Tag: IT data analytics

The global wearables market grew 35.1% year over year during the third quarter of 2020, with total shipments reaching 125 million units according to new data from the International Data Corporation (IDC) Worldwide Quarterly Wearable Device Tracker .

Historically, inserting and retrieving data from a given database platform has been easier compared to a multi-platform architecture for the same operations. To simplify bringing data in from a multi-database platform, AWS Glue DataBrew supports bringing your data in from multiple data sources via the AWS Glue Data Catalog .

Data engineers need a way to enable non-data engineers like business analysts, data analysts, and data scientists to operate using self-service methods by abstracting the complexity of pipeline development. In this post, we discuss AWS Glue custom blueprints, which offer a framework for you to build and share reusable AWS Glue workflows.

At re:Invent 2020, we announced the general availability of Amazon EMR on Amazon EKS , a new deployment option for Amazon EMR that allows you to automate the provisioning and management of open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS).

If you have followed my blog you will know that I have a view that might not align to the broader view. Such is the life of an analyst. Some years ago I recognized that Oracle’s cloud strategy was never going to hinge on its ability to dominate infrastructure.

AWS Glue is fully managed service that makes it easier for you to extract, transform, and load (ETL) data for analytics. You can easily create ETL jobs to connect to backend data sources. There are several natively supported data sources , but what if you need to extract data from an unsupported data source?

This post is co-written by Mei Long at Upsolver. Software as a service (SaaS) based applications are in demand today, and customers have growing need for adopting many of them in their use cases. As adoption grows, extracting data within these various SaaS applications and running analytics across them gets complicated.

With ML workflows, it is often insufficient to train and deploy a given model just once. Even if the model has desired accuracy initially, this can change if the data used for making prediction requests becomes—perhaps over time—sufficiently different from the data used to originally train the model.

We announced the preview of AWS Lake Formation transactions, row-level security, and acceleration at AWS re:Invent 2020 . In Part 1 of this series , we explained how to set up a governed table and add objects to it. In this post, we expand on this example, and demonstrate how to ingest streaming data into governed tables using Lake Formation transactions.

Presto is a popular distributed SQL query engine for interactive data analytics. With its massively parallel processing (MPP) architecture, it’s capable of directly querying large datasets without the need of time-consuming and costly ETL processes.

We recently moved our data analytics to AWS and adopted the Amazon QuickSight business intelligence (BI) service to help our stakeholders worldwide access insights more easily. In this post, I discuss our journey to the cloud and what QuickSight has meant to our business.

Presto is a popular distributed SQL query engine for interactive data analytics. With its massively parallel processing (MPP) architecture, it’s capable of directly querying large datasets without the need of time-consuming and costly ETL processes.

Organizations across the globe are striving to provide a better service to internal and external stakeholders by enabling various divisions across the enterprise, like customer success, marketing, and finance, to make data-driven decisions.

This post continues our Advancing Reliability series highlighting initiatives underway to constantly improve the reliability of the Azure platform. In 2018 we shared steps we’re taking to improve virtual machine (VM) resiliency using live migration .