Tag: IT data analytics

Upsolver is an AWS Advanced Technology Partner that enables you to ingest data from a wide range of sources, transform it, and load the results into your target of choice, such as Kinesis Data Streams and Amazon Redshift.

This reference architecture shows how to train a recommendation model using Azure Databricks and deploy it as an API by using Azure Cosmos DB, Azure Machine Learning, and Azure Kubernetes Service (AKS).

In this post, we provide a working example of AWS service-generated events ingested to Amazon S3. To make sure we have some service events available in default event bus, we use Parameter Store, a capability of AWS Systems Manager to store new parameters manually. This action generates a new event, which is ingested by the following pipeline.

In this post, we share how GDAC created an analytics platform from scratch using AWS services and how GDAC collaborated with the AWS Data Lab to accelerate this project from design to build in record time

This lab walks you through the steps to set up the stack for replicating an Aurora database salesdb to an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster, using Amazon MSK Connect with a MySql Debezium source Kafka connector.

If your organisation has to manage, process and query a great deal of important but short-lived information that is created sporadically and must be reported speedily, then a service like Cosmos DB is ideal.

This data is indexed and populated into Amazon OpenSearch Service to search and visualize it in a Kibana dashboard.

Figure 1 illustrates a solution built with AWS, which extracts O&G well data information from PDF documents.

Admission control in Amazon OpenSearch Service enhances the overall resiliency of OpenSearch clusters by limiting new incoming requests early, at the REST layer, when a node is stressed

With the sheer volume of historical and live data feeds being ingested, and the need for a scalable compute platform for backtesting and spread calculations, our team needed a performant single source of truth to build the application dashboards.

As the data sets would be leveraged by machine learning and analyst teams, the Delta Lake format provided unique capabilities for managing high volume market/tick data — these features were key in developing the Gemini Lakehouse platform:

Example 1 – Unload customer data in JSON format into Amazon S3, partitioning output files into partition folders, following the Apache Hive convention, with customer birth month as the partition key.

Example 3 – Unload line item data (With SUPER column) in JSON format into Amazon S3, partitioning output files into partition folders, following the Apache Hive convention, with customer key as the partition key

Each user who accesses the Q search bar assumes a role that gives them QuickSight permissions to retrieve a Q-embedded URL.

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.