Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level,
In many cases the data lake can be defined as a super set of repositories of data that includes the traditional data warehouse, complete with traditional relational technology. One significant example of the different components in this broader data lake, is in terms of different approaches to the
This white paper discusses the advantages of using the PySpark API, which enables the use of Python to interact with the Spark programming model. It starts with a basic description of Spark and then describes PySpark, its benefits, and when it is appropriate to use instead of "pandas" open source
This is the second in a series of blogs on analytics and the cloud. We will consider the rise of the Internet of Things (IoT), analytics used on that data and how the cloud can be utilized to drive value out of instrumenting a very wide range of ‘things’.
This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.
J White Bear is a data scientist and software engineer at IBM. In this podcast, White Bear discusses simultaneous localization and mapping, an ongoing research area in robotics for autonomous vehicles and well-recognized as a nontrivial problem space in both industry and research.
Seth Dobrin is vice president and CDO, IBM Analytics, platform development, at IBM. In this podcast, Dobrin shares experiences using Apache Spark for data science transformation and some thoughts on a larger vision for data science transformation at scale.
It is said that more data has been created in the past two years than in the entire preceding history of mankind. It would be interesting to find out how much of this data has been analyzed and put to good use. Analyzing and harnessing big data is undoubtedly the major challenge of the day for all
The financial industry faces a wide range of priorities including customer experience, instant fulfillment, cyber security, risk management and compliance, and expenses. A modern financial services platform is needed to strengthen financial businesses as they progress into the future. And this
In a recent CrowdChat discussion, a group of Hadoop and Spark subject matter experts from the IBM Analytics group discussed using cloud-based Hadoop and Spark services as a lever for business agility. From their contributions we drew ten hot topics and themes for experts in all areas of the big
Now that we’re into the swing of 2017, the time is ripe for the first CrowdChat of 2017 to explore the goals, challenges and strategies that CDOs and CIOs are focused on for their organizations. Get involved and share your thoughts in this kick-off IMB Big Data CrowdChat.
Some organizations misunderstand the optimized way to use Hadoop and Spark together, primarily because of their complexity. But investing in both technologies enables a broad set of big data analytics and application development use cases. See what Niru Anisetti and Rohan Vaidyanathan have to say
Ever hear of the Big Data Dudes? If not, crawl out from under that rock and see what these intrepid big data and analytics heroes are up to in their latest analytics blockbuster, "Big Data Dudes and the Lost in Las Vegas Mystery."
The manufacturing industry finds itself embroiled in major changes these days, and analytics, cloud-based technologies, the Internet of Things and volumes of data are fueling its metamorphosis. See how manufacturing companies are shifting resources toward value-add processes such as