Tag: Hadoop

Feed: Planet PostgreSQL. This is my third blog about Stored Procedure support in PostgreSQL, the previous two blogs are reachable from the HighGo CA blogs site https://www.highgo.ca/author/ahsan-h/. The first blog was introduction and usage of Stored Procedures and its difference with Stored Functions, the second blog focussed on creating and using Procedures with Definer and Invoker rights. The purpose of this blog is to demonstrate how to execute a Stored procedure from Java. Java is an important platform for developing enterprise level applications and it is really important to have the capability to call Stored procedure from Java. Lets start ... 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: Featured Blog Posts - Data Science Central. Author: Avinash Udaykumar. Consistent asset health across many levels from cell sites to regions is critical to ensure uninterrupted operations for telecom operators. However, proactively identifying anomalous patterns like equipment malfunction remains a major challenge. Drawing on Subex’s deep implementation experience, this paper describes the chief steps necessary to deploy successful pattern detection solutions. It also examines some high impact use cases for pattern detection that have systematically delivered value for telecom carriers. High operational efficiency is an important differentiator for telecom operators establishing their brand reputation on consistency and service availability ... 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

Feed: AWS Big Data Blog. Amazon EMR is a big data service offered by AWS to run Apache Spark and other open-source applications on AWS in a cost-effective manner. Amazon EMR Notebooks is a managed environment based on Jupyter Notebook that allows data scientists, analysts, and developers to prepare and visualize data, collaborate with peers, build applications, and perform interactive analysis using EMR clusters. EMR notebook APIs are available on Amazon EMR release version 5.18.0 or later and can be used to run EMR notebooks via a script or command line. The ability to start, stop, list, and describe EMR ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Aileen Scott. Data scientist! The most awaited job for candidates who’ve got the affinity for statistics and mathematics. With data growing at an exponential rate, data science now becomes an appealing career path. Data science is super technical, interesting, and tough to get into. However, making an entrance as a data scientist may not be as challenging as you think. First, know that data is being generated in the blink of an eye. To be precise, according to the “2021 Robert Half Technology Salary Guide” data scientist touts to be one ... Read More

Feed: CIO. Author: . Nearly every company that has gone through a digital transformation has struggled with how to best utilize the vast amounts of data being collected. Indeed, we estimate that for most companies, 85%-95% of data is never fully utilized, and therefore wasted. There are many stages in a data lifecycle, including acquisition of the data, creation of data engineering/data sets to impart meaning to the raw data, bulk storage of the data for further use/analysis, database creation for exploring the data, and finally capability to use advanced analytics and/or Machine Learning to extract insights from the data ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Paul Scott-Murphy. By Paul Scott-Murphy, VP Product Management at WANdiscoBy Jeremy Zogg, Sr. Director Engineering at GoDaddyBy Roy Hasson, WW Analytics Specialist Leader at AWS Organizations collect and store vast amounts of data that is processed and analyzed throughout each day in order to deliver fresh actionable insights. As business requirements evolve, however, organizations are faced with a need to collect and store even more data from diverse sources. To manage the ever-increasing volume of data and demands from the business, organizations build and maintain large Hadoop clusters. These primarily reside on-premises and ... Read More

Feed: SAS Blogs. Author: Joe Furbee. SAS loves data. It's our raison d'être. We've been dealing with Big Data long before the term was first used in 2005. A brief history of Big Data*: In 1887, Herman Hollerith invented punch cards and a reader to organize census data. In 1937, the US government had a punch-card reading machine created to keep track of 26 M Americans and 3 M employers as a result of the Social Security Act. In 1943, Colossus was created to decipher Nazi codes during World War II. In 1952, the National Security Agency was created to ... Read More