An approach to modernization can be defined as, “An open, cross-functional collaboration dedicated to building new design systems and patterns that support evolving computing capabilities, information formats, and user needs.”
Within the same spirit of modernization we can say that MongoDB works along with Google Cloud technologies to provide joint solutions and some reference architectures to help our customers leverage this partnership.
At re:Invent 2021, AWS announced several new Amazon Redshift features that bring easy analytics for everyone while continuing to increase performance and help you break through data silos to analyze all the data in your data warehouse.
To keep the example simple but illustrative, we calculate the potential future exposure (PFE) of an equity stock forward contract.
Model equity price by simple Monte Carlo (MC) calculation, which uses Geometric Brownian Motion (GBM):
Today, we’ll show you how to deploy multiple Standard Tier Cloud Memorystore instances which can be used together to scale beyond the limits of a single instance for an application with increased scale demands.
In this post, we provide a step-by-step guide to bring data from ServiceNow to an S3 data lake using AWS Glue Studio and analyze the data with Amazon Athena.
In this solution, ServiceNow data is being extracted through AWS Glue using a Marketplace connector.
In this post, we walk through setting up an application API using the Amazon Redshift Data API, AWS Lambda, and Amazon API Gateway. The API performs asynchronous processing of user requests, sends user notifications, saves processed data in Amazon Simple Storage Service (Amazon S3), and returns a presigned URL for the user or application to download the dataset over HTTPS
A key benefit of a data mesh architecture is allowing different lines of business (LOBs) and organizational units to operate independently and offer their data as a product.
To make building a data lake easier, this post presents a solution to manage and deploy your data lake as an AWS Service Catalog product
We use an AWS CloudFormation template to create S3 buckets to load the script for creating the data lake as an AWS Service Catalog product as well as scripts for data transformation.
In this post, we show you how to use AnalyticsIQ datasets and Amazon QuickSight to generate valuable insights that could improve your organization’s decision-making. we use the AnalyticsIQ Social Determinants of Health Sample Data dataset to gain insights into the relationship between ethnicity and health, as well as how the social determinants impact the health and wellness of individuals.
We needed a solution that could scale and process millions of transactions, by effectively using high memory and CPU configurations to perform complex queries quickly
We used a graph database, Amazon Elastic Kubernetes Service (EKS), and Amazon Neptune, to search for suspicious financial chains across large amounts of transactional data in minutes.
In this post, we share how ENGIE created a CI/CD pipeline for an Amazon MWAA project template using an AWS CodeCommit repository and plugged it into AWS CodePipeline to build, test, and package the code and custom plugins.
This post demonstrates how we automated our engine wash analytics process to handle the complexity of ingesting data from multiple data sources and how we selected the right programming paradigm to reduce the overall time of the analytics job.
The modern data platform ingests delta changes from all source data feeds once per night. The orchestration and transformations of the data is undertaken by dbt. dbt enables data analysts and engineers to write data transformation queries in a modular manner without having to maintain the run order manually.
You can also use the Amazon Redshift query monitoring rules (QMR) feature to set metrics-based performance boundaries for workload management (WLM) queues, and specify what action to take when a query goes beyond those boundaries.
