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
Open source is a disruptor that never quits. It seems to be penetrating and transforming every aspect of established data, analytics and application ecosystems. In this podcast, recorded at IBM InterConnect 2016, listen to David Taieb, a cloud data services developer advocate at IBM, share his
Spark just seems to be getting big play everywhere in the technology arena. What is Spark? And do you need it? Get a good glimpse into its in-memory execution capabilities, some of its key components, its integrations and its availability as a service.
Spark’s momentum is building, and it is rapidly emerging as the central technology in analytics ecosystems within organizations. See why Spark’s technical advancements around iterative processing combined with its easy overall environment and tool set for developers make it a true operating system
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?
The open source Hadoop framework accommodates distributed storage and processing of large data sets on clusters of computers through the use of programming models. If that description sounds complex, then dig into this breakdown of Hadoop components to gain an understanding of just how flexible
https://www.ibm.com/cloud/db2-warehouse-on-cloudApache 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
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
This short series of blogs for the business user is designed to turn key technologies into easy to understand concepts to help explain why they are needed in a modern digital enterprise. When looking at consumer and business transactions in today’s online world, many people may ask, “Why big data
Before the San Jose, California version of Strata + Hadoop World fades from memory, check out these five key experiences that highlighted this conference for data scientists, data engineers, developers and other IT professionals.
In a podcast recorded at IBM InterConnect 2016, Roger Strukhoff, executive director, Tau Institute for Global ICT Research, shares his expert perspective on how open source initiatives are transforming the Internet Of Things.
On the heels of several key announcements to broaden the IBM Cloud Data Services portfolio, see how a wide range of technologies can be implemented in a cloud-based, data warehouse architecture to support operational and analytical workloads.
At the recent Strata + Hadoop World conference, everybody was talking about the growing number of open source solutions. But what does this mean for the rapidly maturing field of data science? Find out in this firsthand report—and get a sense of how open source solutions will take analytics to the
When migrating Hadoop clusters, whether on-premises or to the cloud, little is gained by a migration that puts a halt to production. By taking a transactional approach to data migration, companies can eliminate downtime without risking data loss, capitalizing on the migration process both during