Maybe classifying data as structured or unstructured isn’t so simple. What is structured to some may not be structured to others and vice versa. When it comes to the business value of data, consider another way to look at data—whether it is repetitive data or non-repetitive data.
The IBM dashDB Local warehouse solution combines a full range of data warehousing capabilities while delivering the levels of flexibility and control associated with the cloud. Begin your own free trial of dashDB Local today to discover how it can help you bring new levels of capability to your
The choice to flex a data warehouse on a private cloud is a personal one. It offers benefits in three key areas: enhanced control over data and apps, better management and monitoring, and custom tailoring that is built to address specific user requirements and self-service applications.
A world that grows increasingly complex calls for disruptive innovation in an open, collaborative environment. See how open data science provides an ecosystem of expertise, skill sets and advanced open source data science tools that fuels collaborative creativity in the development and deployment
Understanding data and data relationships is particularly vital in the energy and utilities industry. Discover how industry data models serve as blueprints for defining structures that provide a broad, in-depth view of business, and how they helped one energy and utilities organization extract data
As a foundation for data lakes and refineries, NoSQL databases provide access, processing and storage to structured and unstructured data for high-performance statistical modeling and exploration. Take a look at the multitude of advantages of NoSQL databases and opportunities to bridge them to open
Performing programmatic actions on data across services is quite possible in today’s technology ecosystem. And now, the transfer of data across services such as the dashDB data warehouse and deploying it in new environments is also possible. However, the questions often asked by customers center on
In the past few years, we’ve seen an explosion in the number and variety of organizations that are adopting big data technologies such as Hadoop and Spark and the recent trend to leverage data services in the cloud. How are enterprises coping?
Apache Spark not only excels at data warehousing, in-memory environments for building data marts and other functions, it also is well suited for pulling data from a wide range of sources and transforming and cleansing that data in an Apache Hadoop cluster. And then there is Spark’s complementary
Implementing advanced analytics practices in the government sector can be particularly challenging because of infrastructure and software, security, agility and internal human obstacles. But there is a way to bring the community closer to analytics-driven government and to leave behind the
In 2016 and beyond, the data warehouse will continue to be relevant, but the new requirement will be for hybrid data warehouse solutions. As organizations strive to be more data-driven, new types of analytics are coming to the forefront. IBM has again been named a leader in the Gartner Magic
Since the relational database first came to be, back in the 1980's, there's been a need for organizations to continuously evolve their analytical architectures. A new report talks about how modern groups are getting their data and analytics architecture updated for a new century—and a new set of
Dealing with slow technology is a major concern for anyone who needs quick access to analytic insights. For this reason, it’s vital to have a data warehouse appliance with sufficient speed that allows all users to make the most of its analytic power.
Big data has shown itself to be an illuminating force for sourcing the insight that is powering a tremendous transformation in modern life. To keep pace with the rapid changes, today’s organizations are seeking to improve their capabilities, competencies and culture to turn data into business value