Systems | Development | Analytics | API | Testing

Since the very beginning, Unravel Data has made it a mission to ensure customers are successful, wherever they deploy their modern data platforms. On-premises, in the cloud, and in hybrid environments, Unravel supports the full stack of data processing engines to provide data operations visibility wherever it resides.

As the global business climate is experiencing rapid change due to the health crisis, the role of data to provide much needed solutions to urgent issues are being highlighted throughout the world. Helping customers manage critical modern data systems for years, Unravel sees a heightened interest in fortifying the reliability of business operations in healthcare, logistics, financial services and telecommunications.

Retailers are using big data to report on consumer demand, inventory availability, and supply chain performance in real time. Big data provides a convenient, easy way for retail organizations to quickly ingest petabytes of data and apply machine learning techniques for efficiently moving consumer goods. A top supermarket retailer has recently used Unravel to monitor its vast trove of customer data to stock the right product for the right customer, at the right time.

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.

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.

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.

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.

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.