Now Available: BlueData EPIC on Amazon Web Services

On behalf of the entire BlueData team, I’m thrilled to announce the general availability (GA) of BlueData EPIC on AWS. With this announcement, BlueData is executing on our goal of offering the first and only Big-Data-as-a-Service software platform to support: Choice and flexibility: Provides data science teams with the ability to create their own unique Big Data […] Read More

Distributed Data Science with Spark 2.0, Python, R, and H2O on Docker

Here at BlueData, I’ve worked with many of our customers (including large enterprises in financial services, telecommunications, and healthcare, as well as government agencies and universities) to help their data science teams with their Big Data initiatives. In this blog post, I want to share some of my recent experiences in working with the data […] Read More

Next Generation Big Data Solutions with Dell and EMC

BlueData has been fortunate to work with both EMC and Dell as a partner.  We’ve collaborated with many teams and joint customers across both companies, united in our common mission to simplify and accelerate the deployment of Big Data infrastructure in the enterprise. The newly combined Dell Technologies is a technology powerhouse capable of delivering […] Read More

HPE and BlueData − A Game-Changing Combination for Big Data

Last week, at the Strata + Hadoop World conference, an estimated 7,000 people descended upon New York City to hear about the latest and greatest in Big Data, Artificial Intelligence, and the Internet of Things.  I enjoy this conference for two main reasons: it’s a great opportunity to meet with dozens (if not hundreds) of […] Read More

Evolving from a Data-Centric to Application-Centric Model for Big Data

Over the past several years, many Fortune 500 enterprises have implemented their Hadoop architectures with a data-centric approach. The common theme in their architectures is the build out of one or more Hadoop clusters supporting the concept of a centralized data lake. In other words, they created a central storage repository that is the source […] Read More

Beyond Hadoop-as-a-Service: The Opportunity for Big-Data-as-a-Service

This is a guest blog courtesy of Raghunath Nambiar, distinguished engineer and chief architect of big data and analytics solution engineering at Cisco.  This post originally appeared on the Cisco blog site here. I’ve written in the past about the opportunity for Hadoop-as-a-Service (HaaS) – providing self-service provisioning, elastic scaling, and support for multi-tenancy. But in my […] Read More

HDFS Upgrades Are Painful. But They Don’t Have to Be.

It’s hard enough to gather all the data that an enterprise needs for a Hadoop deployment; it shouldn’t be hard to manage it as well. But if you follow the traditional Hadoop “best practices”, it is. In particular, upgrades to the Hadoop Distributed File System (HDFS) are excruciatingly painful. By way of background, each version […] Read More

Big-Data-as-a-Service. On-Prem or in the Cloud. It’s BDaaS

Today, we announced the directed availability of BlueData EPIC Enterprise for AWS – as part of our broader strategy to provide a flexible Big-Data-as-a-Service (BDaaS) platform for both on-premises and public cloud deployments. Will the real BDaaS please stand up … Here at BlueData, we are maniacally focused on simplifying and streamlining Big Data infrastructure […] Read More

App Workbench – Managing the Menagerie of Big Data Apps

One of the most challenging aspects of Big Data deployments is keeping up with the dynamic nature of Big Data frameworks, distributions, applications, and their latest versions. The success or failure of a Big Data implementation may hinge on how well the organization handles support for the menagerie of applications and tools that data scientists, […] Read More

Apache Spark Integrated with Jupyter and Spark Job Server

Apache Spark is clearly one of the most popular compute frameworks in use by data scientists today. For the past couple years here at BlueData, we’ve been focused on providing our customers with a platform to simplify the consumption, operation, and infrastructure for their on-premises Spark deployments – with ready-to-run, instant Spark clusters. In previous […] Read More