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.

Let’s try to figure out what happens with the application when the source file is much bigger than the available memory. The memory in the below tests is limited to 900MB […]. Naively we could think that a file bigger than available memory will fail the processing with OOM memory error. And this supposition is true.

Once upon a time there was only one way to use Apache Spark but support for additional programming languages and APIs have been introduced in recent times. A novice can be confused by the different options that have become available since Spark 1.6 and intimidated by the idea of setting up a project to explore these APIs.

RELATED BLOG POSTS Unraveling the Complex Streaming Data Pipelines of Cybersecurity Best Practices Blog 5 Min Read Security is top of mind for every enterprise these days. There are so many threats they can hardly be counted, but one commonality exists: data is always the target. Unravel’s mission is to help organizations better understand and improve the performance of their data-based applications. We’re a data business, so we appreciate the scope and implications of these threats.

A very important question is how long something takes and the answer to that is fairly straightforward in normal life: Check the current time, then perform the unit of work that should be measured, then check the time again. The end time minus the start time would equal the amount of time that the task took, the elapsed time or wallclock time.