Tag: Spark

Feed: R-bloggers. Author: R Views. [This article was first published on R Views, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. If you haven’t explored the RStudio website in a while, your next visit may include a pleasant surprise. I recently went to the Tidymodels page, just to see what was new and was immediately drawn into the new landscape imagined by the RStudio education team. Clicking on Get Started I came to ... Read More

Feed: R-bloggers. Author: tomaztsql. [This article was first published on R – TomazTsql, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Azure Databricks is a data analytics platform (PaaS), specially optimised for Microsoft Azure cloud platform. Databricks is an enterprise-grade platform service that is unified for data lake architecture for large analytical operations. Azure Databricks combines: large scale data processing for batch loads and streaming datasimplifies and accelerates collaborative work among data scientists, ... Read More

Feed: AWS Big Data Blog. Data is ubiquitous in businesses today, and the volume and speed of incoming data are constantly increasing. To derive insights from data, it’s essential to deliver it to a data lake or a data store and analyze it. Real-time or near-real-time data delivery can be cost prohibitive, therefore an efficient architecture is key for processing, and becomes more essential with growing data volume and velocity. In this post, we show you how to build a scalable producer and consumer application for Amazon Kinesis Data Streams running on AWS Fargate. Kinesis Data Streams is a fully ... Read More

Feed: SAS Blogs. Author: Jen Sabourin. Some people hear the words computer science and only think of coding but ask any computer scientist and they would tell you that coding is just a tool to solve important and interesting problems. At its core, computer science is about using technology to solve complex problems in the world, and many of the problems solved (and discoveries made) because of computer science have permanently reshaped the way we live, work and communicate. For example: Search engines were developed to solve the issue of information overload, connecting people to the most relevant information available ... Read More

Feed: Recent Announcements. Amazon EKS is a managed service that makes it easy for you to run Kubernetes on AWS. Spot Instances are spare EC2 capacity that offer steep discounts from On-Demand prices. Spot Instances are a great fit to run fault-tolerant applications on your Kubernetes clusters such as batch processing, Big Data ETLs using Apache Spark, and stateless API endpoints, because the scheduler provides built-in mechanisms to manage interruptions gracefully.   Until now, Amazon EKS customers had to manually configure Amazon EC2 Auto Scaling groups, manage graceful draining of Spot nodes, and upgrade the Spot nodes to latest Kubernetes ... Read More

Feed: SingleStore Blog – MemSQL is Now SingleStore. Author: Mike Pickett. When I joined an integration company in late ‘99, I didn’t think data and integration would be my focus and passion. But here I am coming up to 2021, more than 20 years later, in a market that has only gotten more exciting and more relevant to the business. In the early 2000s, it was all about the “connected enterprise”, getting your apps working together. Then it was the “intelligent enterprise” and we saw the rise of the enterprise data warehouse and analytics. In the early 2010s, Big Data ... Read More

Feed: Recent Announcements. Organizations want to leverage massive amounts of data for analytics and insights. To effectively use this data, customers often need to first build a workflow that defines the series of sequential tasks that prepare and process the data. Many customers use Apache Airflow to take advantage of its open source community and large library of pre-built integrations to third-party data processing tools such as Apache Spark and Hadoop. With Apache Airflow, workflows are created as Python scripts that are familiar to data engineers and data scientists. However, data engineers need to install, maintain, scale, and secure Airflow ... Read More

Feed: Recent Announcements. With workload partitioning enabled, each ETL job-run sets an upper bound on the unprocessed dataset size or limits the number of files with a job-run. For example, if you need to process 1,000 files, you can set the number of files to be 500 and separate them into two job runs, which can be executed sequentially or in parallel ... Read More

Feed: AWS Big Data Blog. AWS Glue provides a serverless environment to prepare (extract and transform) and load large amounts of datasets from a variety of sources for analytics and data processing with Apache Spark ETL jobs. This posts discusses a new AWS Glue Spark runtime optimization that helps developers of Apache Spark applications and ETL jobs, big data architects, data engineers, and business analysts scale their data processing and batch jobs running on AWS Glue automatically. Customers use Spark for a wide variety of ETL and analytics workloads on datasets with diverse characteristics. They want to ensure fast and ... Read More

Feed: AWS Big Data Blog. The machine learning (ML) lifecycle consists of several key phases: data collection, data preparation, feature engineering, model training, model evaluation, and model deployment. The data preparation and feature engineering phases ensure an ML model is given high-quality data that is relevant to the model’s purpose. Because most raw datasets require multiple cleaning steps (such as addressing missing values and imbalanced data) and numerous data transformations to produce useful features ready for model training, these phases are often considered the most time-consuming in the ML lifecycle. Additionally, producing well-prepared training datasets has typically required extensive knowledge ... Read More