Available on-demand Apache Spark ™️ has become the de facto open source standard for big data processing due to its ease of use and performance. And the open source Delta Lake project enhances Spark’s lead with new capabilities like ACID transactions, Schema Enforcement and Time Travel.
On-demand webinar Data & ML projects bring many new complexities beyond the traditional software development lifecycle. Unlike software projects, after they were successfully delivered and deployed, they cannot be abandoned but must be continuously monitored if model performance still satisfies all requirements.
Nehmen Sie an diesem Webinar teil und erfahren Sie, wie Sie mithilfe der MapR Converged Data- und Talend Real-Time Big Data-Plattformen Datenintegrationen für die schnelle Transformation von Daten in Anwendungen der nächsten Generation beschleunigen und skalieren können.
As your business generates more and more data to be analyzed, you have to deliver it. In real time. At scale. Right now. How can you and your organization keep up?
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single, general-purpose compute engine.
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single compute engine.
Big data presents both enormous challenges and incredible opportunities for companies in today’s competitive environment. To deal with the rapid growth of global data, companies have turned to Hadoop to help them with performing real-time search, obtaining fast and efficient analytics, and predicting behaviors and trends.
The growing number and variety of big data technologies is rewriting the book on enterprise application development and deployment. Which tools are right for the job and where do they fit in your data pipeline?
Mission-critical systems are pretty needy. They require capabilities such as high availability, disaster recovery, multi-tenancy and resource management to power data applications without fail and meet business service level agreements.
How are leading enterprises deploying Hadoop with Spark in production? What key considerations should you consider to put your Spark-based innovative app to work faster? In this webinar on October 8th at 10 am PT/1 pm ET, you’ll hear real-life examples of companies turning data into action using Spark and Hadoop.
Watch this webinar recording to hear MapR and Databricks uncover practical advantages for having the full set of Spark technologies at your disposal and reveal the benefits for running Spark on Hadoop.
Join us for a complimentary webinar to learn more about Apache Spark ‘s MLlib, which makes machine learning scalable and easier with ML pipelines built on top of DataFrames.