Tag: storage

Feed: Planet PostgreSQL. 1. Introduction This blog is to follow up on the post I published back in July, 2020 about achieving an in-memory table storage using PostgreSQL’s pluggable storage API. In the past few months, my team and I have made some progress and did a few POC patches to prove some of the unknowns and hypothesis and today I would like to share our progress. 2. The PostgreSQL Buffer Manager In the previous post, I mentioned that we would like to build a new in-memory based storage that is based on the existing buffer manager and its related ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Tod Golding. By Tod Golding, Principal Partner Solutions Architect at AWSTenant isolation is fundamental to the design and development of software-as-a-service (SaaS) systems. It enables SaaS providers to reassure customers that—even in a multi-tenant environment—their resources cannot be accessed by other tenants. While the importance of isolation is well understood, the strategies for realizing a robust isolation model vary significantly based on the design of your SaaS application and the services it uses. As a Partner Solutions Architect for AWS SaaS Factory, I have worked with organizations that have a wide range of ... Read More

Feed: Recent Announcements. AWS Launch Wizard offers a guided way of sizing, configuring, and deploying AWS resources for SAP HANA and SAP HANA-based Netweaver systems with a purpose built, easy to use wizard.   On-boarding a new operating system version for an SAP application involves close collaboration between SAP and Operating System (OS) teams, who analyze SAP notes, configure the system, and test extensively before standardizing on the new OS. AWS Launch Wizard eliminates this heavy lifting. You select the OS you want to use from a dropdown menu, and it automatically creates a pre-configured environment aligned with AWS and ... Read More

Feed: Blog – ArangoDB. Author: Christopher Woodward. This post is the first in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. In this first post, we look at what exactly ArangoML is, with later posts in this series showcasing the different tools and use cases. If you have a use case you would like to see highlighted as a part of this series, please let us know on the ArangoML Slack channel. Overview ArangoML is a set of tools and technologies that enable analytics and machine learning on graph data.  ArangoML Tools/Stack The ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Jobin George. By Jobin George, Sr. Partner Solutions Architect at AWSBy Michael Coughlin, Director, Global Partner Solutions at ConfluentBy Joseph Morais, AWS Evangelist, Global Partner Solutions at ConfluentThere has been tremendous adoption of Apache Kafka throughout the years, and developers are increasingly using Kafka as the foundation for their event streaming applications. Today, developers of cloud applications need access to a fully managed Apache Kafka service that frees them from operational complexities so they don’t need expertise to use the technology. Built as a cloud-native service, Confluent Cloud offers developers a serverless experience with ... Read More

Feed: Microsoft Azure Blog. Author: Chaitanya Bangur. Genomics is foundational to the development of targeted therapeutics and precision medicine. Advances in DNA sequencing technologies has driven a revolution in genomics-based research and is helping facilitate better understanding of human biology and disease conditions. This expanded knowledge is leading to the proliferation of personalized medicine strategies to prevent, diagnose, and treat diseases. The trend will continue to accelerate in the coming decade as use of genomics information becomes central to clinical decision support and healthcare delivery. Sequencing genomes at the population level will be required to decipher the genomic fingerprint of ... Read More

Feed: AWS Big Data Blog. This is a guest post by Shimpei Kodama, data engineer at Cookpad Inc.Cookpad is a tech company that builds a community platform where people share recipe ideas and cooking tips. The company’s mission is to “make everyday cooking fun.” It’s one of the largest recipe-sharing platforms in Japan with over 50 million users per month, and is growing fast in other countries as well. Cookpad has been using Amazon Redshift as its data analytics platform since 2015 to make all data accessible for employees to analyze and gain insights to delight end-users and grow its ... Read More

Feed: AWS Big Data Blog. AWS Glue Studio is an easy-to-use graphical interface that speeds up the process of authoring, running, and monitoring extract, transform, and load (ETL) jobs in AWS Glue. The visual interface allows those who don’t know Apache Spark to design jobs without coding experience and accelerates the process for those who do.AWS Glue Studio was designed to help you create ETL jobs easily. After you design a job in the graphical interface, it generates Apache Spark code for you, abstracting users from the challenges of coding. When the job is ready, you can run it and ... Read More

Feed: Matillion. Author: Julie Polito; Data can be one of the most valuable assets for any organization. With the rise of big data, companies can take advantage of vast stores of enterprise data to gain insights and make better decisions. However, as we create and store more data, consumer privacy concerns are growing. Companies need to comply with increasing numbers of regulations, such as GDPR. Data breaches occur with greater frequency, and they can be extremely damaging to an organization’s reputation as well as expensive to clean up. The risks of poor data policies are severe, even fatal for organizations ... Read More

Feed: Actian. Author: Pradeep Bhanot. Abraham Lincoln is quoted as saying, “If I had 8 hours to chop down a tree, I would spend 6 sharpening my axe.”  Spending 75% of the allotted time on preparation may seem like a lot, but in fact, most industry observers report that preparing data for business analysis or machine learning is indeed 70 to 80% of the time spent by data scientists and analysts.The data preparation pipeline consists of the following steps Access the data. Ingest (or Fetch) the data. Cleanse the data. Format the data. Combine the data. And finally, Analyze the ... Read More