The Attunity Blog
Broadcasting live from theCUBE, in conjunction with Strata + Hadoop World in San Jose, California, George Gilbert, Big Data & Analytics Analyst at Wikibon, interviewed Attunity CMO, Itamar Ankorion, Attunity customer Chris Murphy of a large global insurance company, and Martin Lidl from Deloitte, the firm that helped build the data lake. They discussed their experience using Attunity Replicate to enable a Hadoop data lake for a major insurance firm and some of the goals, challenges and solutions of the implementation.
What if you could derive real-time insights using ALL of your data? Making ALL of your data available for analytics helps to support business decisions that improve operations, optimize customer service and enable your company to compete more effectively. To do so, companies like yours often look for ways to bring live data into your analytics platform where it can be merged with other to serve a growing number of users.
The creation and consumption of data continues to grow by leaps and bounds and with it the investment in big data analytics hardware, software, and services and in data scientists and their continuing education. The availability of very large data sets is one of the reasons Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend, with Google, Facebook, Baidu, Amazon, IBM, Intel, and Microsoft, all with very deep pockets, investing in acquiring talent and releasing open AI hardware and software.
The Attunity team was excited to announce this week that Poly-Wood, LLC, an outdoor furniture manufacturer, has chosen our data warehouse automation solution, Attunity Compose, to accelerate its data warehousing build process to enable faster analytics delivery in support of business decision making.
This week, we announced the availability of a new release of Attunity Compose, Attunity’s data warehouse automation software. This new release offers significant enhancements for enterprise customers including 10x faster extract, transform, load (ETL) processing speeds as well as advanced DevOps capabilities that streamline the data warehousing design, development and rollout processes.
At $5.3 billion, SAP is the largest ERP market and is used by many of the world’s largest organizations. Demand for making SAP data available in analytics platforms continues to grow, but making this data available for analytics in real-time can be challenging. Attunity Replicate for SAP is a unique solution for this market need.
Recently, Attunity was pleased to announce that the growing US dental benefits provider had selected our universal data integration solution, Attunity Replicate, as its strategic enterprise data ingest and replication platform. The Attunity software will provide Dentegra with real-time data availability and integration across its heterogeneous operational databases and analytic platforms, and is set to displace the dental insurer’s incumbent data replication technology. This initiative is expected to enable Dentegra to accelerate business solutions while reducing IT costs and labor.
Attunity is proud to be a member of the Hortonworks, Inc. Partnerworks program, a global community to jointly innovate and implement with Hortonworks integrated customer solutions for the on-premises data center and in the cloud. With certifications in HDP, HDP Yarn Ready, and HDP SEC Ready, Attunity is a founding member of Hortonworks’ the Modern Data Solutions (MDS) partner initiative.
NGP VAN is the leading technology provider to progressive political campaigns and non-profit organizations, offering clients an integrated platform of the best fundraising, compliance, field, organizing, digital, and social networking products. Nearly every major Democratic campaign in America is powered by NGP VAN, including the Obama campaign’s voter contact, volunteer, fundraising and compliance operations in all 50 states.
The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms.