Palo Alto, CA, USA
Apr 1, 2020 | By Sandeep Uttamchandani
Data is the new oil and a critical differentiator in generating retrospective, interactive, and predictive ML insights. There has been an exponential growth in the amount of data in the form of structured, semi-structured, and unstructured data collected within the enterprise. Harnessing this data today is difficult — typically data in the lakes is not consistent, interpretable, accurate, timely, standardized, or sufficient. Scully et. al.
Mar 25, 2020 | By Bala Venkatrao
Last year, Cloudera released the Cloudera Data Platform, an integrated data platform that can be deployed in any environment, including multiple public clouds, bare metal, private cloud, and hybrid cloud. Customers are increasingly demanding maximum flexibility to adhere to multi-cloud, hybrid data management demands. Unravel has from the beginning has made it a core strategy to support the full modern data stack, on any cloud, hybrid as well as on-premises.
Feb 26, 2020 | By Phil Schwab
This article discusses four bottlenecks in BigData applications and introduces a number of tools, some of which are new, for identifying and removing them. These bottlenecks could occur in any framework but a particular emphasis will be given to Apache Spark and PySpark.
Unravel Introduces Workload Migration and Cost Analytics Solution for Azure Databricks, now available on Azure Marketplace
Feb 25, 2020 | By Jason Baick
Fresh off a new funding round which includes strategic cloud partner Microsoft, Databricks continues to make huge strides in its mission to ease Spark complexity and simplify analytics through its Unified Analytics Platform. Databricks has also graduated from “visionary” to “leader” in the latest Gartner Magic Quadrant for Data Science and Machine Learning Platforms in 2020.
Feb 14, 2020 | By Phil Schwab
Solving a problem programatically often involves grouping data items together so they can be conveniently operated on or copied as a single unit – the items are collected in a data structure. Many different data structures have been designed over the past decades, some store individual items like phone numbers, others store more complex objects like name/phone number pairs. Each has strengths and weaknesses and is more or less suitable for a specific use case.
Dec 2, 2019 | By Unravel
Whether you are looking to establish a “cloud first” strategy for big data or are migrating from on-premises Cloudera, Hortonworks, and MapR, this session provides practical insights on how to make that journey simple and cost effective on Azure. Join Chris Santiago as he shares how a data driven approach can guide you in deciding which cloud technologies will best fit the needs unique to your organisation and budget.