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Databricks Partners with Tableau: Enabling Organizations To Run Business Intelligence on Data Lakes Faster and More Reliably - Databricks

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Databricks' Unified Data Analytics Platform Combined with Tableau Desktop and Server Enables Access to More Complete and Timely Data for Business Insights SAN FRANCISCO – October 2, 2019 – Databricks, the leader in Unified Data Analytics, today announced a partnership with Tableau Software, the leading visual analytics platform, to enable data teams to run business intelligence on data lakes faster and more reliably. Data lakes are frequently the largest source of data within organizations, but user analytics directly on the data lake often suffers from poor quality data and performance challenges. The new Databricks Connector, just released in version Tableau 2019.3, is optimized for performance and leverages integration with Delta Lake, an open source storage layer that makes existing data lakes reliable at scale. Through this integration, Tableau users can now access and analyze massive datasets across the entire data lake with the most up-to-date and real-time data. "Our goal is to give data teams improved access to data no matter where it lives in the organization. Creating a stronger, better performing integration with Tableau enables teams to now run analytics on the largest set of data which usually resides in the data lake," said Michael Hoff, senior vice president of Business Development and Partners.


Embarking on big data architecture framework

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Below figure shows the overall Big Data analytics architecture framework. MapReduce and Spark provide the large data processing capabilities for different types of analytics. For example, descriptive analytics uses MapReduce to filter and summarize a large amount of data. Similarly, predictive analytics techniques employ MapReduce to process data from data warehouses. Before a data analytics process begins, the relevant data are collected from a variety of sources (stage 1).


Overview: Apache Spark on HDInsight Linux

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Apache Spark is an open-source parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. Spark processing engine is built for speed, ease of use, and sophisticated analytics. Spark's in-memory computation capabilities make it a good choice for iterative algorithms in machine learning and graph computations. Spark is also compatible with Azure Blob storage (WASB) so your existing data stored in Azure can easily be processed via Spark. When you create a Spark cluster in HDInsight, you create Azure compute resources with Spark installed and configured.


2019 Datanami Readers' and Editors' Choice Awards - Datanami

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Datanami is pleased to announce the results of its fourth annual Readers' and Editors' Choice Awards, which recognizes the companies, products, and projects that have made a difference in the big data community this year. These awards, which are nominated and voted on by Datanami readers, give us insight into the state of the community. We'd like to thank our dedicated readers for weighing in on their top picks for the best in big data. It's been a privilege for us to present these awards, and we extend our congratulations to this year's winners. Readers' Choice: The Map of Biodiversity Importance, which combines 2,600 detailed species habitat maps that have been collected over the past 50 years into a single, searchable database that can be used by scientists and conservationists alike Editor's Choice: Okera introduced the industry's first fine-grained access control solution to support both structured and unstructured data from a single unified platform


Top 10 Data Science Platforms That Cash the Analytics Code Analytics Insight

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Data science platforms are the must-have tools for any business enterprises that aspire to scale up its frontiers. Data science platform is essentially a software hub around which all the data science functionalities like data exploration and integration from various sources, coding, model building are performed. Data science platforms are programmed to train and test models and deploy the results to solve real-life business problems. Data science platforms are a massive hit driving business revenues to new heights, this can be ascertained by the fact that the global data science platform market is expected to grow at a CAGR of around 39.2% in the next decade to reach to approx. Using the massively varied data science platforms, one question is often asked and debated, which ones are the top data science platforms that let you use the best tools for the job at hand?