A big data integration platform that is flexible and scalable is needed to keep up with today’s ever-increasing big data volume. Download this infographic to find out how to build a strong foundation with big data integration.
Cloud-based data presents a wealth of potential information for organizations seeking to build and maintain competitive advantage in their industries. However, as discussed in “The truth about information governance and the cloud,” most organizations will be challenged to reconcile their legacy on-premises data with new third-party cloud-based data. It is within these “hybrid” environments that people will look for insights to make critical decisions.
Any organization wishing to process big data from newly identified data sources, needs to first determine the characteristics of the data and then define the requirements that need to be met to be able to ingest, profile, clean,transform and integrate this data to ready it for analysis. Having done that, it may well be the case that existing tools may not cater for the data variety, data volume and data velocity that these new data sources bring. If this occurs then clearly new technology will need to be considered to meet the needs of the business going forward.
The data integration tool market was worth approximately $2.8 billion in constant currency at the end of 2015, an increase of 10.5% from the end of 2014. The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types in the enterprise and beyond — to meet the data consumption requirements of all applications and business processes.
The biggest changes in the market from 2015 are the increased demand for data virtualization, the growing use of data integration tools to combine "data lakes" with existing integration solutions, and the overall expectation that data integration will become cloud- and on-premises-agnostic.
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data.
To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.
IBM Compose Enterprise delivers a fully managed cloud data platform on the public cloud of your choice - including IBM SoftLayer or Amazon Web Services (AWS) - so you can run MongoDB, Redis, Elasticsearch, PostgreSQL, RethinkDB, RabbitMQ and etcd in dedicated data clusters.
You’ve taken the first step and already know that a document- oriented database is the right database for your application. From here, you still have to decide where and how you’ll deploy the software and its associated infrastructure. These decisions lead to additional considerations around administrative overhead, technical support, open-source options, data sovereignty and security, and more. This paper aims to outline the deployment options available when you select IBM® Cloudant® as your JSON store.
This whitepaper talks about how organized criminals and lone fraudsters are continuously adapting to the ever-changing world we live in. Through IBM solutions for insurance fraud prevention, both enterprise and industry-wide financial institutions can prevent future fraud.
The IBM Counter-Fraud Management for Insurance solution is designed to help insurers prevent and intercept attempted fraud while detecting, identifying, and building the case against past fraudulent activity and improper payments.
By taking full advantage of the integration and advanced capabilities currently being offered by leading counter fraud solution providers - including predictive analytics and cognitive computing - enterprises can expect to achieve significantly better outcomes.Aberdeen Group's analysis helps to quantify the value of counter fraud analytics in the insurance industry.
Three decades after the height of the cold war between Apple and IBM, these former nemeses have formed a partnership emblematic of the shifting tide in enterprise IT. Macs are no longer just a niche choice for the creative class. In this report, see how Macs have proven themselves in the enterprise. They’re easier to support than PCs, increase worker productivity, enhance information security and save money.
A range of application security tools was developed to support the efforts to secure the enterprise from the threat posed by insecure applications. But in the ever-changing landscape of application security, how does an organization choose the right set of tools to mitigate the risks their applications pose to their environment? Equally important, how, when, and by whom are these tools used most effectively?
Today, when you make decisions about information technology (IT) security priorities, you must often strike a careful balance between business risk, impact, and likelihood of incidents, and the costs of prevention or cleanup. Historically, the most well-understood variable in this equation was the methods that hackers used to disrupt or invade the system.
Countless studies and analyst recommendations suggest the value of improving security during the software development life cycle rather than trying to address vulnerabilities in software discovered after widespread adoption and deployment. The justification is clear.For software vendors, costs are incurred both directly and indirectly from security flaws found in their products. Reassigning development resources to create and distribute patches can often cost software vendors millions of dollars, while successful exploits of a single vulnerability have in some cases caused billions of dollars in losses to businesses worldwide. Vendors blamed for vulnerabilities in their product's source code face losses in credibility, brand image, and competitive advantage.
The Business Case for Data Protection, conducted by Ponemon Institute and sponsored by Ounce Labs, is the first study to determine what senior executives think about the value proposition of corporate data protection efforts within their organizations. In times of shrinking budgets, it is important for those individuals charged with managing a data protection program to understand how key decision makers in organizations perceive the importance of safeguarding sensitive and confidential information.
This white paper will provide a road map to the most effective strategies and technologies to protect data and provide fast recovery should data be lost or corrupted due to accident or malicious action.