It can consist of textual or non-textual information and is typically the result of streaming data coming from platforms such as social media, location services, mobile apps and Internet of Things (IoT) enabled technologies.
Given the diversity among unstructured data sources, organizations struggle to store and analyze it using traditional data warehouse and data mining methods. But unstructured data is both too valuable and too prevalent to ignore, making up more than 80% of enterprise data. Companies need to find new solutions and creative methods for analyzing it alongside traditional structured data to gain the advantages promised by digital transformation.
Semi-structured data
The third type of data structure, semi-structured data uses internal tags and markings to let users identify different data elements, which in turn enables grouping and hierarchies.
While it makes up only 5-10% of the total data pie, semi-structured data still has several key business use cases. A typical example of the semi-structured data type is email: email’s native metadata lets users classify and search information with the use of additional tools.
Most semi-structured data systems are used for data transport purposes. Sensor data, electronic data interchange (EDI), many social media platforms, and NoSQL databases are all examples of semi-structured data at work.
Making sense of unstructured and structured data in the Azure Cloud
Given the need to integrate traditional structured data with vast amounts of unstructured data from emerging sources, many new tools are becoming available.
For storage purposes, the team at Hanu usually recommends the Azure Data Lake for both structured and unstructured data. To learn more about data lakes and data storage methods, check out our Chief Strategy Office Dave Sasson’s article here.
But when it comes to data, choosing the right repository is just the beginning. Businesses need a database solution that makes their data searchable and discoverable for a range of analytics platforms.
One such solution is Azure Cosmos DB, Microsoft’s globally distributed multi-model database. Cosmos DB offers several well-defined consistency models for fine-tuning performance, single-digit-millisecond latencies at the 99th percentile anywhere in the world, and guarantees high availability with multi-homing capabilities
Cosmos DB is schema-agnostic and automatically indexes all data without the headache of schema and index management. It also supports document, key-value, graph, and column-family data models.
Another solution is Apache HBase, an open-source, NoSQL database modeled after Google BigTable and built on Hadoop that can scale to handle petabytes of data on thousands of nodes. A schema-less database organized by column families, HBase provides random access and strong consistency for vast quantities of unstructured and semi-structured data. With HBase, neither the columns nor the type of data stored in them are required to be defined before using them.
When coupled with HDInsight, HBase provides automatic sharding of tables, automatic failover, and strong consistency for reads and writes. In-memory caching for reads and high-throughput streaming for writes enhances performance, and by creating an HBase cluster inside a virtual network, other HDInsight clusters and applications can directly access tables.
Have more questions about how to get business insights from your structured and unstructured data? Contact the data modernization experts at Hanu today. We’re happy to answer any questions you might have. We will also provide FREE estimation if you want to implement Azure for your data infrastructure.
