With ever-increasing amounts of data at their disposal, large organizations struggle to cope with not only the volume but also the quality of the data they manage. Indeed, alongside volume and velocity, veracity is an equally critical issue in data analysis, often seen as a precondition to analyzing data and guaranteeing its value
Today’s modern applications use multiple purpose-built database engines, including relational, key-value, document, and in-memory databases. This purpose-built approach improves the way applications use data by providing better performance and reducing cost.
2020 has reminded us of the need to be agile in the face of constant and sudden change. Every customer I’ve spoken to this year has had to do things differently because of the pandemic. Some are focusing on driving greater efficiency in their operations and others are experiencing a massive amount of growth.
Machine learning (ML) is becoming more mainstream, but even with the increasing adoption, it’s still in its infancy. For ML to have the broad impact that we think it can have, it has to get easier to do and easier to apply. We launched Amazon SageMaker in 2017 to remove the challenges from each stage of the ML process, making it radically easier and faster for everyday developers and data scientists to build, train, and deploy ML models.
Over the years, we have had a front-row seat to digital transformation occurring across all industries and regions around the world. And in 2020, we’ve
From finance to smart cities, distributed ledger technology is beginning to deliver on its vaunted potential in several key sectors.
Given the Bitcoin price craze in the face of the morose economy during the Covid-19 pandemic, one may assume that the distributed ledger technology (DLT)/blockchain bubble is ready to burst once again. However, new developments justify paying close attention to this sector
Data analytics helps marketers learn about their customers with target precision, from the movies they watch on Netflix to their favorite scoop of chocolate ice cream. Data is ubiquitous, essential and beneficial — except when it’s not.
We launched Amazon QuickSight as the first Business Intelligence (BI) service with Pay-per-Session pricing. Today, we are happy to announce the preview of Amazon QuickSight Q, a Natural Language Query (NLQ) feature powered by machine learning (ML).
Data is ubiquitous in businesses today, and the volume and speed of incoming data are constantly increasing. To derive insights from data, it’s essential to deliver it to a data lake or a data store and analyze it. Real-time or near-real-time data delivery can be cost prohibitive, therefore an efficient architecture is key for processing, and becomes more essential with growing data volume and velocity.
Before you can create visuals and dashboards that convey useful information, you need to transform and prepare the underlying data. The range and complexity of data transformation steps required depends on the visuals you would like in your dashboard.
There’s a light at the end of the tunnel for IT pros who have been keeping a close eye on the job market. Plus, here are the skills that employers want in 2021.
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.
AWS Glue provides a serverless environment to prepare (extract and transform) and load large amounts of datasets from a variety of sources for analytics and data processing with Apache Spark ETL jobs. This posts discusses a new AWS Glue Spark runtime optimization that helps developers of Apache Spark applications and ETL jobs, big data architects, data engineers, and business analysts scale their data processing and batch jobs running on AWS Glue automatically.
This post discusses common use cases for Lambda stream processing and describes how to optimize the integration between Kinesis Data Streams and Lambda at high throughput with low system overhead and processing latencie
