Database Reference
In-Depth Information
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Questions that require advanced analytics:
An examples of this type is a credit card system
that uses machine learning to build better fraud-detection algorithms. The goal is to go beyond
the simple business rules involving charge frequency and location to also include an individual's
customized buying patterns, ultimately leading to a better experience for the customer.
Organizations that take advantage of Big Data to ask and answer these questions will more effectively derive new
value for the business, whether it is in the form of revenue growth, cost savings, or entirely new business models. One
of the most obvious questions that then comes up is this: What is the shape of Big Data?
Big Data typically consists of delimited attributes in files (for example, comma separated value, or CSV format ),
or it might contain long text (tweets), Extensible Markup Language (XML),Javascript Object Notation (JSON)and other
forms of content from which you want only a few attributes at any given time. These new requirements challenge
traditional data-management technologies and call for a new approach to enable organizations to effectively manage
data, enrich data, and gain insights from it.
Through the rest of this topic, we will talk about how Microsoft offers an end-to-end platform for all data, and the
easiest to use tools to analyze it. Microsoft's data platform seamlessly manages any data (relational, nonrelational and
streaming) of any size (gigabytes, terabytes, or petabytes) anywhere (on premises and in the cloud), and it enriches
existing data sets by connecting to the world's data and enables all users to gain insights with familiar and easy to use
tools through Office, SQL Server and SharePoint.
How Is Big Data Different?
Before proceeding, you need to understand the difference between traditional relational database management
systems (RDBMS) and Big Data solutions, particularly how they work and what result is expected.
Modern relational databases are highly optimized for fast and efficient query processing using different
techniques. Generating reports using Structured Query Language (SQL) is one of the most commonly used techniques.
Big Data solutions are optimized for reliable storage of vast quantities of data; the often unstructured nature of
the data, the lack of predefined schemas, and the distributed nature of the storage usually preclude any optimization
for query performance. Unlike SQL queries, which can use indexes and other intelligent optimization techniques to
maximize query performance, Big Data queries typically require an operation similar to a full table scan. Big Data
queries are batch operations that are expected to take some time to execute.
You can perform real-time queries in Big Data systems, but typically you will run a query and store the results
for use within your existing business intelligence (BI) tools and analytics systems. Therefore, Big Data queries are
typically batch operations that, depending on the data volume and query complexity, might take considerable
time to return a final result. However, when you consider the volumes of data that Big Data solutions can handle,
which are well beyond the capabilities of traditional data storage systems, the fact that queries run as multiple tasks
on distributed servers does offer a level of performance that cannot be achieved by other methods. Unlike most
SQL queries used with relational databases, Big Data queries are typically not executed repeatedly as part of an
application's execution, so batch operation is not a major disadvantage.
Is Big Data the Right Solution for You?
There is a lot of debate currently about relational vs. nonrelational technologies. “Should I use relational or non-
relational technologies for my application requirements?” is the wrong question. Both technologies are storage
mechanisms designed to meet very different needs. Big Data is not here to replace any of the existing relational-
model-based data storage or mining engines; rather, it will be complementary to these traditional systems, enabling
people to combine the power of the two and take data analytics to new heights.
The first question to be asked here is, “Do I even need Big Data?” Social media analytics have produced great
insights about what consumers think about your product. For example, Microsoft can analyze Facebook posts or
Twitter sentiments to determine how Windows 8.1, its latest operating system, has been accepted in the industry and
the community. Big Data solutions can parse huge unstructured data sources—such as posts, feeds, tweets, logs, and
