The IBM Analytics Engine team is excited to announce the General Availability (GA) of IBM Analytics Engine, the next generation of IBM’s Apache Spark and Apache Hadoop cloud service in the London DC.
This blog post is an excerpt from our solution tutorial - "Gather, visualize, and analyze IoT data". The tutorial walks you through setting up an IoT device, gathering mobile sensor data in the Watson IoT Platform, exploring data and creating visualizations and then using advanced machine learning services to analyze data and detect anomalies in the historical data.
In the world of Data Science, the time required to transform data to good quality is a recurring barrier towards gaining insights. Data scientists or analysts will spend a bulk of their effort in cleaning the data using a variety of handwritten scripts. IBM Watson Data Platform’s data refining tools aim to reduce the pain associated with creating good quality data. The tool has an intuitive user interface and templates enabled with powerful operations to shape and clean data. It also provides metrics and data visualization which aid in every step of the process. Incremental snapshots of the results are provided allowing the user to gauge success with each iterative change. Saving, editing, and running the steps within projects provide the ability to refine data of almost any size within the Watson Data Platform.
Data science is rapidly being established as the new frontier for analytics, as it moves from niche interest to the mainstream. Combining elements of statistics, computer science, applied mathematics and visualization, it offers a powerful new set of tools and techniques to enable more effective decision-making.
Machine learning is one of the most exciting areas of data science, with enormous potential to transform data into the pure gold of competitive advantage. Data scientists can seem like wizards when their models first accurately predict customer or market behavior, or reveal valuable insight from previously untapped data sources.
It’s often said that data science is a team sport. Everyone now uses data in their day-to-day work—which means data scientists, data engineers, developers and business analysts need to find effective ways to work together to deliver the insight that the business needs. However, many organizations are still struggling to put the tools and processes in place to support seamless collaboration between these different skill sets.
The launch of IBM® Analytics Engine marks the start of a new stage in the evolution of big data analytics—which makes it the perfect time for you to reconsider your analytics architecture. If you are struggling to transform big data into business insight, or your company’s adoption of Hadoop and Spark seem to be stalling, please read on to learn more about what IBM Analytics Engine can do for you.
IBM Watson Data Platform, a platform designed to make data discoverable and accessible to data professionals around the world. Central to this is our new IBM Data Catalog offering, which enables companies to build a 360 view of all information, making data accessible for self-service analytics and data science initiatives.
Data Science Experience IBM As IBM continues to expand its global cloud data center footprint this year, the Data Science Experience (DSX) team has made it possible for enterprise customers to run their data science and machine learning workloads in different geographic regions. Now Data Science Experience (DSX) is available through IBM's London data center.