It’s an exciting time for Big Data Analytics and Artificial Intelligence/Machine Learning (AI/ML). If you are in one of the many organizations using these techniques to deliver new services and product experiences, you will be interested in knowing that HPE completed its acquisition of BlueData today.
Every day, customers ask, “What can you do to help accelerate and simplify our digital transformation journey?” Using Big 
According to Gartner, by 2020 half of organizations will lack sufficient AI and data literacy skills needed to extract business value from their data.1 Customers are already demanding easier-to-implement, faster-to-deploy, and more cost-effective solutions for AI/ML and Big Data Analytics.
Data-first vision
HPE’s acquisition of BlueData is part of our "data-first" vision enabling us to provide easier, faster, and more cost-effective methods of deploying AI/ML, data science, and Big Data Analytics applications on-premises, in the cloud, and across hybrid environments.
BlueData’s current customers span multiple industries. Organizations such as Citi, GM Financial, GlaxoSmithKline, Macy’s, Nasdaq, and SCL Health have taken advantage of the container-based BlueData EPICTM software platform, including key features such as:
- ElasticPlane™ – enables you to spin up instant clusters on-demand in a secure, multitenant environment in just a few mouse clicks
- IOBoost™ – delivers performance on par with bare-metal, but with the agility and simplicity of containers.
- DataTap™ – decouples compute and storage resources, eliminating time-consuming data movement yet with the ability to tap into both on-premises and cloud-based storage
The BlueData platform can be run on-premises, on multiple public clouds including AWS, Google Cloud Platform and Microsoft Azure, and as a hybrid multi-cloud environment enabling effective utilization of resources and lower operating costs.
With the flexibility for your tools of choice, the BlueData EPIC platform is pre-integrated with many of the most common AI and Big Data tools. You can easily spin up Cloudera, Hortonworks, and MapR clusters; create standalone Spark and Kafka environments; deploy machine learning with TensorFlow or H2O; and install your choice of data science tools like Jupyter or Zeppelin notebooks. You can even add your own preferred tools and quickly upgrade to new versions, providing the ultimate in agility and configurability.
Our vision is to provide customers with an integrated software and hardware experience that takes the complexity out of deploying Big Data Analytics and AI/ML infrastructure and applications, democratizing the use of our customer’s most valuable asset, data. HPE Apollo servers is an ideal platform on which to deploy BlueData Epic and your containerized Big Data Analytics and AI/ML applications, providing highly scalable and cost-effective infrastructure for these classes of applications. These platforms, including the most recently announced Apollo 4200 Gen 10 Server, are ideal for symmetric and asymmetric Hadoop clusters and for creating data lakes with Software-Defined Storage.
To answer the customer question, “What can you do to help accelerate and simplify our digital transformation journey?,” HPE and BlueData provide a self-service, multi-tenant, cloud-ready infrastructure on which to more easily, more quickly, and more cost effectively deploy all your Big Data Analytics, Artificial Intelligence, and Machine Learning applications.
Learn more
1 Fostering Data Literacy and Information as a Second Language: A Gartner Trend Insight Report (February 23, 2018)
Forward-looking statements:
Information set forth in this communication, including statements as to Hewlett Packard Enterprise's outlook and financial estimates and statements as to the expected timing, completion, and effects of the proposed acquisition by Hewlett Packard Enterprise of BlueData, constitute forward-looking statements within the meaning of the safe harbor provisions of the Private Securities Litigation Reform Act of 1995.
These statements are based on various assumptions and the current expectations of the management of Hewlett Packard Enterprise and may not be accurate because of risks and uncertainties surrounding these assumptions and expectations. Factors listed below, as well as other factors, may cause actual results to differ significantly from these forward-looking statements.
Major risks, uncertainties, and assumptions include, but are not limited to: the expected benefits and costs of the transaction; management plans relating to the transaction; the expected timing of the completion of the transaction; statements of the plans, strategies, and objectives of Hewlett Packard Enterprise for future operations, including with respect to artificial intelligence and big data analytics; any statements regarding anticipated operational and financial results; any statements of expectation or belief; and any statements of assumptions underlying any of the foregoing; the risk that disruptions from the transaction will harm Hewlett Packard Enterprise business; the effect of economic, competitive, legal, governmental and technological factors, and other factors described under "Risk Factors" in Hewlett Packard Enterprise's Annual Report on Form 10-K and Quarterly Reports on Form 10-Q.
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