Big Data Engineering

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Characteristics of a Big Data Analysis Framework

Even though new sets of tools continue to be available to help you manage and analyze your big data framework more effectively, you may not be able to get what you need. In addition, a range of technologies [more…]

Identify the Data You Need for Your Big Data

Take stock of the type of data you are dealing with in your big data project. Many organizations are recognizing that a lot of internally generated data has not been used to its full potential in the past [more…]

The Fundamentals of Big Data Integration

The fundamental elements of the big data platform manage data in new ways as compared to the traditional relational database. This is because of the need to have the scalability and high performance required [more…]

How to Integrate Big Data

Just having access to big data sources is not enough. You will need to integrate these sources. Soon there will be petabytes of data and hundreds of access mechanisms for you to choose from. But which [more…]

How to Ensure the Validity, Veracity, and Volatility of Big Data

High volume, high variety, and high velocity are the essential characteristics of big data. But other characteristics of big data are equally important, especially when you apply big data to operational [more…]

How to Create a Big Data Implementation Road Map

Big data implementation plans, or road maps, will be different depending on your business goals, the maturity of your data management environment, and the amount of risk your organization can absorb. So [more…]

RDBMSs in a Big Data Environment

Big data is becoming an important element in the way organizations are leveraging high-volume data at the right speed to solve specific data problems. Relational Database Management Systems are important [more…]

Text Analytics for Unstructured Big Data

Numerous methods exist for analyzing unstructured data for your big data initiative. Historically, these techniques came out of technical areas such as Natural Language Processing [more…]

The Creation of Manageable Big Data Structures

As computing moved into the commercial market, data was stored in flat files that imposed no structure. Today, big data requires manageable data structures. When companies needed to get to a level of detailed [more…]

How to Set the Architectural Foundation for Big Data

It is important to lay a strong architectural foundation if you want to be successful with big data. In addition to supporting the functional requirements, it is important to support the required performance [more…]

Performance Matters in Big Data Architectural Management

Your big data architecture also needs to perform in concert with your organization’s supporting infrastructure. For example, you might be interested in running models to determine whether it is safe to [more…]

The Evolution of Distributed Computing for Big Data

Behind all the important trends over the past decade, including service orientation, cloud computing, virtualization, and big data, is a foundational technology called [more…]

Distributed Computing Basics for Big Data

If your company is considering a big data project, it’s important that you understand some distributed computing basics first. There isn’t a single distributed computing model because computing resources [more…]

Big Data Applications

Custom and third-party applications offer an alternative method of sharing and examining big data sources. Although all the layers of the reference architecture are important in their own right, this layer [more…]

Big Data Virtualization Basics

Virtualization is ideal for big data because it separates resources and services from the underlying physical delivery environment, enabling you to create many virtual systems within a single physical [more…]

The Cloud in the Context of Big Data

Cloud computing is a method of providing a set of shared computing resources and is becoming increasingly important for your big data initiative. The cloud includes applications, computing, storage, networking [more…]

How to Make Use of the Cloud for Big Data

Clearly, the very nature of the cloud makes it an ideal computing environment for big data. So how might you use big data together with the cloud? Here are some examples: [more…]

Big Data and Polyglot Persistence

The term polyglot is borrowed and redefined for big data as a set of applications that use several core database technologies, and this is the most likely outcome of your implementation planning. The official [more…]

Big Data and the Origins of MapReduce

MapReduce is increasingly becoming useful for big data. In the early 2000s, some engineers at Google looked into the future and determined that while their current solutions for applications such as web [more…]

Functional vs. Procedural Programming Models for Big Data

When people talk of map and reduce in big data, they do so as operations within a functional programming model. Functional programming is one of the two ways that software developers create programs to [more…]

Why Should You Use Hadoop for Big Data?

Search engine innovators like Yahoo! and Google were faced with a bog data problem. They needed to find a way to make sense of the massive amounts of data that their engines were collecting. These companies [more…]

The Evolution of Deployment Models in the Big Data Era

With the advent of big data, the deployment models for managing data are changing. The traditional data warehouse is typically implemented on a single, large system within the data center. The costs of [more…]

The Future of Data Warehouses in the Big Data Era

The data warehouse market has indeed begun to change and evolve with the advent of big data. In the past, it was simply not economical for companies to store the massive amount of data from a large number [more…]

Data Mining for Big Data

Data mining involves exploring and analyzing large amounts of data to find patterns for big data. The techniques came out of the fields of statistics and artificial intelligence [more…]

Put Big Data to Use

Text analytics can be used to help gain insight into data. So, what if the data is big data? That would mean that the unstructured data being analyzed is high volume, high velocity, or both. [more…]

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